learning progresses, thinking about mechanisms ...... software to create a podcast, and a variety of free solutions are .... An encouraging illustration of rep-.
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Meaningful Learning
Cross-References
Richard Gunstone Faculty of Education, Monash University, Clayton, VIC, Australia
▶ Ausubelian Theory of Learning ▶ Conceptual Change in Learning
Measures Keywords Conceptual understanding The term “meaningful learning” became prominent in science education through the work of the educational psychologist David Ausubel and his use of this label in the 1960s to designate learning that is in total contrast to rote learning. At its core this usage can be characterized as suggesting that, in most contexts most of the time, “rote learning is bad; meaningful learning is good.” Such usage has become widespread, so that “meaningful learning” serves as a label for learning seen to be of worth, of real purpose, in a wide variety of contexts. These range from academic discussions of alternative conceptions and the need to pursue conceptual change to popular debates of educational fads (e.g., “does [some specific fad] actually lead to any meaningful learning?”). Meaningful learning has also been central in other theories of learning that have been variously influential in science education, including Wittrock’s Theory of Generative Learning.
▶ Assessment: PISA Science ▶ Public Science Literacy Measures ▶ Test
Mechanisms Peter Machamer1 and Thomas V. Cunningham2 1 Cathedral of Learning, University of Pittsburgh, Pittsburgh, PA, USA 2 University of Arkansas for Medical Sciences, Little Rock, AR, USA
Keywords Cognitive science; Explanations; Learning progressions; Models A common and successful strategy for explaining something scientifically is to describe the mechanism that produces it. Mechanisms are composed of entities and activities that are, in various senses, in the world. Entities are things
R. Gunstone (ed.), Encyclopedia of Science Education, DOI 10.1007/978-94-007-2150-0, # Springer Science+Business Media Dordrecht 2015, Corrected Printing 2016
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Mechanisms, Fig. 1 The central dogma of molecular biology (Redrawn, based on Machamer et al. 2000)
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that behave or engage in activities. Activities are ways of working that produce phenomena. Succinctly put, “mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions” (Machamer et al. 2000, p. 3). Mechanistic explanations are representations (often verbal descriptions) of mechanisms. Mechanisms may be described in varying detail. Mechanisms can never be completely described, since, given any context, there will always be more to be said about what is involved in producing a given phenomenon. Since no system is ever completely causally closed, there will always be additional entities or activities warranting inclusion. Nevertheless, we may distinguish mechanistic explanations from mechanism schemas and from mechanism sketches. A sketch is an incomplete description of a mechanism that contains many gaps or “black boxes” that cannot be filled in because of limitations in scientific knowledge; sketches are often apparent in ongoing scientific research. A schema is an incomplete description of a mechanism that contains many lacunae but where some gaps may be filled in with further details given current scientific knowledge. For example, Francis Crick’s central dogma of molecular biology provides a schema for a mechanistic explanation of the uni-directionality of information transfer in biological systems (Fig. 1). A mechanistic explanation is a sufficiently filled out schema, such that our scientific knowledge fills in the gaps in a way that suffices for the explanatory needs of a field at a time and the interests of the investigators. Let us consider some examples of mechanistic explanations, first, an internal combustion engine: a car moves because a high-energy fuel
is mixed with air and burned in a special compartment so that energy is released to move a piston, which in turn moves a rod connected to other components that turn the wheels that propel a car by exerting force on the ground. We explain how a car works by providing a mechanism schema of how a drive system converts gasoline into rotational and translational motion. Yet another type of mechanistic explanation involving cars could look at the mechanisms established by legal and enforcement institutions that attempt to ensure that drivers stop at red lights, obey stop signs, put on the brakes, etc. Or, one might describe the macroeconomic relationships between gross domestic product, trade deficits, and supplies of and demands for various goods and services to provide a mechanistic description of the distribution of American-made automobiles in certain regions of the United States. To explain by describing the entities and activities that give rise to target phenomena is to explain mechanistically. Consider, also, chemical transmission at neuronal synapses (see Craver 2009). Neurons are described as “firing together”; they propagate electrical signals from one to another as a group, in a complex, orchestrated, and poorly understood fashion that serves as the basis of the mind and nervous system. A neuron is a cell. Between each neuron lies a space called a synapse. When one neuron is stimulated, it propagates an electrical current along the length of its body. At the end point it reaches a synapse, which must be bridged to communicate with an adjacent neuron. Chemical transmission is the mechanism whereby the electrical signal from a neuron is converted into a chemical signal across a synapse (Fig. 2; cf. Machamer et al. 2000, p. 9). This neurotransmitter signal is then converted back into an electrical signal in an
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adjacent neuron. A textbook account of this is complex. It includes entities such as membranes, molecules, receptors, and transmitters, as well as activities such as depolarization, priming, fusion, and release. An explanation of how chemical transmission occurs begins with a description of which entities undergo these activities and how their doing so causes other entities to behave. Thus, the entire system is explained by showing how it proceeds from an initial condition to a later condition, resulting in communication between neurons. In the history of the life sciences, the topic of mechanistic explanation has often been
contentious, as some authors have argued there is something irreducibly special about biological systems that cannot be fully captured by them (e.g., Haldane 1913). Contemporary discussions of mechanism need not be committed to reductionism or to the claim that all mechanisms are specified at a lower level than the phenomenon being explained. For example, individual behavior at a biological or person level may be mechanistically explained by detailing an ecological, social, or cultural mechanism. So mechanisms for vision may be described, for example, in terms of chemical reactions, in terms of neuron connections, in terms of areas of the visual system, in
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terms of a person’s cognitive capacities, and in terms of ecological and social dimensions. Thus, many mechanisms are explanatorily pluralistic. A current trend in science education is to focus on scientific models as loci for instruction and learning (Gobert and Buckley 2000). Mechanistic explanations encompass such representations as diagrams, equations, and written description. Thus, mechanism sketches and schemata are well suited for this modelbased paradigm. As the cognitive science of learning progresses, thinking about mechanisms will be central for thinking about teaching science. Just as we may describe mechanisms when giving scientific explanations, both teaching and learning may also be described mechanistically.
Cross-References ▶ Epistemology ▶ Learning Progressions ▶ Models ▶ Science Studies
References Craver CF (2009) Explaining the brain. Oxford University Press, New York Gobert JD, Buckley BC (2000) Introduction to modelbased teaching and learning in science education. Int J Sci Educ 22:891–894 Haldane JS (1913) Mechanism, life, and personality. John Murray, London Machamer P, Darden L, Craver CF (2000) Thinking about mechanisms. Philos Sci 67:1–25
Mediation of Learning ▶ Socio-Cultural Perspectives and Characteristics ▶ Socio-Cultural Perspectives on Learning Science
Mediation of Learning
Memory and Science Learning Eric Wiebe North Carolina State University, Raleigh, NC, USA
Keywords Cognition; Computational; Information processing; Models; Neurophysiological
Introduction Human memory can be thought of as the capacity for retaining and recalling experience. This retention can vary from a matter of milliseconds to a lifetime, and similarly the nature of recall can vary greatly. I will qualify the discussion here to specifically consider events for which attention plays a role and where the individual has the opportunity to control aspects of their cognition for tasks such as learning. The second half of the twentieth century gave rise to modern theories of cognition and memory, many of which paralleled the emergence of computing technologies and the broad conceptual framework of information processing. The current accepted psychological models of memory trace their roots to early work by Broadbent and later by Treisman, Atkinson and Shiffrin, Neisser, Cowan, and Baddeley in the 1960s through 1980s.
Information Processing Models of Memory Information processing models represent mental activity as occurring in a series of stages, with starting points and feedback loops at numerous points. These models typically encompass not only memory but also those processes that bracket memory functions within a learning task context. Common across most of these models is agreement on what stages of activity take place
Memory and Science Learning
and the belief that different stages will require differing amounts of mental resources This common model starts with sensory processing, where raw signals in the environment gain access to the brain through the peripheral sensory organs (e.g., the eyes and ears) and are received and stored in a short-term sensory store. Information in these stores might reside for one half to 4 s before decaying. Only if information is selected by attentional resources is it then interpreted through a stage of perception. Perceptual processing itself generally requires few attentional resources (i.e., is considered automatic) and driven by both ongoing sensory input (termed “bottom-up processing”) and inputs from long-term memory (“top-down processing”). This speed and relative automaticity is what distinguishes perception from cognitive processes. Attentional resources may select some perceptual information for more effortful processing by cognitive functions involving working memory. Working memory, as defined by Baddeley, consists of three core components: the verbalphonetic subsystem, the spatial subsystem, and central executive. The verbal-phonetic subsystem itself is composed of two components: the phonological loop that represents linguistic information and the articulatory loop where words or sounds can be rehearsed. The spatial component can be thought of as a visuospatial sketchpad which represents visual information in an analog form. The verbal-phonetic and visuospatial subsystems of working memory seem to function more cooperatively than competitively when it comes to allocation of attentional resources. That is, research has demonstrated that the limited capacity of working memory can be optimized by a division of incoming information between these two encoding channels. On the other hand, the limited capacity overall of working memory means that high demands on either of these two channels will result in interference and competition between information chunks and the inability to appropriately encode, manipulate, or transfer it to long-term memory. Some research has also suggested that this model of working memory should be amended to include a kinesthetic subsystem.
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Long-term memory is organizationally different, with information either stored as declarative or procedural information. Models of knowledge representation are used to better understand how long-term memory procedural information (how to do things) may interact with declarative (factual) information or episodic memory (of specific events). Physiologically, procedural knowledge primarily occurs in brain systems involving the neostriatum (a subcortical part of the forebrain, the location of essentially all cognitive and perceptual activity) while declarative knowledge primarily involves the hippocampus (also in the forebrain). Various knowledge elicitation techniques can be used to build models of an individual’s knowledge structures in order to better understand what it means to be expert at a domain or how one’s knowledge structures change as they learn. The term mental model or schema can be used to describe an individual’s cognitive structure with regard to a concept or system. Current work on learning trajectories or progressions is used to more fully describe how these cognitive structures manifest themselves as behaviors in learning contexts. It is important to remember that while long-term memory is considered both unlimited and permanent (baring injury or disease), it does not guarantee that an individual will be able to access this information when needed for a learning task. The context in which you are attempting to recall information, the cues provided for you, and the temporal and capacity challenges put on working memory will all influence your ability to recall information in a form that is most useful. The overarching central executive serves a number of key functions, including (1) controlling the allocation of attentional resources to incoming stimuli, (2) coordinating multiple tasks and channels of information, and (3) retrieving and temporarily holding information from long-term memory. The dorsolateral prefrontal cortex is thought to play a key role in executiveattentional functions and, by extension, working memory functionality. Because of this close connection, individual differences are best explored through a combined system of working memory subsystems and executive-attentional control.
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It is not surprising that there are also likely connections between working memory capacity, executive-attentional control, and general fluid intelligence. Because many of these operations require mental resources, the attention system helps direct and allocate resources based on both automatic and conscious action. However, it is important to remember that this resource is not binary and can be divided between multiple resources. Exactly how much and under what conditions continues to be an active area of research. It follows that the mind is not simply a passive recorder of incoming streams of data, but use of the working and long-term memory systems is a dynamic process that often involves conscious cognitively effortful activity. Learning and experience both recruit and alter brain structure as memory is utilized. Understanding the role of memory in learning requires understanding the different affordances and constraints of working and long-term memory. In this information processing model, learning is considered to involve the ways in which long-term memory has been activated and shaped, via sensory and working memory systems. Individuals will chose to execute a response (or not) based on perception and shaped by working and long-term memory. Feedback they then receive via their sensory system will help direct further activity. Working memory, true to its name, is the crucible where the examining, comparing, evaluating, and transforming representations of information take place. Information received and created here will only be available later in time if it is placed in long-term memory. The usefulness of this information stored in long-term memory depends on whether the correct information can be recalled in the appropriate context and applied to the current task taking place in working memory. Central to this model of cognition is that working memory capacity is limited while long-term memory is not and that cognitive processes are much slower and reflective than automatic, perceptual ones. The capacity limits of working memory interact with the transitory nature of this information. Not surprisingly, the more information one attempts to hold in working memory, the faster
Memory and Science Learning
this information is likely to decay due to less resources devoted to its rehearsal and preservation. Conversely, more information can be more effectively held for longer periods of time in working memory if adjacent units are grouped, or chunked, together by associations in an individual’s long-term memory. How information is presented can either facilitate or hinder an individual’s ability to group and associate information in long-term memory. However, incorrect or inappropriate (for the learning task) information learned at a previous time and retrieved from long-term memory for this current learning task can interfere with the current working memory operations.
Other Models Early theorizing on the nature of human thought and the emergent computer revolution led to connectionist models of cognition, broadly inspired by the physiology of the brain (i.e., neural networks). Two important computational models of cognition to emerge in the 1990s that combined connectionist with symbolic (rule-based) architectures were Anderson’s ACT-R model and Newell’s Soar model. Computational cognitive models such as ACT-R have been the basis for a new generation of intelligent tutors being developed to assist learners though dynamic, adaptive computer-based learning environments. Newer models based on Bayesian mathematical models do not attempt to mimic physiological structure but rather behaviors observed in humans and other animals. Bayesian probabilistic methods look at how prior knowledge can form alternative structures and make inferences as to the best representation to use given the available data and context. All types of computational models can be used to understand human cognition, predict human performance in learning tasks, and build computer-based tools that assist human learning. There has also been a long-standing interest in understanding memory and attention by directly examining the neurophysiological functioning of animals and humans. Physiologically, memory is
Memory and Science Learning
neither a single entity nor a phenomenon that happens in a single area of the brain. Historically, the invasive techniques required for these types of studies have limited the depth of probing into human physiology. Hence, researchers have used animal models where more invasive techniques were considered acceptable, or humans already suffering from neurological injury or disease. Recent breakthroughs in imaging (fMRI and PET) and other technologies have meant that neurophysiological research and refinement of theoretical models have made considerable strides in recent years. It should be noted that theoretical models based on physiological studies of neuronal networks should be differentiated from connectionist models developed around and modeled in computer systems, as the latter are only broadly based on the functioning of the human brain. Both these forms of model are valuable since both human and machine cognition are utilized in modern educational systems.
Memory in Science Learning A learning environment that leverages what is known about memory and attention will recognize that learners come with memory that is structured differently and with varying degrees of expertise in different conceptual domains and in ways of processing information. Such an environment will attempt to maximize learner opportunities to effectively acquire new information that shapes these memory structures and to access and utilize prior memory structures. To that end, science learning environments guided by both human and machine teachers should assess prior knowledge (memory structures) and how students are able to utilize it. Expertise in science can be used to describe both what information is contained within longterm memory and how it is organized and utilized to chunk or organize information in working memory. Experts will have a performance advantage in their areas of expertise in terms of how efficiently and effectively they can both process information and utilize it. Novices are distinguished from experts in terms of their lack of
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appropriate organizational structure of information in long-term memory. Learning in science often involves inquiry cycles that involve the introduction of background conceptual material, investigation into scientific phenomena, and then reflection on the linkage of what was investigated to the broader scientific concept. Background conceptual material needs to link new material – often presented in textual, symbolic, and visual forms – with prior memory structures. Sweller’s cognitive load theory and Mayer’s associated multimedia learning theory leverage knowledge of working memory capabilities with learning contexts such as this to provide insight into how to design multimedia content for learning. Sound pedagogical strategies will encourage rehearsal and encoding to form strong linkages with existing long-term memory structures. Similarly, well-designed investigations will also provide information through multiple modalities and facilitate linkage to prior memory structures, providing scaffolded support to guide attention to the most salient information and support its rehearsal and encoding. Finally, reflective activities allow the development of long-term memory structures that can be generalized and used in related scientific practice with similar conceptual and procedural elements. Science education is impacted by many of the same trends in technology infusion as other educational areas. Because of this, scientific phenomena are often experienced by students virtually mediated by computer-based environments. This trend has a number of characteristics that are impacted by human attention and memory. Computer interface design needs to be mindful of the bandwidth and temporal rate of information delivery so as to not to overload working memory. However, well-designed systems can leverage the fact that information can be distributed between computer and human memory and scaffolded in ways that support learning and retrieval. Similarly, emerging technologies around intelligent tutoring systems can create parallel models of student cognition to be used to help provide guidance and support for learning. Emerging understanding of the critical role
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the affective state plays in attention and effective, conscious cognitive effort also has led to a better understanding as to how learning environments need to be designed to both monitor and attend to the affective and cognitive dimensions of science learners.
Cross-References ▶ Information Processing and the Learning of Science ▶ Learning Progressions ▶ Multimodal Representations and Science Learning ▶ Neuroscience and Learning ▶ Prior Knowledge
References Cowan N (1995) Attention and memory. Oxford University Press, New York Kane MJ, Engle RW (2002) The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon Bull Rev 9(4):637–671. doi:10.3758/BF03196323 Mayer RE (2001) Multimedia learning. Cambridge University Press, Cambridge, UK Rolls ET (2008) Memory, attention, and decision-making: a unifying computational neuroscience approach. Oxford University Press, Oxford Wickens CD, Hollands JG (2000) Engineering psychology and human performance, 3rd edn. Prentice-Hall, Upper Saddle River
Metacognition and Science Learning Gregory P. Thomas Secondary Education, University of Alberta, Edmonton, Alberta, Canada
Metacognition refers to an individual’s knowledge, control/regulation, and awareness/ monitoring of his/her thinking and learning processes. A more simplistic and less useful definition often used is that metacognition is thinking
Metacognition and Science Learning
about one’s own thinking. Research and scholarship in metacognition in science education typically draws on metacognition theory from educational psychology and engages and adapts that theory to address issues regarding the learning and teaching of science. Metacognition is executive, higher-order thinking that is superordinate to but that also interacts closely with the cognitive processes that students employ to construct knowledge and develop understanding via their science learning experiences. Successful science learners are consistently found to be adaptively metacognitive for the demands of their learning environments. While it might be appealing to view an individual’s metacognition as good or bad, this is a simplistic notion. Rather, what might be valuable metacognition in one context or culture may be considered less or more valuable or adaptive in another, depending on the task or learning and cognitive demands of that particular context or culture. It is important to consider contextual and cultural factors when theorizing and investigating metacognition. Developing and enhancing metacognition is congruent with existing and reform directions in science education such as conceptual change, scientific inquiry, and the use of information technology. Each of these reform directions has both commonly shared and reform-specific cognitive processes associated with them, and students should develop metacognition in relation to such cognition. For example, students should be metacognitive regarding the process of consciously considering new information against their existing scientific conceptions and theories and should be able to engage in conscious revision of their existing views in light of new information that might become available via the use of, for example, a microworld computer simulation. Further extending this example, students should be metacognitive regarding how the use of the computer technology might facilitate their learning and conceptual revision compared with, for example, the use of a textbook or a laboratory investigation. It is increasingly acknowledged that while domain-general metacognition across curricular subject areas is important, metacognition research and scholarship in the field of
Metacognition and Science Learning
science education should and increasingly does account for the domain-specific science information to be learned by students and also the cognitive processes and metacognition to be employed by them to learn, understand, and employ that information and those processes within and beyond their science classrooms. Metacognitive knowledge can be classified as declarative, e.g., definitions and/or conceptions of thinking and learning; procedural, e.g., knowing how to engage in learning and/or cognitive processes; and conditional, knowing when and why to engage particular learning and/or cognitive processes to achieve learning objectives. Science learners are all metacognitive to varying extents, but because metacognition is internal, it can be difficult to qualify and/or quantify an individual’s metacognition. Much of students’ metacognitive knowledge is tacit, and they often do not have a language to detail or explain their metacognitive knowledge or thought processes. This can lead to difficulty in evaluating the nature of students’ metacognition and/or the impact of interventions aimed at developing and enhancing students’ metacognition. It can also lead to difficulty in teaching students to be more and differently metacognitive and to be attentive and responsive to the varying demands of learning tasks and/or learning environments. Such a language of thinking needs to be taught to students and teachers. Despite the importance of metacognition for science learning, science classroom learning environments are most often not sufficiently metacognitively oriented. The metacognitive orientation of a science classroom environment refers to the extent to which specific psychosocial factors known to assist in the development and enhancement of metacognition are evident in that environment. Improving the metacognitive orientation of a classroom environment requires that teachers make metacognitive demands on students to consider their thinking and learning processes, not just the science they are asked to learn. It also requires that students are encouraged and supported to talk with the teacher and each other about their science thinking and learning processes, that they are able to voice their views
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regarding the nature of the learning activities they are asked to engage in, and that they are increasingly given control over the selection and enactment of their preferred learning activities under the supervision of the teacher. Developing and enhancing student metacognition in science learning environments requires that teachers are themselves aware and knowledgeable of the thinking and learning processes required to learn and understand science in those environments. It also requires that teachers are able to incorporate the explicit teaching of metacognitive knowledge and related cognitive and learning strategies and the modeling of those strategies into their teaching settings and pedagogies. Interventions aimed at enhancing metacognition seek to elicit metacognitive experiences in students. Metacognitive experiences are those conscious experiences that are educed in students when they reflect on and consider their thinking and learning processes, most often with reference to their learning and/or cognitive performance. They constitute key stimuli for students’ revision of their metacognitive knowledge and dispositions. Interventions typically fall into one of two broad categories. The most common category of interventions involves engaging students in the use of metacognitive activities such as concept maps, Venn diagrams, and Predict-ObserveExplain (POE) or using metacognitive prompts to orient their cognition when they engage with science learning. The expectation or assumption is that students will often without prompting reflect on the use of those strategies and develop metacognition in relation to their use. The other, less common category of interventions involves explicitly inducing metacognitive conflict in students. Metacognitive conflict is analogous to cognitive conflict; however, it refers to the conflict experienced by students when they are asked to consider conceptions of learning, what it means to know and understand science, and how to know and understand science that run counter to their existing conceptions or beliefs regarding such matters. Metacognitive conflict approaches require that teachers are able to articulate increasingly sophisticated views of science and science
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learning to students that reflect the nature of science and science subject area disciplines. Ideally, a combination of interventions drawn from both categories would be evident in science classrooms so that students would be challenged to consider multiple means of constructing their understanding of science, what it learns to understand science, and how they can better learn and understand science. A primary goal of developing students’ metacognition is to assist them to become independent, effective science learners who are able to tailor their thinking and learning processes to the demands of the science material to be learned and who can do so beyond their high school years. Debate is ongoing regarding how students’ metacognition can and should be investigated and evaluated. Two categories of methods in metacognition research are identified: online and off-line. Online methods are those employed when an individual is engaged in real time in a learning or cognitive activity. Such methods include think-aloud protocols and eye tracking. Off-line methods are those employed before or after task performance. They are typically selfreport measures and include surveys, questionnaires, and interviews. Both online and off-line methods have affordances and constraints, and a researcher’s selection and use of methods is guided by their epistemological assumptions, and the degree of inference they consider is appropriate in metacognition research. Online measures while targeting real-time cognition and metacognition might interfere with individuals’ normal engagement in and performance of a learning task. Conversely, off-line measures while not interfering with students’ real-time task engagement, cognition, and metacognitive activity are influenced by what students are aware of and/or can recall regarding their thinking and metacognition and the extent to which they can accurately report their thoughts. It may be that a combination of online and off-line methods could be employed in research studies to gain a comprehensive understanding of students’ metacognition from a variety of perspectives. Further exploration of this possibility is necessary.
Metaphors for Learning
Priorities for future research on metacognition in science education include conceptualizing and implementing interventions to enhance students’ metacognition and science learning in authentic, content-rich settings, investigations into teacher metacognition, and exploration of methods for seeking data leading to findings that can inform enhancements in science teacher pedagogy.
Cross-References ▶ Classroom Learning Environments ▶ Concept Maps: An Ausubelian Perspective ▶ Conceptual Change in Learning ▶ Culture and Science Learning ▶ Didactical Contract and the Teaching and Learning of Science ▶ Microworlds
References Georghiades P (2004) From the general to the situated: three decades of metacognition. Int J Sci Educ 26:365–383 Thomas GP (2012) Metacognition in science education: past, present and future considerations. In: Fraser BJ, Tobin KG, McRobbie CJ (eds) Second international handbook of science education. Springer, Dordrecht, pp 131–144 White RT (1998) Decisions and problems on metacognition. In: Fraser BJ, Tobin KG (eds) International handbook of science education. Kluwer, Dordrecht, pp 1207–1213 Zohar A, Dori JD (eds) (2012) Metacognition in science education: trends in current research. Springer, Dordrecht
Metaphors for Learning Anna Sfard University of Haifa, Haifa, Israel
Keywords Acquisition metaphor; Participation metaphor
Metaphors for Learning
Metaphors for learning are metaphors that we use, either explicitly or in an only implicit manner, to describe learning. What often appears as but an innocent figure of speech may in fact inform how we think about the topic, what we are able to notice, and what pedagogical decisions we are likely to make. Different approaches to learning may have similar metaphorical underpinnings, and therefore it is useful to categorize them according to their underlying metaphors. In this entry, a brief explanation on the role of metaphors in our conceptual thinking is followed by a succinct survey of the metaphors for learning identified by those who studied the topic. Two of these metaphors, known as acquisition metaphor and participation metaphor and considered as arguably the primary source of all known approaches to learning, are then presented in some detail.
Metaphors as a Source of Conceptual Systems Metaphor can be defined as a familiar word or phrase used in an unfamiliar context. We are witnessing such metaphorical use while speaking about our ideas or thoughts as “crystallizing” and about love as “burning.” In both these cases, a word – burn, crystallize – has been taken from its source domain, that of physical phenomena, and used within a target domain for which it was not originally intended, that of human thoughts or emotions. Traditionally considered to be not much more than literary gimmick, the metaphor has been recognized in the last decades as the basic mechanism through which people cope with new situations and develop original ways of thinking. Nowadays, there is a consensus that any advanced human idea, be it everyday or scientific, grows from previous concepts through the mechanism of metaphor. The American linguist George Lakoff (1993) takes these claims to the extreme when he speaks about conceptual metaphor, the cognitive mechanism that transforms what one already knows – familiar conceptualizations, everyday bodily experiences, and forms
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of activity – into new ways of thinking. Whereas ubiquitous and usually quite helpful, this mechanism becomes truly indispensable when we face new phenomena. Indeed, it is only through the use of familiar words in a new context that we can make sense of unfamiliar situations. Similar claims about metaphors are being made these days by researchers who refuse to view human thinking as fundamentally different from any publicly accessible forms of activity and prefer to consider this inner process as a special case of communicating. Within this latter approach, metaphors are said to be generators of novel discourses. This formulation brings in full relief the systemic nature of metaphors, that is, the fact that metaphor-generated target discourses tend to reproduce considerable portions of source discourses. Whether cognitivist or discursive, the assumption that metaphors are responsible for our conceptual systems leads to the conclusion that our bodily experience is the primary source of even the most abstract of our ideas. This fact has been explicitly acknowledged by philosophers of science, who point out, for example, that his familiarity with the solar system strongly influenced Rutherford’s interpretations of his experimental data as indicating the structure he hypothesized for the atom. At the same time, claims are being made about risks resulting from the systemic nature of metaphors. Unaware of their being guided by unintended metaphorical entailments, the users may be led to unhelpful conclusions. To minimize the risks, we need to be explicit about metaphors underlying our thinking. In scientific research, and in studies on learning in particular, where it is crucially important to control our language for undesirable uses, identifying hidden metaphors is not just advisable – it is imperative.
Metaphors for Learning The ways we tell stories about learning, whether in our daily life or in research, is full of terms and phrases that disclose the metaphorical origins of our talk. This is well illustrated by common learning-related expressions such as construction
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of knowledge, mental scheme, internalization, scaffolding, apprenticeship, negotiating meaning, and appropriation. Because these terms have no obvious “literal” counterparts, their metaphorical nature may not be immediately visible. On closer inspection, however, one realizes that the words construction and scaffolding originate in discourses on [house] building, the word apprenticeship is taken from discourse on vocational training, and the first associations that come to our mind upon hearing words such as negotiating or appropriating are human activities in domains such as politics or propriety management. Two categories of metaphors are ubiquitous in research on learning: those that help us to think about different properties or types of learning and those that offer answers to the question of what learning is. In the above list of learning-related metaphorical terms, the first category is represented by the term scaffolding, which refers to the support granted to the learner by a more knowledgeable person; apprenticeship that speaks about learning that involves the learner’s and teacher’s co-participation in the activity to be learned; and internalization, Piaget’s notion referring to a stage in learning where what has been done by physical manipulations can now be done “in the head” (Piaget, 1952). The second category, that of learning-defining metaphors, is represented here by the expression construction of knowledge, which many writers consider as an equivalent of the term learning. The constructivist metaphor is clearly an alternative to the one that portrays learning as a mere transmission of knowledge. Since defining metaphors constitute the foundation on which one’s thinking about learning is based, each such trope combines with some other properties–describing metaphors better than with some others. Thus, for instance, constructivist metaphor is strongly associated with mental schemes, internalization, and transfer, whereas none of these expressions fits the participationist vocabulary. Our metaphors for learning can make considerable difference in the ways we teach. The constructivist metaphor, first introduced to research on human development and learning many
Metaphors for Learning
decades ago by the French psychologist Jean Piaget, has revolutionized Western pedagogy. Its long-term impact can be felt all around the world even today. It has been influential earlier and more widely in science than in any other area of the school curriculum. For some time before the advent of Piaget’s innovative ideas, instruction had been shaped by the learning-astransmission-of-knowledge metaphor, which is deeply engraved in many languages – consider, for instance, such English expressions as “getting education” or “imparting knowledge.” This metaphor pictures the teacher as the “broadcaster” and the learner as the “receiver.” This vision supported the lecture-based frontal teaching that had been the dominant form of school instruction all around the world until a few decades ago and can still be found in many places. Once educators began thinking about learning as the activity of building one’s own knowledge, lecturing started giving way to more active and interactive pedagogies, guided by the principle of encouraging learners to voice and develop their own ideas. Another metaphor, this time more explicit, has been coined to describe the kind of change that occurred with the transition from the transmission to construction metaphor: the teacher, rather than being “the sage on the stage,” was now playing the role of “guide on the side.”
Acquisition and Participation Metaphors In spite of the considerable diversity of figures of speech that pervade our talk and shape our thinking about learning, it is possible to divide all the resulting approaches into two broad categories. Thus, for instance, although the transmission and construction visions of learning are quite different in their assumptions and implications, they can still be seen as two instances of yet another, more fundamental metaphor: the metaphor of learning as an act of taking possession over some entities – concepts, knowledge, skills, or mental schemas. This acquisition metaphor comes to the scholarly discourse directly from everyday expressions, such as acquiring or
Metaphors for Learning
imparting knowledge, having concepts, or getting (seeing) meaning. The alternative metaphor is the participation metaphor. This trope, which originates in the sociocultural ideas of Vygotsky and his followers (Vygotsky, 1978), pictures the Learner as the peripheral participant (Lave & Wenger, 1991) in the special forms of an activity that humans developed throughout history. School subjects, such as mathematics or science, are good examples of such activities. These two metaphors for learning differ considerably in their most fundamental entailments and in particular in those that deal with the question of what it is that changes when people learn. Whereas the acquisition metaphor portrays learning as the process of extending and transforming mental entities, the participation metaphor equates learning with changes in patterned, recurring ways of acting. The former approach, therefore, assumes a basic ontological difference between what happens inside and outside the human head, whereas the latter approach makes this distinction between internal and external processes irrelevant. In other words, the acquisition metaphor is fundamentally dualistic, whereas with the participation metaphor, the duality disappears. This basic ontological disparity has been shown to entail many other differences, either in ways in which the resulting theories view and explain phenomena or in how they inform the practice of teaching and learning and associated values (Sfard 1998). Since the participation metaphor does not easily combine with our everyday thinking and talking about learning, the first researchers who opted for this metaphor had to be strongly motivated to be able to abandon the time-honored acquisition metaphor. Indeed, they did have a valuable insight to gain. On the basis of the acquisition metaphor, it was difficult to account either for the cross-cultural and cross-situational diversity of individual learning or for the existence of societal learning. This latter type of learning, which is widely believed to be unique to humans, expresses itself in the increasing complexity of our ways of thinking and acting across successive generations. Acquisitionist researchers, for whom knowledge to be acquired
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comes directly from the world and is relatively independent of social interactions, did not have means to account for those changes that transcend a single life span. By reconceptualizing learning as a process of becoming capable of acting in uniquely human ways, the participationists provide a solution to the conundrum: An individual participant may offer her own version of an established activity, and if her innovation is deemed an improvement over the former way of doing things, it is likely to spread to the entire community. In this way, human activities are constantly refined, and the innovations are passed from one generation to the next. Once adopted by the community, the reformed ways of acting will be the ones to be learned by every new member. This participationist model of learning also explains why different communities are likely to act differently in the face of similar tasks. The difference between the acquisitionist and the participationist versions of human development, therefore, manifests itself in how we understand the origins and the nature of human uniqueness. For the acquisitionist, this uniqueness lies in the biological makeup of the individual. The adherents of a participationist vision of learning, on the other hand, believe that it is the collective life that brings about all the other uniquely human characteristics, with the capacity for individualizing the collective – for individual reenactments of collective activities – being among the most important of these characteristics. Once it is accepted that scientific theories are but metaphors turned into rigorously told stories, those who evaluate theories become more interested in the question of whether a theory is helpful than in the query of whether it is true. Debates between adherents of different metaphors, therefore, are not about facts – about what learning really is – but about which of the metaphorically grounded visions of learning answers more questions in a more convincing way. The response to this query may depend on what is being asked in research. Different metaphors for learning may thus coexist, serving different areas of research for different purposes.
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Cross-References ▶ Communities of Practice ▶ Constructivism ▶ Didactical Contract and the Teaching and Learning of Science ▶ Language and Learning Science ▶ Pluralism ▶ Prior Knowledge ▶ Values and Learning Science
References Lakoff G (1993) The contemporary theory of metaphor. In: Ortony A (ed) Metaphor and thought. Cambridge University Press, New York, pp 202–250 Lave J, Wenger E (1991) Situated learning: legitimate peripheral participation. Cambridge University Press, New York Piaget J (1952) The origins of intelligence of the child. Routledge and Kegan Paul, London Sfard A (1998) On two metaphors for learning and the dangers of choosing just one. Educ Res 27(2):4–13 Vygotsky LS (1978) Mind in society: the development of higher psychological processes. Harvard University Press, Cambridge
Microworlds Janice Gobert Social Sciences and Policy Studies Department, Learning Sciences Graduate Program, Worcester Polytechnic Institute, Worcester, MA, USA
Keywords Simulation; Virtual lab The term microworld was first introduced by Seymour Papert as part of the pedagogical philosophy of constructionism. To Papert, a microworld is a “. . .subset[s] of reality or a constructed reality so . . . as to allow a human learner to exercise particular powerful ideas or
Microworlds
intellectual skills” (Papert 1980, p. 204). In this definition, microworlds are very open-ended in pedagogical style. Typically, microworlds are models of scientific or social phenomena that represent the domain-specific properties and conceptual representations of the phenomena of interest, thereby providing perceptual affordances and conceptual levers to the learner. The degree of structure used to guide students’ activities in learning environments, particularly those involving microworlds, is a topic that has been debated in science education, where some have encouraged open-ended exploration by students (as does Papert), and others have offered guidance or structure within the microworld to promote optimized learning. In our environment, Inq-ITS, we use carefully constrained technology “widgets,” embedded within a broader technology learning environment to support a degree of open-endedness while also including structured guidance so that students can hone their inquiry skills (Gobert et al. 2013). These widgets, together with the artifacts students generate, serve to represent and make salient the products and processes of inquiry for the learner to support both effective monitoring and meta-level understanding of inquiry. Using microworlds, students formulate hypotheses and test them, interpret their data, warrant their claims, and communicate their findings. In our work, we instrumented our environment and microworlds to log all students’ interactions, which support real-time analyses (i.e., of the log files) based on knowledgeengineering and educational data mining techniques. This results in assessment metrics for researchers and teachers on the specific inquiry skills of interest. It also helps us scaffold students’ inquiry processes in “real time” (i.e., during the microworld session).
Cross-References ▶ Games for Learning ▶ Simulation Environments
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References Gobert J, Sao Pedro M, Raziuddin J, Baker RS (2013) From log files to assessment metrics for science inquiry using educational data mining. J Learn Sci 22(4), 521–563 Papert S (1980) Computer-based microworlds as incubators for powerful ideas. In: Taylor R (ed) The computer in the school: tutor, tool, tutee. Teacher’s College Press, New York, pp 203–201
Milieu Ge´rard Sensevy School of Education, University of Western Brittany, Rennes, France
Keywords Adidactical; Didactic contract; Didactic milieu; Epistemic system; Symbolic forms; Teaching In this entry, we discuss the notion of milieu as interpreted by the French educational didactician Guy Brousseau (1997). We may generally define this concept in the following terms: the milieu is the actual material and symbolic structure of the problem at stake, which one has to deal with in order to solve this problem (Sensevy 2012). Here and hereafter, the term “problem” refers in a very general way to any situation in which one has to restore an equilibrium, in the Dewean sense (Dewey 1938). Consider this example: at primary school, students are asked to reproduce a puzzle by enlarging it, in such a way that a segment which measures 4 cm on the model will measure 7 cm on the reproduction. The pieces of this puzzle constitute the milieu that the students face for this “enlargement problem.” This kind of milieu responds to three conditions: 1. The teacher’s intentions are inscrutable. Here, the students ignore the specific teacher’s teaching intentions. They concentrate on a pragmatic purpose (enlarging the puzzle),
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and at first they do not recognize the kind of knowledge (proportional reasoning) necessary to enable them to solve the problem. In this way, the students have to achieve certain autonomy. 2. The relationships within the milieu are pregnant and adequate. In enacting their activity, the students get feedbacks from the milieu, which helps them to make decisions about the strategies they use. For example, as they have to enlarge the puzzle in such a way that a segment, which measures 4 cm on the model, will measure 7 cm on the reproduction, they may decide to add 3 cm to every dimension. As a result of this strategy, the pieces are not compatible; the students may realize concretely that the additive strategy is not a good one. The milieu feedbacks are pregnant in that they focus the students’ attention on the relevance of the used strategy. They are adequate, in that an effective proportional strategy in making pieces will obtain their compatibility. 3. The knowledge at stake provides a winning strategy in problem solving. A specific knowledge (in the puzzle case, proportional reasoning) enables students to solve the problem. In this kind of milieu, the students can examine the situation, take a decision, enact it, and judge on their own the relevance of their strategy, according to the milieu feedbacks. With respect to these three conditions (specially the first one), Brousseau (1997) draws attention to two fundamental features of such a milieu that he terms adidactical. First, it “lacks of any didactical intentions with regard to the students” (Brousseau 1997, p. 40). In our example, the signs provided by the pieces of the puzzle are non-intentional ones and opposed to the students’ goal (enlarging the puzzle) until they use the proportional strategy. Second, designing an adidactical milieu means taking into account not only the specific knowledge this milieu has to embed but also the current knowledge system with which students will approach this milieu. This is the reason why Brousseau defined the milieu as “the system opposing the taught system or, rather, the previously taught system”
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(Brousseau 1997, p. 57). In our example, students invariably act by adding 3 cm to every dimension. They face the adidactical milieu with an additive knowledge system, which provides them with their actual ineffective strategy. Within this perspective, one may acknowledge the very nature of the teacher’s work. In order to describe the teaching-learning relationship, one has to be able to call for another concept, that of didactical contract (Brousseau 1997; Sensevy 2012). The didactical contract organizes the actual system with which the students deal with the milieu. In our puzzle example, one could say that students cope with the milieu within an additive contract, which has stemmed from the previous teacher and students’ joint action in mathematics. The didactical contract can be viewed both as a system of expectations between the students and the teacher and as the current students’ strategic system. One might think that the notion of milieu is specific to a certain kind of teaching-learning process. It is true in the sense that a milieu cannot be designed without taking into account the specific knowledge it embeds. But the notion of milieu has a general relevance, in that it refers to the actual material and symbolic structure of the problematic situation, insofar that this structure may provide feedbacks for the student’s epistemic action. In this respect, let us consider an activity in the topic of mechanics (often taught in grade 11). This activity (Tiberghien et al. 2009; Sensevy et al. 2008) occurs while introducing the three laws of Newtonian mechanics. It aims at familiarizing students with the direction of the action and helps them to differentiate between action and motion. Then, the designers elaborate a milieu where directions of action and motion are different and observable even with common sense. In this activity, the students have to throw and catch a medicine ball (heavy ball) and then answer a series of questions. The first one is “locate and note the moment(s) where you exert an action on the medicine ball; each time specify in which direction you exert this action on the medicine ball.” For the students, it is not easy to differentiate direction of action and motion when they catch the medicine ball at its lower point, and some of them say they exert a force
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Milieu, Fig. 1 Two kinds of proposals for approaching the “medicine ball” problem
downward. After a while, the feedbacks of the milieu (the way their hands have “to resist” to the ball) may help them to begin to conceptualize the situation accurately. This milieu (medicine ball) has a lot in common with the previous one (puzzle). It provides some feedbacks that are more or less immediately perceived by the students (the puzzle pieces do not fit together; the hands exert a force upward). One could term these kinds of feedbacks causal feedbacks. But there are other feedback possibilities in a milieu. Let us consider now another situation, in which the milieu is a rather complex one. After having worked on the medicine ball problem, the students have to study the whole movement of the medicine ball within a specific activity (“Aristotle or Galileo?”). The students have to analyze different students’ answers to the task of “representing the forces which are exerted on the medicine ball (when it is going upward) represented by a point and noted M-B.” They are asked to study two proposals (summarized in Fig. 1), composed by two annotated vectors and a text: One representation is correct from the point of view of the current model of mechanics (initiated by Galileo). The other representation corresponds to an intuitive analysis of the situation: according to this point of view (close to Aristotle’s) there is always a force in direction of the movement.
In this problem, the students have (1) to identify which type of answer refers to an Aristotelian viewpoint, (2) to identify the systems 1 and 2 which act on the system M-B and to draw a conjecture about what the additional force represents for the students (A) and why they need to represent this force, and (3) to rely on the interaction model in order to justify the fact that this
Milieu
force does not model an action exerted by the medicine ball when it goes upward. How is the milieu shaped in this situation? First, the students are confronted with a text from which they have to understand that the problem to be solved consists of analyzing two different student’s responses. Second, they have to pay attention to the fact that the student’s responses are vector representations. Third, while reading the text, students have to focus on a specific sentence (“according to this point of view (close to Aristotle’s), there is always a force in the direction of the movement”) in order to be able to work out the problem at stake. By referring to the previous sentence, they have to recognize this Aristotelian view as “incorrect.” Fourth, they have to refer their analysis to the moment when the medicine ball is going up. Fifth, they have to scrutinize the two representations in order to identify which group analyzes the situation “intuitively,” by drawing a force in the direction of the movement. Thus they have to consider the representation A and identify the vector F3/MB as expressing the Aristotelian viewpoint of a force in the direction of movement. They have to formulate hypotheses about the reason why the students need to represent this force. Finally, they have to justify the “fact that this force does not model an action exerted by the medicine ball when it goes upward,” by applying the interaction model. According to the current didactic contract, students are supposed to recognize that there are only the earth and the air which exert an action on the ball and that both of these actions are downward. If we compare this kind of milieu to the previous ones studied (e.g., the “feel the medicine ball” milieu), we can acknowledge deep commonalities and striking differences. In both ways the students have to decipher and take into account a set of symbolic forms (the medicine ball and the hands pressure, the different meanings in the text of the problem), which refers to the nature of the problem at stake. Then they have to inquire into this set of symbolic forms in order to institute logic relationships between them and to transform them in an epistemic system of symbolic forms. But there is also a conceptual
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difference between the two kinds of milieu. In the first milieu (the “feel the medicine ball” milieu), students have to experience causal feedbacks by interrogating their own body, which functions as a milieu. In the second milieu (the “Aristotle-Galileo” milieu), they have to experience rational feedbacks, by inferring new meanings from the semantic and semiotic units they put in relation. But above these commonalities and these differences, there is a deep kinship between the two milieus. Even though the teacher’s intentions can be used by the students for working out the problem, it is not possible for them to rely on this recognition to solve the problem. In order to solve it, they have to orient themselves in the milieu, then to inquire into the milieu and, in doing so, to encounter the fundamental meanings of the physics involved in this milieu. It is worth noting that the teacher’s work is crucial to help the students achieve their inquiry. The art and the science of teaching could be seen as a way of monitoring the relationship between the student’s work and the milieu.
Cross-References ▶ Agency and Knowledge ▶ Didactical Situation ▶ Epistemic Goals ▶ Transposition Didactique
References Brousseau G (1997) Theory of didactical situations in mathematics. Kluwer, Dordrecht Dewey J (1938) Logic. The theory of the enquiry. Henry Holt, New York Sensevy G (2012) About the joint action theory in didactics. Zeitschrift f€ ur Erziehungswissenschaft 15(3):503–516, http://link.springer.com/article/ 10.1007%2Fs11618-012-0305-9# Sensevy G, Tiberghien A, Santini J, Laube´ S, Griggs P (2008) An epistemological approach to modeling: cases studies and implications for science teaching. Sci Educ 92(3):424–446 Tiberghien A, Vince J, Gaidioz P (2009) Design-based research: case of a teaching sequence on mechanics. Int J Sci Educ 31(17):2275–2314
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Mindfulness and Science Education Malgorzata Powietrzynska1 and Kenneth Tobin2 1 Urban Education, The Graduate Center, City University of New York, NY, USA 2 The Graduate Center, City University of New York, NY, USA
Keywords Heuristics; Interventions; Meditation; Mindfulness; Wellness . . . it is my fervent aspiration that our culture will pay more attention to well-being, will include strategies to promote well-being with our educational curricula and within the healthcare arena, and will include well-being within our definitions of health. These changes would help to promote greater harmony and well-being of the planet. (Davidson 2013)
In the above quote, Richard Davidson, a leading scholar and one of the pioneers of contemplative neuroscience, echoes the wishes of a growing number of like-minded enthusiasts who call for the expansion of the goals of the twenty-first century education to include promotion of wellness and sustainability. After decades of seemingly nonstop curriculum reform, much of which has focused on the production of scientists and increasing participation in science-related professions, it is desirable to revamp the goals of science education to address wellness and sustainability as well as goals that relate to the grand challenges faced by humanity. It is opportune to orient curricula to understanding the body and the mind and developing tools to afford lives as functionally literate citizens. Adapted from Buddhist traditions, mindfulness practices offer a unique opportunity to address in the classroom the cognitive and the oft-neglected affective dimensions of human ontology. As evidenced by the exponential growth of mindfulness-related publications (Fig. 1), there is widespread interest in the applicability and potential benefits of mindfulness in various fields of social life including educational contexts. This interest is fueled
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largely by the recent developments in the field of contemplative neuroscience that provide compelling evidence for our brain’s enormous plasticity (ability to change its structure and function) and for the significance of the bidirectionality of the mind-body connection. Mindfulness training emerges as affording a number of positive changes in the brain which in turn improve the functioning of our bodies. Mindfulness involves intentionally remembering to pay attention to the present moment experience – a disposition that defies our default preoccupation with analyzing and obsessing about the past or charting out and worrying about the future (so-called mind wandering). This present moment awareness is accompanied by a nonjudgmental acceptance of one’s thoughts, feelings, and perceptions and seeing things for what they are without identifying with them. Such an accepting mindset can translate into increased levels of caring for the natural world. Mindfulness training has been linked to a range of cognitive, social, and psychological benefits to students and teachers. It supports development of self-regulatory skills associated with emotion and attention, self-representations, and prosocial dispositions such as empathy and compassion (MLERN 2012). Furthermore, mindfulness tends to decrease stress, depression, anxiety, and hostility. An increase in mindfulness may involve a higher incidence of focus, heightened awareness of thoughts and emotions and their relevance to learning, and awareness of what is happening in the moment. Research for at least a half century has shown a relationship between emotions and cognitive focus. For example, studies indicate that positive emotions are associated with broadening of cognitive processes, and negative emotions are associated with narrowing of cognitive processes. There are numerous ways to interpret the research and associated theoretical frameworks in relation to the teaching and learning of science. However, it seems clear that the valence and intensity of emotion are salient. We conclude that it is important to be aware of the mediational potential of emotions. In terms of mindfulness, the goal is to be aware of emotions and endeavor to “let them
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Mindfulness and Science Education, Fig. 1 Growth in the number of mindfulness-related publications over the last two decades. Data obtained from a search for “mindfulness” in Google Scholar
go,” making sure that they do not mediate participants’ conduct in ways that prove to be distracting and deleterious to learning. In the event that a participant decides that emotions are persisting and are adversely affecting learning, it is important to know how to intervene to sever attachments and/or reduce the intensity of the emotions. Research on the intensity of emotions and focus has implications for teaching science. For example, consider a classroom incident reported by Tobin and Llena (2010). During a lesson on conversion of units, the teacher and several students were frustrated with most students’ performance on a recent quiz. The regular science teacher had been absent due to illness and a substitute had been teaching the class. Students were having difficulty following their substitute science teacher’s efforts to re-teach the work for which their test performance had been poor. An altercation broke out when a student leant across to clarify for another student what the teacher had said. The teacher reprimanded her for speaking while he was speaking. Almost immediately the
learning environment became dysfunctional in many respects. The teacher’s anger was intense, represented through his gestures, prosody, and spoken text. Consistent with the intensity of emotion decreasing focus, the teacher was less able to attend to teaching the students about conversions from one unit to another. His oral presentation was slow, contained long pauses, and included utterances about “rude student.” Some students regarded the altercation as a performance and laughed at what was happening, regarding the text as unintelligible, an object for humor and ridicule. For the student who had been reprimanded, words such as “rude student” were inflammatory and catalyzed further outbursts, ratcheting up the intensity and distribution of high emotions. In a very short time interval, less than a minute, the teacher endeavored to continue teaching, while at the same time he continued to refer to the student as rude and taunted her by mimicking her prosody and chanting “temp, temp, temper” as she reacted with high intensity, “you have every nerve to call me a rude
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student. . ..” At the same time laughter punctuated a turbulent classroom environment in which an increasing number of students’ actions were accusing and disrespectful and the teacher’s words were taunting and escalating. Laughter was polysemic, some instances possibly intended to encourage an escalation of the altercation, other instances reflecting amusement at what was happening, and nervous laughter that projected anticipation and acknowledgment that unfolding events were dangerous and cascading out of control. A key point of emphasis is that the learning environment was not mindful and the intensity of emotions focused actions away from the teaching and learning of science. Although there was widespread awareness of what was happening, there were no corrective interventions to reestablish a focus on learning science. In this context we regard it as a critical priority for science educators to use interventions, such as heuristics and breathing meditation, to create and maintain mindful learning environments. Lack of fluency in a classroom is often associated with high levels of emotional intensity and low levels of mindfulness. Of concern is the impact of sustained intense emotions on the well-being of teachers and students, a concern that is even greater in those contexts such as in the USA where there are well-documented trends associated with teacher turnover and student absenteeism. It is therefore imperative to understand and minimize undesirable negative emotions in the classroom and produce more positive emotional climates and more desirable states of wellness. The potential of mindfulness to address some of the intractable problems of education constitutes a strong rationale for the use of mindfulness-based interventions.
Cultivating Mindfulness in the Classroom: Mindfulness-Based Interventions Over the last decade, a number of approaches to cultivating mindfulness in educational settings have been developed. Many of the programs are loose adaptations of the Mindfulness-Based
Mindfulness and Science Education
Stress Reduction Program (MBSR) originally created for clinical purposes by Jon Kabat-Zinn. An alternative approach to incorporating mindfulness-based interventions into teaching and learning practices involves the use of heuristics (refer to Fig. 2 for an example of a heuristic). Consistent with the hermeneutic tradition, using a heuristic is a way of surrounding the construct with meaning through a series of statements (referred to as characteristics), each describing some aspect of mindfulness. As students or teachers read each characteristic, they select a point on the available rating scale. This process is meant to afford getting insights into one’s conduct vis-à-vis various dimensions of mindfulness. The theory that supports this intervention is reflexive inquiry (Bourdieu and Wacquant 1992) where reflexivity may be understood as becoming aware of the unaware. Because so much of what happens in social life happens without conscious awareness, reflexivity is important for actors, such as science teachers and their students, so that they can identify aspects of their practice and their supporting rationale, changing them as desirable to benefit the collective (Tobin 2012). Accordingly the use of heuristics aims at deepening awareness of self and others and of the surrounding structures. As students and teachers ponder the characteristics of mindfulness, they become aware of them and may pay attention to conduct that reflects those characteristics within themselves and each other. As the participants’ awareness of mindfulness increases, they often indicate the desire to change their conduct to more closely reflect an idea expressed in a heuristic. Consequently, changes in their conduct may become visible. Such positive changes have potential to contribute to a more harmonious learning environment. For example, students and their teachers may develop more compassionate attitudes toward self and others and learn to replace reacting with responding which may be associated with decreased incidence of aggression and bullying. A useful feature of a heuristic is its malleability. A heuristic may be adapted to fit any educational context: a science class at different grade levels, teacher training or professional development,
Mindfulness and Science Education Mindfulness and Science Education, Fig. 2 Mindfulness in Education heuristic
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Mindfulness in Education Heuristic Your name
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For each characteristic circle the numeral that best reflects your current state of mindfulness in this class. As necessary, provide contextual information that applies to your rating. 5= Very often or always; 4= Often; 3= Sometimes; 2 = Rarely; 1= Hardly ever or never
During this class: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
I am curious about my emotions. I find words to describe my emotions. I allow thoughts to come and go without being distracted by them. I notice my emotions without reacting to them. I am kind to myself when things go worng for me. I recover quickly when things go worng for me. Even when I am focused I use my senses to remain aware. When I am emotional, I notice my breathing. When I am emotional, I notice my heart beat. I maintain a positive outlook. The way in which I express my emotions depends on what is happening. The way in which I express my emotions depends on who is present. I can focus my attention on learning. When I produce strong emotions, I can let them go. When my emotions change I notice changes in my body temperature. The way I position and move my body changes my emotions. I use breathing to manage my emotions. I am kind to others when they are unsuccessful. I can tell when something is bothering another person. I am aware of others’ emotions from the tone of their voices. I recognize others’ emotions by looking at their faces. When I am with others my emotions tend to become like their emotions.
a cogen session (see the entry Teacher Research), etc. It may change its format (i.e., become a narrative); characteristics may be added, deleted, or altered to adequately reflect salient features of a particular educational setting. Similarly a heuristic may or may not include a rating scale associated with each characteristic. If a rating scale is included, the scale points can be written to fit the context of use. A heuristic may be completed in “one sitting” or alternatively individual characteristics may be used each day to draw attention to different dimensions of mindfulness allowing students and teachers to make personal choices about each in meaningful ways. This could be done in a variety of ways including the use of technology, cell phones, blogs, chalkboards, etc. Heuristics may be used for planning and guiding classroom activities with the purpose of facilitating adoption of practices that are characterized
by mindful attention, focus, and compassion to self and others. Breathing meditation is often an integral part of mindfulness training. Focusing on the breath allows participants to bring their attention to the present moment. It also assists with disassociating oneself from any (especially negative) thoughts and emotions. Breathing meditation may be used at the beginning of a class session to help students and teachers calm their minds and sharpen attention and focus. Using breathing meditation in a science classroom may afford exploration of the relationship between emotional feelings and respiration (the mind | body connection) and its significance for emotion regulation. Accordingly, students and teachers may observe changes in their emotional states as they practice deep abdominal breathing. Conversely, their attention may be brought to the
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changes in their breathing patterns (and possibly other physiological markers such as fluctuating heart rate) as their emotional states shift. Increased awareness of these connections may be accomplished through the use of relevant characteristics in a heuristic or through other techniques such as the use of oximetry (see the entry Teacher Research). Breathing meditation may be presented as a medical intervention that is associated with improvements in one’s well-being. Breathing meditation may be modeled and facilitated by the teacher or by students. It may be a relatively short activity lasting between 3 and 5 min at a time. Participants may be encouraged to maintain the practice outside the classroom if they find it beneficial.
Mindfulness in Science Education Science process skills became a visible part of the science curriculum in the post-Sputnik reform movement associated with the 1960s and 1970s. Basically, curriculum developers considered the steps of the scientific method and/or problem-solving and broke them down into skills. Different projects had different lists of process skills, in part because materials were developed around different psychological theories of learning. Prominent among these were the learning theories of Jean Piaget and Robert Gagne´. Consider the 5 Es (engagement, exploration, explanation, elaboration, and evaluation), a present-day articulation of the Learning Cycle, which was based on Piaget’s developmental theory and emerged in the post-Sputnik era as a framework for the Science Curriculum Improvement Study in the USA. From a sociocultural point of view, each of the 5 Es is an interaction chain (Collins 2004), that is, involving multiple interactions between individuals and social artifacts that are enacted using available structures (i.e., resources). The quality of enactment reflects criteria such as fluency (i.e., enactment occurs just in time, is anticipatory, and is appropriate) and the extent to which others’ actions are in synchrony and maintain flow. It is essential for successful interaction
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chains to occur in order for students to enact any of the 5 Es effectively and appropriately. The likelihood of this happening can be heightened if students establish and maintain focus and fluency while being aware of (attentive to) unfolding events that are salient to their learning. If others are involved in an interaction, for example, it is important that an actor is aware of emotional styles related to the extent to which he/she or others are expressing emotional cues. Language is an important tool for enacting process skills when actions are internal (i.e., thought), external (i.e., spoken or written), and a combination of both internal and external. For example, the quality of enactment of a process skill concerns the words and utterances used, their prosody (e.g., loudness, frequency modulation, intonation, etc.) and proxemics (e.g., gestures, body movement and orientation, eye gaze, head tilt, etc.). Quality counts. In addition, what counts as quality will reflect the theoretical frameworks that underpin central criteria such as teaching and learning. For example, in terms of the sociocultural framework that we [the authors] adopt in our research, dialogue is central. No matter what happens to be the focus of the curriculum, we want the enactment and associated interaction chains to be characterized by dialogic inquiry. That is, as teachers and students interact, we want them to be respectful to one another, share time of talk and the number of talking turns, listen attentively to what others have to say, make sense of it, and understand its affordances in comparison to alternatives. Also, if injustices arise we expect participants to speak out in favor of corrective action. When individuals speak, they do so for the benefit of others, not just for themselves. When science process skills were initially emphasized in science education, there were cliche´s to the effect that “science is a verb,” “science is something that is done,” and “authentic science” – meaning that what children might do, as science, would likely differ significantly from what postdoctoral researchers would do as science. It was argued that science had a role in terms of enhancing functional literacy in an increasingly technological society in which
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citizens have the knowledge to feel at home with the technologies they use in their lifeworlds and are not intimidated by them. The process skills learned and employed in school science, it was argued, would be available for use in different fields of the lifeworld, including shopping, working, hobbies, and consuming media. Now, more than six decades later, there is a compelling argument that students should know about healthy lifestyles and bodies, including neuroplasticity. To know, in the sense we use it here, extends far beyond just knowing the facts to include enacting healthy lifestyles and engaging in activities that transform the structure and functioning of the brain. We offer mindfulness as the process skill of the present decade.
Sound Bodies and Sound Minds The emerging science behind the benefits of mindfulness to wellness provides support for incorporating contemplative practices into the educational arena. Mindfulness-based interventions are associated with positive outcomes for students and teachers through addressing cognitive and affective dimensions of teaching and learning. Whereas mindfulness can be a constituent of a traditional approach to science education, we regard it as a central component of a radical transformation of science education to embrace overarching goals related to wellness and sustainability. Mindfulness is a way of enacting social life that can expand learning potential and ameliorate the nature and intensity of emotions that rise and fall in the normal course of life. As part of the toolkit that individuals possess, mindfulness heightens awareness of emotions and their attachment to the ongoing conduct of social life. Given the alarming increase of violence in institutions that previously have been regarded as sanctuaries, such as schools, we regard as a priority for all humans to learn about mindfulness and to enact it in the course of everyday life, including schooling, and science education. Because science has traditionally been involved with learning about life and bodies and relationships between humans and the living and nonliving environments in which they conduct
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their lives, we regard mindfulness as central to a transformed science education that includes the science of learning and being in the world.
Cross-References ▶ Affect in Learning Science ▶ Concept Mapping ▶ Emotion and the Teaching and Learning of Science ▶ Teacher Research
References Bourdieu P, Wacquant LJ (1992) An invitation to reflexive sociology. The University of Chicago Press, Chicago Collins R (2004) Interaction ritual chains. Princeton University Press, Princeton Davidson RJ (2013) What does science teach us about well-being? The Huffington post. http://www. huffingtonpost.com/richard-j-davidson/science-wellbeing_b_3239792.html?utm_hp_ref¼religion. Accessed 26 July 2013 Mind and Life Education Research Network (MLERN) (2012) Contemplative practices and mental training: prospects for American education. Child Develop Perspect 6(2):146–153 Tobin K (2012) Sociocultural perspectives on science education. In: Fraser BJ, McRobbie CJ, Tobin KG (eds) Second international handbook of science education. Springer, Dordrecht, pp 3–17 Tobin K, Llena R (2010) Producing and maintaining culturally adaptive teaching and learning of science in urban schools. In: Murphy C, Scantlebury K (eds) Coteaching in international contexts: research and practice. Springer, Dordrecht, pp 79–104
Mindtools (Productivity and Learning) David H. Jonassen Educational Psychology and Learning Technologies, University of Missouri, Columbia, MO, USA
Keywords Cognitive partners; Concept mapping
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and test external models, our internal models make progress. This entry introduces the notion of “mindtools,” which are readily available tools for constructing syntactic and structural representations of what is being learned. Mindtools repurpose commonly used computer applications to engage learners in critical thinking and conceptual change. There are several classes of mindtools, including semantic organization tools (e.g., databases, concept mappers), dynamic modeling tools (e.g., spreadsheets, expert systems, or systems modeling tools), visualization tools (e.g., drawing or visual modeling environments), knowledge construction tools (e.g., multimedia construction and story construction tools), and conversation and collaboration tools. All of these tools may be used to construct models of what is being learned.
Early in the evolution of classroom-based computing technologies, Taylor (1980) described three roles that computers could play in the classroom: tutor, tool, and tutee. In the tutor role, the computer teaches the student, a role fulfilled nowadays by Web-based tutorials, information sites, and drill-and-practice. Computers continue to be very powerful productivity tools, including word processing and organizational tools such as databases and spreadsheets. The most constructivist application of computer technologies is in playing the role of tutee, where the students actually teach the computer. One way in which computer technologies can serve as a tutee is by enabling students to construct models of what they are learning. Science educators have long recognized the importance of modeling in understanding scientific phenomena. Humans are natural model builders, constructing conceptual models of everything that we encounter in the world. The better that we understand any part of the world, the better we can model that part of the world – whether it is from science, economics, or how an automobile operates. This entry briefly describes the use of computers as mindtools (Jonassen 2000, 2006) to create models of the ideas that we are learning. Science and mathematics educators have long recognized the importance of modeling in understanding scientific and mathematical phenomena. Psychologists have explored mental or conceptual models and how these can be represented through external models or visualizations. The models in the minds of learners can be understood as being embodied in the equations, diagrams, computer programs, or other symbolic constructs used by learners to represent their understanding. Although still a topic of research, it is clear that there is a dynamic and reciprocal relationship between our internal mental models and the external models that we construct. The primary purpose of modeling is the construction and revision of conceptual understanding – that is, conceptual change. Building explicit models of our internal conceptual models engages and supports the process of conceptual change. When we build
What Can Be Modeled If model building serves to externalize mental models, then learners should benefit through the use of a variety of tools to model a variety of phenomena. Different models engage different kinds of thinking, and mindtools may be used to model different kinds of phenomena (Jonassen 2000). Modeling Domain Knowledge Domain knowledge is often presented and understood by learners in a very linear fashion, consisting of a series of facts and unrelated pieces. By modeling the domain and its structure, elements can be related to each other in complex associative maps (e.g., concept maps) or causally related systems (e.g., spreadsheets, expert systems, or system models). Concept maps are spatial representations of concepts and their interrelationships that reflect the knowledge held by the learner. Figure 1 illustrates a concept map describing molar conversion in chemistry. These representational structures can also be seen as cognitive structures, reflecting conceptual knowledge, structural knowledge, and semantic networks. As students study some domain in
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Mindtools (Productivity and Learning), Fig. 1 Concept map on molar conversion
a science course, they can continuously add to their concept maps, refine them, and even use them to reveal gaps or misunderstandings. Comparing one’s concept map with others can result in conceptual change, as models constructed by other students can represent alternative structure of the same ideas. Modeling Systems Scientific subject matter content can be thought of in terms of coherent systems. Rather than focusing on discrete facts or characteristics of phenomena, when learners study content as systems, they develop a more integrated view of the
world. Systems thinking involves understanding the world as process systems, feedback systems, control systems, and living systems, all of which can be seen as self-reproducing organizations of dynamic, interdependent parts. Systems are goal driven, feedback controlled, self-maintaining, self-regulating, and synergetic. Requiring learners to organize what they are learning into relevant interacting systems provides an opportunity to develop a more integrated view of the domain. Systems and subsystems are defined by structural and causal relationships. Systems modeling tools, such as Stella, enable learners to build
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models that focus on systems and their internal interactions. Figure 2 illustrates a model depicting the growth and maintenance of a local deer population that was produced by high school students. In this model, we see that the deer population is a function of factors that are part of the larger ecosystems, including reproduction rates that are dependent on predation and hunting. System models show the interactions of components within a system. Modeling Problems In order to solve virtually any kind of problem, the problem solver must mentally construct a problem space by selecting and mapping specific relations of the problem (Jonassen 2006). Using modeling tools to create visual or computational models externalizes learners’ mental problem space. As the complexity of the problem increases, producing efficient models becomes
even more important. Figure 3 illustrates the advice, factors, and rules to guide the development of an expert system knowledge base relating to how molar conversion problems are solved in chemistry. Reflecting on the problem solution process can help learners to better solve this kind of problem. Modeling Experiences (Stories) Stories are the oldest and most natural form of sensemaking. Cultural knowledge is often conveyed through different types of stories, including myths, fairy tales, documentaries, and histories. Humans appear to have an innate ability and predisposition to organize and represent their experiences in the form of stories and can more easily understand stories than expository texts. An alternative modeling experience to studying content is to collect and study stories that capture relevant disciplinary experiences.
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Mindtools (Productivity and Learning), Fig. 3 Excerpt from expert system rule base on molar conversion
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For example, we have constructed databases of stories about how engineers solve different kinds of problems, which have been collected into case libraries (i.e., databases of stories). When studying how to solve problems in any domain, students’ understanding may be enriched by such stories that help them to identify the lessons learned from each problem-solving situation. This process is known as case-based reasoning – the reuse of prior experiences. Databases are a logical means for capturing and storing such stories or problem-solving cases, and the process of collecting and indexing them serves to enhance the overall intellectual resource. Modeling Thinking (Cognitive Simulations) Many educators have argued for an emphasis of metacognition and self-reflection by learners, having students reflect on their learning processes in order to better learn how to learn. Metacognitive reflection may be enhanced by
building a model of how to engage in different kinds of learning. Rather than modeling content or systems, learners can model the kind of thinking that they need to perform in order to solve a problem, make a decision, or complete some other task. Figure 4 illustrates a systems model of decision-making that is based on the case-based reasoning theory just presented. These students developed a model of decision-making, a common form of problem solving and how it is supported by stories of prior experiences.
Why Should We Use a Mindtools Approach? Why do mindtools work? That is, why do they engage learners in critical, higher-order thinking and facilitate conceptual change? The process of articulating what we know in order to construct a model forces us to reflect on what we are
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studying in new and meaningful ways. The common homily, “the quickest way to learn about something is to have to teach it,” explains the effectiveness of mindtools, because learners are teaching the computer when constructing models. Mindtools are not intended to make learning easier. Learners do not use mindtools naturally and effortlessly. Rather, the use of a mindtools approach typically requires learners to think harder about the subject matter domain than they would by simply studying its content.
Knowledge Construction, Not Reproduction Mindtools represent a constructivist use of technology, as they are concerned with the process of how we construct knowledge from our experiences. When students develop databases, for instance, they are constructing their own conceptualization of the organization of a content domain. How we construct knowledge depends upon what we already know, which depends on the kinds of experiences that we have had, how we have organized those experiences into knowledge structures, and what we believe about what we know. This does not mean that we can comprehend only our own interpretation. Rather, we are able to comprehend a variety of interpretations and to use them in further constructing our understandings. Constructivist approaches to learning strive to create environments where learners actively participate in ways that help them construct their own knowledge rather than having the teacher try to ensure that students understand the world as they have been told. In a mindtools environment, learners are actively engaged in interpreting the external world and reflecting on their interpretations. This is not “active” in the sense that learners actively listen and then mirror the one correct view of reality but rather “active” in the sense that learners must participate and interact with the surrounding environment in order to create their own view of the subject. Mindtools function as formalisms for guiding learners in the organization and representation of what they know.
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Learning with Technology The primary distinction between computers as tutors and computers as mindtools was best captured by Salomon, Perkins, and Globerson (1991) as the effects of technology versus the effects with computer technology. Learning with computers refers to the learner entering an intellectual partnership with the computer. Learning with mindtools depends “on the mindful engagement of learners in the tasks afforded by these tools and that there is the possibility of qualitatively upgrading the performance of the joint system of learner plus technology” (p. 2). In other words, when students work with computer technologies, instead of being controlled by them, the students enhance the capabilities of the computer, and the computer enhances the students’ thinking and learning. The result of such an intellectual partnership with the computer is that the whole of learning becomes greater than the sum of its parts.
Cross-References ▶ Argumentation Environments ▶ Concept Mapping ▶ Concept Maps: An Ausubelian Perspective ▶ Modeling Environments ▶ Scientific Visualizations ▶ Simulation Environments ▶ Technology for Science Education: History ▶ Technology for Science Education: Research
References Jonassen DH (2000) Computers as mindtools for schools: engaging critical thinking. Prentice-Hall, Columbus Jonassen DH (2006) Modeling with technology: mindtools for conceptual change. Merrill/PrenticeHall, Columbus Salomon G, Perkins DN, Globerson T (1991) Partners in cognition: extending human intelligence with intelligent technologies. Educ Res 20(3):2–9 Taylor R (1980) The computer in the school: tutor tool, tutee. Teacher’s College Press, New York
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Minority ▶ Indigenous and Minority Teacher Education
Misassignment of Teacher ▶ Teaching Science Out-of-Field
Mobile Data Collection ▶ Field-Based Data Collection
Mobile Devices ▶ Handheld Devices
Mobile Learning ▶ Handheld Devices
Mobile Technology ▶ Handheld Devices
Model of Educational Reconstruction Reinders Duit IPN-Leibniz Institut for Science and Mathematics Education, Kiel, Germany
A key feature of the Educational Reconstruction approach is that in planning instruction – by teachers or curriculum developers – the science content to be learned and students’ cognitive and
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affective variables, including their learning processes, should be given equal attention. In addition, the science content is not viewed as “given” but has to undergo certain reconstruction processes. The science content structure (e.g., for the concept of evolution) has to be transformed into a content structure for instruction. The two structures are fundamentally different. The first step in this reconstruction, called “elementarization” (in German, Elementarisierung), is to identify the elementary ideas that relate to the aims of instruction, taking into account student perspectives (e.g., their pre-instructional conceptions). Then the content structure for instruction has to be developed. Finally, teaching and learning settings have to be designed. The tendency of many approaches aiming at more efficient science instruction to put the major emphasis on just instructional methods should be seen as problematic. The Model of Educational Reconstruction (MER) (Fig. 1) draws on these basic ideas (Duit et al. 2012). It is based on European Didaktik and Bildung (formation) traditions – with a particular emphasis on the German tradition (Westbury et al. 2000). It has been developed as a theoretical framework for studies looking at whether it is possible and worthwhile to teach particular science content areas. Clarification of science subject matter – including science concepts, views about the nature of science, and also the relevance of science in daily life and society – should be given substantial attention when developing instruction of a particular science content. In the past, however, often the main (or even the only) issues informing the clarification process are those that come from the structure of the science content involved. Educational issues are considered only after the science subject matter structure has been clarified. The MER closely links analyses of the science content structure and the educational significance of parts of it, with empirical studies of students’ understanding and with preliminary trials of pilot instructional modules in the classroom. It is a key assumption of the model that the curriculum developers’ awareness of the students’ points of view may substantially influence the reconstruction of the particular science content. The MER
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Model of Educational Reconstruction, Fig. 1 The three components of the Model of Educational Reconstruction (MER)
has been designed primarily as a frame for science education research and development. However, it also provides useful guidance for planning instruction in school practice. The core of the model is the analytical process of transposing (or transforming) human knowledge (i.e., the cultural heritage), such as domainspecific scientific knowledge, into knowledge for schooling that contributes to students’ scientific literacy. The science content structure cannot be directly transferred into a content structure for instruction. It has to be “elementarized” to make it accessible for students, but also enriched by putting it into contexts that make sense to the learners. Figure 1 illustrates the fundamental interaction between the three components of the MER. They influence each other mutually. Consequently in practice the result is a complex recursive step-by-step process. The MER shares major characteristics with other models of instructional design. In French science and mathematics education, the conception of Transposition Didactique includes similar ideas about the process of transposing human knowledge. The MER is explicitly based on constructivist views about efficient teaching and learning environments. The cyclical (recursive) process of educational reconstruction, i.e., the process of theoretical reflection, conceptual analysis,
small-scale curriculum development, and classroom research, is also a key concern of Developmental Research, of Design-based research (Cobb et al. 2003), and of other efforts to implement more evidence-informed approaches to teaching and learning in science. There is also a significant overlap with the idea of Teaching and Learning Sequences as discussed by Meheut and Psillos (2004). For all such approaches, research and development are intimately linked, and the conditions of teaching and learning in instructional practices (in schools and elsewhere) are explicitly taken into account. The MER also shares major features with the Learning Progression approach that has developed in the past decade, primarily in the USA (Duschl et al. 2011). In a nutshell, the MER shares major features with other frameworks of science education research and instructional design. The particular contribution of these frameworks is the idea that science content structure has to be reconstructed on the basis of educational issues, i.e., the aims of instruction and students’ perspectives.
Cross-References ▶ Bildung ▶ Developmental Research
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▶ Didaktik ▶ Evidence-Informed Practice in Science Education ▶ Learning Progressions ▶ Teaching and Learning Sequences ▶ Transposition Didactique
References Cobb P, Confrey J, diSessa A, Lehrer R, Schauble L (2003) Design experiments in educational research. Educ Res 32(1):9–13 Duit R, Gropengießer H, Kattmann U, Komorek M, Parchmann I (2012) The Model of Educational Reconstruction – a framework for improving teaching and learning science. In: Jorde D, Dillon J (eds) The world of science education: science education research and practice in Europe. Sense Publishers, Rotterdam, pp 13–47 Duschl R, Maeng S, Sezen A (2011) Learning progressions and teaching sequences: a review and analysis. Stud Sci Educ 47:123–182 Meheut M, Psillos D (2004) Teaching – learning sequences: aims and tools for science education research. Int J Sci Educ 26(5):515–536 Westbury I, Hopmann S, Riquarts K (2000) Teaching as reflective practice: the German Didaktik tradition. Routledge, New York
Modeling Environments Astrid Wichmann Education, Ruhr University Bochum, Bochum, NRW, Germany
Main Text Modeling environments are computational tools that support learners in building dynamic or static models that represent phenomena such as plant growth, the solar system, or crowd behavior. In the context of a well-designed curricular activity, the learner creates the model by specifying objects, their characteristics (i.e., variables), and their relationships. Relationships can be specified in different ways depending on the learning environment. Modeling tools such as concept maps or causal loop diagrams allow the learner to specify relationships qualitatively. In concept maps, relationships can be specified by
Modeling Environments
describing the nature of the relationship (e.g., “is a”). In causal loop diagrams, relationships are specified by polarities (i.e., “+” and “”) and include feedback loops describing how one variable causes a change of another variable, which then causes a change of the original variable. Some modeling environments allow learners to not only specify relationships but also to run the models, revealing potentially unexpected behaviors and allowing learners to test their predictions. In executable modeling environments, the learner can observe how values of variables change over time.
Modeling Languages Executable modeling environments are different from simulation environments because of the ability to create a model of a dynamic system and to study it in a quantitative way. Executable modeling environments are based on a formalized language such as Petri nets, NetLogo, or System Dynamics, allowing the learner to quantitatively specify variables and relationships. System Dynamics, a modeling language developed by Forrester (1961), has been frequently used in learning settings. It is developed to represent dynamic systems (e.g., population growth) and to predict and understand relationships between variables (e.g., death rate and birth rate) over time. System Dynamics, like other languages, makes use of generic variable types to simulate direct and indirect effects of variables within complex systems.
Inquiry Learning Modeling has been the focus of considerable research and is often cited as a central aspect of inquiry learning, where students actively construct their knowledge by specifying relations between variables of dynamic phenomena. For example, as part of an iterative inquiry cycle, a learner might start out with an orientation activity in which information about relevant objects or variables is sought and a research question is formulated.
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During an initial hypothesis activity, students would be supported in creating a model that embeds their hypothesis. In an ensuing experimentation activity, variables of a model could be modified for the purpose of testing the hypothesis. In the results activity, the produced data is analyzed and interpreted by the learner, allowing inferences. Based on those inferences or conclusions, a new hypothesis or research question could be formulated, beginning a new inquiry cycle.
meet their purpose, they must be updated or replaced with better ones. This restructuring process helps learners to refine their understanding of the complex nature of scientific phenomena and the scientific inquiry process. In terms of learning outcomes, modeling environments have been shown to foster learning of complex topics. For more simple topics, other learning environments such as simulations or more traditional forms of instruction are more effective.
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Modeling environments are designed to work either as stand-alone tools or as activities embedded within a broader learning environment, where learners create models as part of an inquiry project. Stand-alone tools like Model-It (Krajcik and Blumenfeld 2006) or STELLA (Steed 1992) allow the learner to build executable models. They are typically integrated by the researcher or teacher, into a substantive curriculum unit, requiring careful design of activities. An embedded approach, such as employed by Co-lab (van Joolingen et al. 2005), includes a modeling activity within an overarching technology-supported learning environment. Co-lab uses the metaphor of buildings that includes different houses and rooms to guide the learner through activities of an inquiry process. The Web-based Inquiry Science Environment (WISE) (Slotta and Linn 2009) offers modeling tools in combination with notes, hints, domain information, and data visualization tools, which are provided in the context of project-based inquiry.
▶ Concept Mapping ▶ Inquiry, Learning Through ▶ Simulation Environments
Applications for Learning
Modeling Teaching
In its application as a learning activity, modeling has been studied in various domains, including water flow, thermodynamics, and ecosystems, and real-world phenomena such as traffic flow. Research has shown that modeling environments allow the learner to appreciate the value of models and the role that models play in science. Learners become aware that models play a role in scientific reasoning. When models no longer
Jolie Mayer-Smith Faculty of Education, Department of Curriculum and Pedagogy, University of British Columbia, Vancouver, BC, Canada
References Forrester J (1961) Industrial dynamics. Productivity Press, Portland Krajcik JS, Blumenfeld PC (2006) Project-based learning. In: Sawyer RK (ed) Cambridge handbook of the learning sciences. Cambridge University Press, New York, pp 317–333 Slotta JD, Linn MC (2009) WISE science: inquiry and the internet in the science classroom. Teachers College Press, New York Steed M (1992) Stella, a simulation construction kit: cognitive process and educational implications. J Comput Math Sci Teach 11:39–52 van Joolingen WR, de Jong T, Lazonder AW, Savelsbergh E, Manlove S (2005) Co-lab: research and development of an online learning environment for collaborative scientific discovery learning. Comput Hum Behav 21:671–688
Keywords Modeling teaching
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Introduction Modeling is the act of sharing through explicit demonstration a particular skill, practice, activity, or way of thinking. Modeling teaching involves showcasing teaching practice, as well as the reasoning that informs, and the language that explains that practice. Effective modeling has been shown to enhance learning-to-teach science in elementary, secondary, and postsecondary settings. Modeling teaching is a highly flexible strategy that can illustrate how to plan lessons; how to design and implement diverse instructional strategies such as inquiry teaching, lecturing, hands-on laboratory sessions, and learning beyond the classroom (e.g., in field trips and other informal contexts); and how to assess student understanding before, during, and after instruction. Modeling can also be used to illustrate the application of theory in the practice of teaching and to encourage and assist preservice teachers in acquiring skills in reflective practice (Loughran and Berry 2005).
Modeling Is a Guide, Not a Recipe A challenge associated with the use of modeling teaching as a strategy is that any given episode can only highlight certain aspects of a topic, issue, theory, thought process, or practice. The observer or novice teacher may misinterpret a modeling episode intended to show how a topic might be taught, to mean this is how that topic must be taught. For example, modeling the constructivist teaching approach of probing prior knowledge of students for a particular science concept may be misconstrued to indicate that every science concept one teaches will require such probing. Similarly, modeling the use of teaching approaches such as concept mapping for reviewing topic “X,” or role playing for promoting an embodied understanding of a particularly complex scientific process, may lead novice teachers to link the modeled teaching approach with the particular topic used in the exemplar or to broadly apply the modeled practice to topics where it will not assist with student learning.
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To address this issue, it is essential that all modeling of teaching be accompanied by some conversation about the practice or practices that were modeled. Debriefing of a modeled episode needs to incorporate reflection and participation by the preservice teachers, as well as explicit discussion of the instructor’s intentions or a conversation on teaching about teaching by the educators who undertook the modeling. This combination of dialogic debrief elements avoids the educator adopting (and modeling) a “teaching as telling, showing, [and] guided practice approach” (Myers 2002, p. 131).
Learning Through Modeling Modeling of both “good” and “bad” teaching has merit, but both types of exemplars must be accompanied by discussions of practice. Modeling of poor practices with preservice teachers participating as students can provoke thinking and discussion about teaching and learning issues that may be invisible and tacit and thus missed in “good” and effective instructional exemplars. For example, modeling teaching of a laboratory activity where the required materials are not all available, are poorly labeled, or are not distributed on the laboratory bench, where safety concerns are handled cavalierly, and where students are rushing to complete the laboratory and data recording can lead to rich discussions of the important safety, organization, and learning elements of hands-on laboratory lessons. Both the modeling of teaching and learning from that modeling require skills and practice. Novice teachers as observers of modeled episodes need skills in order to see and understand what is happening. To assist and scaffold such learning, teacher educators should establish a “safe,” trusting classroom environment where talk about teaching is encouraged and happens regularly and guidelines for how to offer honest feedback in a positive and supportive manner are provided.
Conclusion The intentional and explicit modeling of teaching, the ability to “unpack” that teaching without being
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didactic, and modeling the critique of teaching for the learner are complex processes that contribute to the development of teacher educators and represent a form of professional knowledge that is researched through the self-study of teaching and teacher education (Loughran and Berry 2005). Thus, modeling teaching informs not only those who are learning to teach but also those who do the modeling of practice.
Cross-References ▶ Identity of Teacher Educators ▶ Pedagogy of Teacher Education ▶ Teacher Educator as Learner ▶ Teacher Professional Development
References Loughran JJ, Berry A (2005) Modelling by teacher educators. Teach Teach Educ 21:193–203 Myers CB (2002) Can self-study challenge the belief that telling, showing, and guided practice constitute adequate teacher education? In: Loughran J, Russell T (eds) Improving teacher education practice through selfstudy. Routledge Falmer, London, pp 130–142
Models Cynthia Passmore School of Education, University of California, Davis, Davis, CA, USA
Keywords Explanation; Mechanisms; Scientific reasoning
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Science is fundamentally about coming to understand the world in both its complexity and amazing elegance. At its core it is about developing explanations for the workings of the natural world based on evidence. Science is a particular way of approaching the generation, evaluation, and revision of knowledge that has served as the foundation for profound shifts in our collective understandings and our manipulation of the world.
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Models are viewed by many as the functional units of scientific thought. They allow the scientist to reason about a phenomenon by representing and highlighting aspects of that phenomenon that are salient to answering questions about how and why the phenomenon works the way it does. They take a small number of theoretical ideas and highlight the key relationships among them making it possible to reason with the ideas to address questions and problems in the discipline (Giere 1988). Models – sets of ideas that define relationships among various theoretical and concrete objects and processes – are ubiquitous in science. Some examples of important and familiar models include the model of the atom, the model of the particulate nature of matter, the model of plate tectonics, and the model of protein synthesis. Models function as reasoning tools that allow one to bound, explore, organize, and investigate phenomena and to develop explanations, generalizations, abstractions, and causal claims about those phenomena. The verb modeling refers to the acts of generating, evaluating, revising, and using/applying models. Modeling is, at its core, about making sense of the world by producing and coupling theoretical interpretations and understandings with empirical measurements/observations in order to develop explanations and predictions about phenomena. Consistent with its centrality in scientific reasoning, modeling is now recognized as a key practice that can be used to organize and structure classroom science from the elementary grades through graduate school, and likewise core disciplinary models can serve as the foundation for content organization of science education. Thus, just as models and modeling are often at the center of accounts of scientific practice, an explicit focus on models and modeling is becoming more common in science education.
Models Models are sets of ideas about why particular classes of phenomena function the way they do. They exist on a continuum between nascent
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and naı¨ve ideas in an individual’s mind – often called mental or internal models – and clear and widely shared ideas that guide a community of scholars in a particular field, often called expressed or conceptual models. They provide an important foundation for scientific thought. Models are human constructions that stipulate a set of relationships between and among observed or theoretical processes, events, and objects. Models can be used to figure out and understand how something works. For example, a model for the particulate nature of matter stipulates relationships between small bits of matter (atoms). Such a model would include the core stipulation that the matter in the world is made up of tiny, unseen particles and that those particles interact with one another in specific ways. From this model (the set of ideas about how particles behave), one can understand and explain a broad class of phenomena from phases of matter to how smell travels across a room. The level of detail and the particular attributes of a model are dependent upon the aim of the modeler, and therefore, there may be many models for reasoning about any particular phenomenon. Take again the case of the particulate nature of matter. Depending on what the modeler is using the model for, the model may invoke a generic particle, or it may invoke those particles as atoms and molecules with particular attributes (e.g., polarity). The model is a dynamic mental entity that can be used to explain and predict phenomena. Determining the relationships among the model ideas is what enables a model to function as a reasoning tool. Even though the word model is used in general parlance to refer to purely descriptive depictions, in science, something does not function as a model unless it can be used to reason about the underlying causes of a phenomenon. In this sense, models sit between theory and evidence. Within a community of practice, a model must be externalized and communicated in order to undergo evaluation and be used by the community. The externalized model can take a range of representational forms depending on the aim of the modeler and the purposes to which
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the model is being put to in a particular context. Many models that contain the same core conception of a system can be alternatively represented in diagrammatic, narrative, and/or mathematical forms. However, it is not the expressed form that makes something a model or not; rather it is the way in which the underlying ideas are used toward a goal of sensemaking that defines a model. Models are subject to evaluation based on conceptual as well as empirical criteria. Model evaluation based on conceptual criteria involves comparing the model to previously agreed upon theoretical ideas or other models. Typically, scientists strive for consistency among the models in their discipline such that the assumptions in one model do not violate or contradict those in another. While this is a common criterion to use for evaluation, there are some times when two contradictory models may be held by the community and used in different circumstances because each has some utility in explaining or predicting empirical data. Perhaps the most notable instance of this in physics is the continued use of both particle and wave models of light. Empirical criteria are applied when comparing a model or the explanatory output of the model to observations and data. The scientist asks if the model is useful in explaining the data at hand, and in some cases the predictive power of the model is used to evaluate the utility of the model. It is the correspondence between the model and the phenomenon that is key to empirical model evaluation, and in that sense a model can be thought of as a representation of particular aspects of the phenomenon.
Modeling Models are at the core of scientific reasoning. They hold the theoretical ideas of the reasoner in a clarified arrangement that allows her/him to make sense of the world. Models mediate between the more abstract and very general theoretical ideas in a field and the empirical world. The act of modeling includes developing, examining, critiquing, and using these sets of ideas.
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Modeling is the active process of putting discrete ideas together by stipulating the relationships between and among them. Modeling is also the act of examining the utility of the model in the face of some sensemaking need. In this case the ideas are held up and examined for their fruitfulness. This may result in particular ideas or relationships being kept as is, discarded, or modified. This analysis and revision process is one form of modeling. And finally, when a scientist is using the model and applying it in a deliberate way to a phenomenon, she/he is modeling.
Functions of Models Models serve a variety of functions and are central to the day-to-day working and reasoning of most scientists (Osbeck et al. 2010). In some disciplines models are named with the word model and the modeling is explicit. In others the role of the model may be more implicit, but in virtually all cases there is a small set of ideas that are at the core of particular scientific activities. Models are used in practice to define and develop questions, they often point to specific investigations and data collection needs, they form the basis of explanations and predictions, and they are key to communicating about the processes that underlie observable phenomena. Thus, models inform and mediate a range of activity in science, and in turn these activities become the basis of further model development and refinement. Scientists alternate between interacting with the physical world and developing models that explain measurements and observations. Extant models play a key role in helping to define the scope of observations. They become important filters on the world that affect what the scientist notices and finds worthy of investigation. In this way, models that already exist in a discipline are an important element in the generation of questions worth pursuing. For example, if one was interested in explaining the existence of particular trait variations in a population of organisms, the aspects of that phenomenon that are salient would depend in large part on the models that the phenomenon was viewed through. If the
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investigator was operating with evolutionary models, the observations and questions asked would differ from those asked from the perspective of physiological models. As scientists use their models to ask questions about the world, they must both incorporate and generate data in a systematic way. The model often assists the scientist in considering what kind of data would be useful, and in this way, models provide a foundation for investigations in science. Since models incorporate the current understanding of a phenomenon, they form the basis of the scientist’s interactions with that phenomenon. Once an investigation is complete, the data generated are analyzed with regard to the model, and the model is evaluated with regard to the data. That is, the scientist seeks to understand the data with regard to the model, incorporates the empirical results into the model, and uses those results to inform further refinements of the model. Models are developed with respect to a particular phenomenon or class of phenomena. In this way many models begin in a rather specific state. However, the aim of the modeler is to explain a broader swath of the world, and thus, models developed in one context can be taken to other contexts, tested for their fruitfulness, and often revised once again. The common use of model organisms in the biological sciences is a case in point. Scientists investigate the workings of particular organisms in order to first understand how processes and mechanisms work in that species. Then they use that understanding as a basis for explaining similar attributes across a wider range of organisms. A model for a specific metabolic pathway in one species, for example, may then be revised or broadened when taking into account how the system works across several species.
Models and Modeling in the Science Classroom In science education the term models has been used to refer to a wide class of objects. Science teachers often refer to objects in their classrooms such as plastic torsos and Styrofoam ball depictions of the solar system as models. Teachers also
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often talk about modeling particular behaviors for students in the sense that they show them what to do or how to do it. While these uses of the word model and modeling are legitimate uses in education, in terms of scientific practice, models and modeling take on a much more specific meaning as explicated above. Merely depicting or describing a system should not be the endpoint of activity because science is centrally concerned with understanding and exploring the mechanisms that underlie the observable world. An explicit focus on models and modeling in the science classroom is becoming more common (Windschitl et al. 2008). Classroom teachers have crafted experiences for students that make the model-based sensemaking that scientists do accessible and attainable for children across grade levels (Schwarz et al. 2009). One curricular area that is commonly taught with an explicit focus on models and modeling is around the particulate nature of matter. This example can illustrate how classrooms can be centered on model-based sensemaking and how a focus on models and modeling can productively organize classroom activity in much the same way that it organizes scientific activity. Imagine a middle school classroom in which the students are investigating the attributes of matter. The curricular unit begins with some concrete experiences and a focus on a few phenomena like the movement of a smell across the room, the way a drop of food coloring disperses in a glass of water, and the phase changes of water. With the assistance of the teacher, the students are pushed to wonder what it is about the nature and structure of matter that would help them explain these phenomena. Over the ensuing days, they experience more phenomena and are introduced to and develop the ideas of particles and their movements and interactions. This can play out in classrooms in a wide variety of ways, but the key feature of this experience must be that students come to a deep understanding of the various aspects of the model, that they develop and elaborate the core stipulations in the model as a community, and that they work to test those ideas against phenomena by using them to explain the phenomena that motivated their investigations.
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This kind of classroom is in stark contrast to one where students are told that the world is made up of small particles, called atoms and molecules, and asked to repeat that fact on a test. On the surface these two contexts may appear similar in that in each classroom one goal is for the students to emerge with the knowledge that matter is made of particles. However, in the classroom in which this idea is presented to students in its final form and assessed as a fact, the students have not experienced using the ideas as a tool for making sense of the world nor are they likely to understand their utility in explaining phenomena. On the other hand, when the exploration of matter is motivated by asking questions about phenomena and the road to answering those questions is through clearly articulating a model for the underlying mechanisms that can be used to explain those phenomena, the students may achieve something more than simply knowing the definition of atoms and molecules. Modeling provides an opportunity for students to connect sets of ideas together, thus making it possible for them to construct a rich understanding of the phenomena in the context of an explanatory framework. Designing classrooms that are model based involves a shift both in pedagogy and in content organization. Classrooms that are model based focus on the connections between theoretical ideas and observable phenomena and can provide opportunities for students to use models to develop explanations and engage in argumentation (Passmore and Svoboda 2012). This implies that the teacher must craft an environment in which students are active learners in a community setting and that scientific concepts are not presented as discrete facts.
Summary A focus on models and modeling has become more prominent in science classrooms. This focus has come as science educators have made deliberate attempts to build science experiences that mirror important aspects of scientific practice. Because modeling is central to reasoning in science, it provides an important framework for
Morals and Science Education
engaging students in scientific reasoning. Modelfocused science classrooms provide students with opportunities to make sense of the world by developing, representing, sharing, and applying models.
Cross-References ▶ Communities of Practice ▶ Mechanisms ▶ Multimodal Representations and Science Learning ▶ Scientific Visualizations ▶ Visualization and the Learning of Science
References Giere RN (1988) Explaining science: a cognitive approach. University of Chicago Press, Chicago Osbeck L, Nersessian NJ, Malone KR, Newstetter W (2010) Science as psychology: sense-making and identity in science practice. Cambridge University Press, New York Passmore C, Svoboda J (2012) Exploring opportunities for argumentation in modelling classrooms. Int J Sci Educ 34:1535–1554. doi:10.1080/09500693.2011. 577842 Schwarz CV, Reiser BJ, Davis EA, Kenyon LO, Ache´r A, Fortus D, Shwartz Y et al (2009) Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. J Res Sci Teach 46(6):632–654. doi:10.1002/tea.20311 Windschitl M, Thompson J, Braaten M (2008) How novice science teachers appropriate epistemic discourses around model-based inquiry for use in classrooms. Cogn Instr 26(3):310–378. doi:10.1080/ 07370000802177193, Routledge
Morals and Science Education Holmes Rolston III Colorado State University, Fort Collins, CO, USA
Science and conscience have a vital, if sometimes uneasy, relationship. Moral education demands levels of responsible agency that science
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education does not, owing to the shift from what is the case to what ought to be the case. Facts and causes are the domain of science and values and duties the domain of ethics; but criticism is equally requisite in both. Science and ethics alike are embedded in traditions where truths are shared through education. Ethicists often find stages in moral life; no analogous claims have been made for scientific life. Morality has to be chosen, entered into, lived, and practiced, in ways that science does not. People are responsible for their values as they are not for their science. Astronomy is sometimes thought to leave humans lost and lonely among the stars, and this may leave puzzles where to place Earthbound human morality in a vast meaningless universe. “The more the universe seems comprehensible, the more it also seems pointless” (Weinberg 1988, p. 154). More recently physics has made dramatic discoveries at astronomical and submicroscopic ranges, such as the formation of elements in the stars involving microphysical process, such that the midrange scales, where the known complexity mostly lies (in ecosystems or human brains), depend on the interacting microscopic and astronomical ranges. This “anthropic principle” endorses and even celebrates human cognitive and moral powers. We humans do not live at the range of the infinitely small, nor at that of the infinitely large, but we may well live at the range of the infinitely complex. That restores human dignity and worth (Barr 2003). Biological sciences often carry implicit or explicit overtones of who and what humans are, which may not be coherent with the implicit or explicit human self-understandings in classical or contemporary moral education. Human behavior is shaped by selfish genes (Dawkins 1989); we should biologicize ethics as disguised selfinterest (Wilson 1975, p. 562). If so, can humans be altruistic? Scratch an “altruist” and watch a “hypocrite” bleed (Ghiselin 1974, p. 247). Ethicists may agree about selfish tendencies in human nature but argue that humans can and ought to be educated toward a common good, or at least more enlightened self-interests.
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Theologians may find that humans are in need of redemption. Meanwhile, biologists may find more cooperation coded into the human genome than previously thought (Nowak and Highfield 2011). The sciences may also open up new possibilities (cloning, genetically modified genes; Bruce and Bruce 1998) or threats (climate change, mass extinction; Gardiner 2011) with which inherited moral systems are unfamiliar. Moral education may enlighten and elevate the human nature that has evolved biologically (Campbell 1976). By prevailing Darwinian accounts, biological natural history results from natural selection, which is thought to be blind, both in the genetic variations bubbling up without regard to the needs of the organism and in selection for survival, without regard to advance. Other biologists hold that such behavior can be more positively interpreted. Organisms defend their lives; their so-called selfishness is really self-actualizing, the defense of vitality. Reproduction is the ongoing sharing of biological value and promise. The genes function to conserve life; they also make possible a creative upflow of life struggling through turnover of species and resulting in more diverse and complex forms of life over millennia. Such biologists emphasize the continuing vital creative processes over time, the ascent of life from the simple to the complex, a prolific (pro-life) biosphere, the conservation and elaboration of genetic information, and the effective and efficient results of genetic creativity and natural selection. This may lead to a sense of respect for life, made possible by our human singularity, the sole species with moral powers, and with responsibility for caring for other humans and for the Earth. Reinterpreting natural history more constructively may also have implications for human selfestimates. Humans evolved from prehuman primate ancestors; we may be told that we inherit a monkey’s mind. “DNA evidence provides an objective non-anthropocentric view of the place of humans in evolution. We humans appear as only slightly remodeled chimpanzee-like apes”
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(Wildman et al. 2003, p. 7181). But humans have over three times the brain size of chimps, so that a 3 % difference in protein structures makes 300 % bigger brains. Cognitively, we are not 3 % but 300 % different (Marks 2002, p. 23). When you compare Einstein with a chimp, it does not appear that Einstein is only slightly remodeled; nor do we wonder whether an atomic bomb built with his theory that E ¼ mc2 is a slightly remodeled ant-fishing stick. An explosion of cognitive powers emerges with the human mind, an event otherwise unknown in natural history. Neurosciences may agree that the human mind is immensely complex and also find openness and mutability (in synaptic connections) that permits humans to be morally responsible (Merzenich 2001). “We are hugely different. . . . the differences are light years apart” (Gazzaniga 2008, p. 13). The ecological sciences will add that on Earth humans are (and ought to be) at home, the root idea in ecology. A moral priority is a sustainable biosphere. Ecologists also find that humans are degrading the biosphere. They may be apprehensive about ecosystem services or impending extinctions (Millennium Ecosystem Assessment 2005). They will demand education in conservation biology. No one is rational if he or she is neutral, dispassionate, about one’s home. One is immoral if unconcerned about life in jeopardy on one’s home planet. Biologists are almost unanimous in their respect for life on an endangered planet. The Earth’s impressive and unique biodiversity warrants wonder and care. In both science and moral education, one seeks enlightenment. Philosophers may push the claim that modern science, after 400 years, still leaves the ultimate value questions urgent and unresolved. Indeed, there is no scientific guidance of life. The value questions remain as acute and painful as ever.
Cross-References ▶ Biology, Philosophy of ▶ Cultural Values and Science Education
Motivation and the Learning of Science
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References
Introduction
Barr SM (2003) Modern physics and ancient faith. University of Notre Dame Press, Notre Dame Bruce D, Bruce A (1998) Engineering genesis: the ethics of genetic engineering in non-human species. Earthscan Publications, London Campbell DT (1976) On the conflicts between biological and social evolution and between psychology and moral tradition. Zygon J Relig Sci 11:167–208 Dawkins R (1989) The selfish gene, new ed. Oxford University Press, New York Gardiner SM (2011) A perfect moral storm: the ethical tragedy of climate change. Oxford University Press, New York Gazzaniga MS (2008) Human: the science behind what makes us unique. Ecco, Harper Collins, New York Ghiselin M (1974) The economy of nature and the evolution of sex. University of California Press, Berkeley Marks J (2002) What it means to be 98 % chimpanzee: apes, people, and their genes. University of California Press, Berkeley Merzenich M (2001) The power of mutable maps. In: Bear MF, Connors BW, Paradiso MA (eds) Neuroscience: exploring the brain, 2nd edn. Lippincott Williams and Wilkins, Baltimore, p 418 Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: synthesis. Island Press, Washington, DC Nowak M, Highfield R (2011) Supercooperators: altruism, evolution, and why we need each other to succeed. Free Press, New York Weinberg S (1988) The first three minutes. Basic Books, New York Wildman DE, Uddin M, Liu G, Grossman LI, Goodman M (2003) Implications of natural selection in shaping 99.4 % nonsynonymous DNA identity between humans and chimpanzees: enlarging genus Homo. Proc Natl Acad Sci U S A 100:7181–7188 Wilson EO (1975) Sociobiology: the new synthesis. Harvard University Press, Cambridge, MA
Why is motivation so important? Learning is typically the result of intellectual, emotional, and physical engagement, and engagement is an outcome of motivation. Without motivation there is little engagement, and without engagement, little learning can occur. This is true in general, not only for the learning of science; one is not likely to become proficient at tennis, carpentry, or any intellectual activity unless one is motivated, for whatever the reason, to become proficient at these activities. While motivation is a necessary condition for learning to occur, it is also a desired outcome of learning. Knowledge is like a skyscraper, each level built on the foundations provided by the lower levels. However, for the construction to continue beyond a certain level, there needs to be motivation to do so. Thus, for example, one can learn about Rutherford’s model of the atom and be content with this knowledge, without any desire to learn more about the atom. Moving on to the next level (perhaps Bohr’s model of the atom, in this case) requires the motivation to go further. This motivation can be developed and fed by the process of learning about Rutherford’s model. Thus, learning and motivation are intertwined – the first seldom occurs without the second and the second needs to be fostered by the first for learning to continue.
Motivation and the Learning of Science David Fortus Weizmann Institute of Science, Rehovot, Israel
Keywords Achievement goals; Engagement; Lifelong learning; Motivation; Self-determination
Theories of Motivation There exist several theories of motivation (Schunk et al. 2008) – expectancy-value theory, attribution theory, self-determination theory, and achievement goal theory. While none of these theories were developed specifically for use in science education, they have all been applied in the context of science education. Expectancy-value theory in science education addresses the combined effect of the perceived value of learning a science topic and the expectation of succeeding to learn it, so Level of motivation ¼ perceived value success expectancy One’s success expectancy is directly related to one’s perceived self-efficacy in science, while the
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perceived value of learning a topic is related to the cost involved in learning the topic (time, effort, negative feelings), the interest in the topic and the pleasure involved in learning it, the practical value of understanding the topic, and if the learning of the topic supports the attainment of personal needs (social acceptance, values, identity). Attribution theory attends to the perceived attributes for success or failure at a task related to learning, such as talent, perseverance, luck, difficulty of task, etc. Each of these attributes has three dimensions: (a) Locus – Is the attribute perceived by the individual as located “in” or “out of” him/her (such as skill and self-efficacy, attributes that are typically perceived as individual, personal, located “in” the student, versus difficulty of the task, which is typically perceived as an externally imposed condition, and therefore “out” of the learner)? (b) Controllability – Is the attribute grasped as something one can control and manipulate (such as effort versus luck)? The locus of control is a combination of locus and controllability. For example, while I may accept that I control how much effort I exert, I may feel that I am being forced to exert effort rather than choosing to do so myself. The locus of control has been used in studies on wait time, problem solving, understanding of the nature of science, meaningful learning, and others. (c) Stability – Is the attribute grasped as something constant or variable (such as fatigue or help from friends)? The more the attributes for success are perceived as internally located, controllable and stable, the greater will be the motivation to engage in a learning task. Both expectancy-value theory and attribution theory focus on the magnitude of motivation for engaging in a learning task; while self-determination theory and achievement goals theory also attend with the effects of being more or less motivated, they also emphasize the qualitative nature of motivation, distinguishing between different types of motivation.
Motivation and the Learning of Science
Self-determination theory emphasizes every person’s need to belong, to feel able, and to be autonomous. It distinguishes between intrinsic motivation (doing something for its own sake) and extrinsic motivation (doing something because it leads to certain results). Research shows that highly intrinsically motivated people tend to be more adaptable and have better conceptual understanding than people with lower intrinsic motivation. Intrinsic motivation is supported by providing students with a sense of control and choice, challenges that lie within their zone of proximal development, positive feedback about their abilities, and close personal relations. Achievement goal theory has been used more often in science education studies than the other motivational theories. It focuses on individuals’ goal orientation, which is why individuals engage in learning activities. The theory distinguishes between two main goal orientations: mastery goals orientation and performance goals orientation. Mastery-oriented individuals strive to develop competence in order to achieve a sense of mastery; they learn because they want to understand. Mastery goals orientation is positively associated with a wide range of positive cognitive, emotional, and behavioral outcomes and should therefore be fostered by parents, teachers, and schools. Performance-oriented individuals strive to demonstrate competence and are therefore concerned with others’ perceptions of their competence and with their ability relative to others. Performance goals are subdivided into performance-approach and performance-avoidance goals. According to this distinction, when pursuing performanceapproach goals, individuals are focused on attaining favorable judgments of competence; they learn because they want others to think they understand. On the other hand, when pursuing performance-avoidance goals, individuals are focused on avoiding unfavorable judgments of competence; they are concerned that others may think that they don’t understand. Findings from studies that have adopted this distinction between performance-approach goals and performance-avoidance goals support its
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prevalence among students and strongly suggest that performance-avoidance goals are associated with maladaptive patterns of engagement. On the other hand, the evidence regarding performanceapproach goals is not consistent. People are not either mastery oriented or performance oriented. Mastery orientation and performance orientation are two independent continua characterizing individual learners. One can be both high mastery and performance oriented or be characterized by any two values of goal orientation on both continua.
Aspects of the Significance of Motivation in Learning Science We have all learned things that we no longer remember; often we don’t even remember that we ever learned these things! Knowledge that is not revisited, built on, used, and embellished is doomed to fade away. When we are faced with an issue, such as whether or not to consume genetically modified foods, in order to reach a position that is not just emotionally driven but also supported by reason, we need to be able to recall and draw upon concepts related to genetics, nutrition, and ecosystems that we learned in middle and high school. More likely is that whatever we learned in high school will not suffice to reach a firm understanding of the issues at stake in genetic engineering; so we will need to go beyond our school-based knowledge and learn new ideas. For this learning to occur, we need to be motivated to learn. Thus, the goal of helping people become lifelong learners is contingent on the existence of ongoing motivation to learn (Maehr 2012). The goal of education is not to fill a pail but to light a fire (sometimes attributed to Yeats). Rather than feed the fire of motivation to learn science, many studies from the late 1960s and onward suggest that schools often do the opposite – they extinguish it. Six-year-old children are typically full of awe and curiosity about the world. Your average 14-year-old child, however, has lost his drive to understand and make
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sense of the world, he is no longer inquisitive. Research has not only documented students’ declining motivation to learn science but has also shown that this waning of motivation to learn science is not an inevitable consequence of adolescence; schools and teachers can support and enhance students’ motivation to engage with science or they can diminish it (Nolen 2003; Vedder-Weiss and Fortus 2012). Clearly this has great significance when considering the widespread phenomenon of declining student enrollments in science and how to reverse such trends. Understanding the reasons for this decline in the motivation to learn science and how to best address it should be one of the central goals of the science education community.
Cross-References ▶ Affect in Learning Science ▶ Attitudes to Science and to Learning Science ▶ Emotion and the Teaching and Learning of Science ▶ Interests in Science ▶ Self-Efficacy in Learning Science
References Maehr ML (2012) Encouraging a continuing personal investment in learning: motivation as an instructional outcome. Information Age, Charlotte Nolen SB (2003) Learning environment, motivation, and achievement in high school science. J Res Sci Teach 40(4):347–368 Schunk DH, Pintrich PR, Meece JL (2008) Motivation in education: theory, research and application. Pearson, Upper Saddle River/Columbus Vedder-Weiss D, Fortus D (2012) Students’ declining motivation to learn science: a follow up study. J Res Sci Teach 49(9):1057–1095
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Multiculturalism
Culture; Diversity; Equity; Multicultural; Social justice
or subcultures share traits and values that bind them together as a group. Although numerous microcultures exist within most nations, the United States is exceptionally rich in having very diverse and distinct cultural groups that make up its population and history. Individuals with competencies in several microcultures may be considered bicultural or multicultural or bilingual and multilingual. These individuals may develop a broader view and range of cultural competencies as a member of multiple microcultures (Grant and Ladson-Billings 1997).
Multiculturalism
Multicultural Education
Multiculturalism has been used by scholars and practitioners from countries all over the world in referring to educational efforts to instruct and infuse more positive values about human pluralism and to improve the learning potential for all students. Multiculturalism is a philosophical position and movement that assumes that the gender, ethnic, racial, and cultural diversity of a pluralistic society should be reflected in all of its institutionalized structures but especially in educational institutions, including the staff, norms and values, curriculum, and student body. It recognizes that equality and equity are not the same thing, meaning that equal access does not necessarily guarantee fairness. Within the United States, the origin of multiculturalism was more concerned with freedom, liberty, and equality and less with particular subcultures in society. However, over time, multiculturalism has evolved to focus on ethnic and cultural diversity, as well as cultural identity. Cultural identity is based on traits and values learned as part of our ethnic origin, religion, gender, age, socioeconomic level, primary language, geographical region, place of residence (e.g., rural, suburban, or urban), and disabilities or exceptional conditions. Each of these groups (also called microcultures, subsocieties, subcultures, subcommunities) has distinguishable cultural patterns shared with others who identify themselves as members of that particular group. Individuals belonging to the same microcultures
Multicultural education grew out of two primary movements – the Civil Rights Movement of the 1960s and 1970s and the Ethnic Studies movement, which grew out of the Civil Rights Movement. During these movements, African Americans and many other groups of color demanded equity and equality in the policies and practices of schooling. Consequently, numerous schools developed and taught ethnic studies courses and colleges and universities established ethnic studies departments or programs. Also during the Civil Rights Movement, other groups (women, people with disabilities, the poor, and gays, lesbians, and bisexuals) began to increase their efforts to make schooling equal and equitable for members of their groups. In the 1970s, the multicultural education movement grew. The Civil Rights Movement and the Ethnic Studies movements were energized by scholarship and participation by groups of color, women, and people with disabilities, and soon the influence of these movements began to capture the attention of K-12 and university educators. From that time to the present day, multicultural education has developed to become the educational vision and approach to school and societal change that has been advocated by an increasing number of people, but also objected and challenged by others. Multicultural education, sometimes referred to as multiethnic education, antiracist education, or multiracial education, has been used by
Multiculturalism Felicia Moore Mensah Science Education, Teachers College, Columbia University, New York, NY, USA
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countries around the world and thus widely used in the field of education. It is a philosophical concept and an educational process. Multicultural education as a philosophy and practice continued its early development as different groups (African American, Asian American, Latino, Native American, and European American) began to learn (both formally and informally) about how race, class, and gender influence their presence in society and in educational settings and how their group and other groups contributed to the growth, development, and history of the United States. As an educational process, multicultural education has many approaches that vary widely. Although there are many different approaches, leaders within the field of multiculturalism have reached some level of consensus on the goals, aims, and purpose of multicultural education (Banks 2001).
Goals of Multicultural Education A major goal of multicultural education is to reform the school and other educational institutions so that students from diverse racial, ethnic, and socialclass groups will experience educational equality (Banks 2001). Another important goal of multicultural education is to provide both male and female students an equal chance to experience educational success and mobility. Multicultural education theorists are increasingly interested in how the interaction of race, class, and gender influences education, yet the emphasis that is given to these variables varies greatly. Most multicultural scholars and researchers agree that institutional changes must occur if the goals of multicultural education are realized or implemented successfully. Institutional changes may reside in changes in the school curriculum; teaching materials; teaching and learning styles; the attitudes, perceptions, and behaviors of teachers and administrators; and the goals, norms, and culture of the school (Sleeter and Grant 2009). Therefore, multicultural education confronts social issues involving race, ethnicity, socioeconomic class, gender, sexual orientation, and disability. Multicultural education provides instruction in familiar contexts that are
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built upon students’ diverse ways of thinking. It encourages students to investigate world and national events, as well as how these events affect their lives. It teaches critical thinking skills, as well as democratic decision making, social action, and empowerment skills. Multicultural education theory, practice, and research are conceptually defined in several different ways, with a number of educators attempting to deal with this differentiation by developing typologies or approaches to multicultural education. These typologies and approaches also aid in defining the goals of multicultural education. Banks (2001) describes five dimensions of multicultural education as content integration, the knowledge construction process, prejudice reduction, an equity pedagogy, and empowering school and social structures. Briefly, (a) content integration deals with the extent to which teachers use examples, data, and information from a variety of microcultures in illustrating basic educational concepts, principles, generalizations, and theories in their subject area or discipline; (b) the knowledge construction process describes the procedures by which social, behavioral, and natural scientists create knowledge and how the implicit cultural assumptions, frames of references, perspectives, and biases within a discipline influence the ways that knowledge is constructed within it; (c) prejudice reduction describes the characteristics of children’s racial attitudes and strategies that can be used to help students develop more democratic attitudes and values; (d) an equity pedagogy exists when teachers use techniques and methods that facilitate the academic achievement of students from diverse racial, ethnic, and social-class backgrounds. Finally, (e) empowering school and social structures are designed to help students and teachers cross racial and ethnic boundaries so that students may participate more effectively in school and society. These five dimensions of multicultural education help students to develop positive self-concepts and identities as they learn about the culture, contributions, and history of diverse groups that have contributed to their nation’s history. Nieto frames a definition and goals of multicultural education within a sociopolitical context
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because multicultural education needs to confront issues of power and privilege in society and not just issues of difference (race, class, gender, etc.). Nieto and Bode (2008) focus on seven characteristics of multicultural education: antiracist education, basic education, important for all students, pervasive, education for social justice, a process, and critical pedagogy. First, (a) antiracist education makes antidiscrimination explicit in the curriculum and teaches students the skills to combat racism and other forms of oppression; (b) basic education advances the idea that all students have a right to engage in core academic subjects as well as the arts and to develop social and intellectual skills and knowledge to be used in a diverse society; (c) multicultural education is important for all students because it challenges the commonly held misunderstanding that it is only for students of color, multilingual students, or special interest groups; (d) the pervasive nature of multicultural education emphasizes an approach that permeates the entire educational experience, including school climate, physical environment, curriculum, and relationships; (e) social justice teachers and students put their learning into action, and students learn that they have the power to make a change in a democratic society; (f) as a process, multicultural education is an ongoing developmental process that involves relationship building among individuals and educational institutions; and (g) critical pedagogy draws upon experiences of students as well as multiple viewpoints that lead to self-reflection and action. From a review of the literature, Sleeter and Grant (1985) observed five approaches to multicultural education. They are teaching the exceptional and the culturally different, human relations, single- group studies, multicultural education, and education that is multicultural and social reconstructionist. Sleeter and Grant also acknowledged that there is some overlap between the five approaches that they outline. First, (a) teaching the exceptional and the culturally different aims to equip students with the academic skills, concepts, and values to function the American society as well as its culture and
Multiculturalism
institutions; (b) human relations consists of developing positive relationships among diverse groups and individuals to fight stereotyping and to promote unity; (c) single-group studies have a target group that is looked at in depth and information about the group’s history, including experiences with oppression and resistance to that oppression, is highlighted; (d) multicultural education is used by Sleeter and Grant as an approach to include the previous approaches and to deal with multiple groups at the same time and to reform the total schooling process so that all students benefit from a multicultural education; thus, this approach focuses on issues and concerns across multiple groups (Banks 2001); and (e) education that is multicultural and social reconstructionist describes a complete redesign and critical action toward social change. For example, it entails addressing issues and concerns that affect students of diverse groups and encourages students to take an active stance by challenging the status quo, speaking out, and joining with other groups in examining common or related concerns.
Multicultural Education Across Subjects Multicultural education extends to all subject areas, including science and mathematics as well as social studies, language arts, and art. Multicultural education across different subjects teaches students to apply critical thinking skills to all subject areas. Moreover, multicultural teacher education practice and policy derives from work within subject areas, such as multicultural science teacher education (Atwater and Riley 1993), and prepares teachers to become multiculturalists in their approach to teaching and vision for education. Scholars and practitioners new to multicultural education as a field may find that the existence of several different approaches to multicultural education may lead to conceptual confusion. This confusion suggests to some that multicultural education is inconsistent, making it the subject of criticism (see Grant and Ladson-Billings 1997 for discussion). The shortcoming of the multicultural approach is that it does not assertively
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address issues dealing with poverty and unemployment, nor does it necessarily help build the political skills and group solidarity that some ethnic groups need (Grant and Ladson-Billings 1997). However, as more researchers and teachers come to understand multiculturalism and multicultural education, and develop broader views in areas such as sociocultural perspectives and gender, the meanings of multiculturalism and multicultural education will become more defined, accepted, and affirmed as well as more refined in its definitions and approaches and its application across different subject areas. Therefore, teachers in each subject area can analyze their teaching style to determine the extent to which they reflect multicultural issues, values, and approaches.
Summary Multiculturalism is a common feature of many countries in the Twenty-first Century. For some, such as USA, multiculturalism has been a central feature of their society for many years, for others, such as Australia (by many measures today the most multicultural country on the planet) this is a more recent – post World War Two – development. Multicultural education acknowledges and affirms the belief that the strength and richness of such countries is in their human diversity. It demands a school staff that is multiracial, multiculturally literate, and multilingual. It demands a teaching staff that reflects gender and race diversity across subject matter areas. It demands a curriculum that organizes concepts and content around the contributions, perspectives, and experiences of all the groups that are a part of US society and the greater world. It confronts current social issues involving race, socioeconomic status (SES), gender, sexuality, and disability. Multicultural education accomplishes this by providing instruction in a social-cultural context that students understand and are familiar with and builds upon students’ learning styles and community strengths. It teaches critical thinking skills, as well as democratic decision making, social action, and empowerment skills. Finally, multicultural
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education is a total process – it cannot be truncated and trivialized. All the components of its definition must be in place for multicultural education to be genuine and viable.
Cross-References ▶ Cultural Influences on Science Education ▶ Cultural Values and Science Education ▶ Culture and Science Learning ▶ Curriculum ▶ Sociocultural Perspectives and Gender
References Atwater MM, Riley JP (1993) Multicultural science education: perspectives, definitions, and research agenda. Sci Educ 77(6):661–668 Banks JA (2001) Multicultural education: historical development, dimensions, and practice. In: Banks JA, McGee Banks CA (eds) Handbook of research on multicultural education. Jossey-Bass, San Francisco, pp 3–24 Grant CA, Ladson-Billings G (1997) Dictionary of multicultural education. The Oryx Press, Phoenix Nieto S, Bode P (2008) Affirming diversity, the sociopolitical context of multicultural education, 5th edn. Allyn & Bacon, Boston Sleeter CE, Grant CA (2009) Making choices for multicultural education: five approaches to race, class, and gender, 6th edn. Wiley, New York
Multimedia ▶ Multimedia Videos and Podcasting
Multimedia Videos and Podcasting Shawn Michael Bullock Faculty of Education, Simon Fraser University, Burnaby, BC, Canada
Keywords Multimedia; Online video; Podcasts; Video clips
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clips for public viewing. Not only can science teachers view videos “on demand”; they can dynamically search for relevant video content from home, at school, or during an unplanned moment in class. For example, a science teacher might quickly call up a YouTube video that helps explain a concept in response to an unanticipated question during a classroom discussion. Importantly, students with access to the Internet at home are able to find the videos shown in class or search for additional video content to supplement their learning. The high volume of multimedia videos, together with the highly accessible searching and viewing environments, can help students take more control over their learning, as ownership and control of video content no longer resides exclusively with teachers. New forms of digital devices, such as smartphones and tablet computers, have make accessing multimedia video content even easier. These devices routinely have built-in video cameras that enable both science teachers and students to create their own multimedia videos for pedagogical purposes. The twenty-first century has thus seen a shift away from using multimedia videos solely for the purposes of transmission of scientific knowledge; learners can now use relatively inexpensive and common equipment to access, create, and co-construct knowledge about science. Such applications of multimedia videos are consistent with a social constructivist view of learning and fit well within the recent social phenomena of “Web 2.0” (i.e., where many users contribute to collections like YouTube or Wikipedia and all benefit from the aggregated resources). One example of using video to empower students to create knowledge about science is found in the Slowmation project, which challenges students and teachers to create models of scientific concepts (e.g., mitosis) and then take digital photos of their models and edit them into a short stopmotion animation film, complete with voice-over narration (Hoban 2007). In the classroom, a science teacher would facilitate the creation of student-produced Slowmation videos as a way for students to visualize and reflect upon their own ideas as well as to reveal the range of
Science educators have a long history of incorporating diverse media into classroom experiences. From the advent of educational television (ETV) in the 1950s to microcomputers in the 1980s to the current applications of smartphones and tablets for learning, multimedia videos have been one of the principle means of augmenting the quality of learning in science classrooms. The role that videos play in science instruction has changed dramatically, however, both as a result of changing views of teaching and learning and as a result of the hardware available to teachers. Initially, educational television was thought of as a means of efficient of delivery of content and equity in school curriculum by ensuring that all children were able to watch high-quality content created by expert teachers (Cuban 1986). These early educational videos were thought of primarily in terms of the ability to transmit factually correct information. While some technology enthusiasts argued that ETV could replace teachers, most educators viewed television as a teaching aid to be used under certain conditions. Television was valued for its ability to depict dynamic scientific processes and remote locations. Most science classrooms have access to TVs, VCRs, and DVD players so that teachers can show videos (or clips of videos) deemed to have some educational value. The use of videocassettes and DVDs reflect the expectation that videos are best used “on demand” (i.e., rather than on scheduled broadcast) and that teachers must have flexibility to integrate them into their curriculum. The World Wide Web (WWW) has introduced a vital new source of multimedia videos for use in science classrooms. Although the first decade of the public use of the WWW did not have sufficient infrastructure to make video streaming practical in most cases, the increased bandwidth and network service to schools, together with the proliferation of computers, have provided science teachers with a wealth of accessible digital video online. The popular video-sharing site YouTube, founded in early 2005, makes it easy for anyone to upload video
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conceptual models that exist within the student community. Around the same time the YouTube.com emerged, a journalist commenting on the new form of amateur radio on the Internet coined the term “podcasting” (Berry 2006). Podcasting is a portmanteau of the words “iPod” and “broadcast” – the former being the most popular (although by no means only) digital media player over the last decade. A podcast is a syndicated form of media – either audio, video, or textual – that is delivered over the Web. Users can “subscribe” to any podcast using popular software such as iTunes, or via a dedicated website, and listen to or view it using a computer, tablet, smartphone, portable gaming system, or dedicated media player. Podcasts have been called “a converged medium” because they bring together audio and video media, the Internet, and media devices (Berry 2006). Although there are many professionally produced podcasts available from sites such as Scientific American (e.g., http://www.scientificamerican.com/multimedia.cfm) and the Canadian Broadcasting Corporation (CBC, e.g., http://www.cbc.ca/quirks/), one of the hallmarks of podcasting is the vast array of contributions made by amateurs who simply wish to create and broadcast their own content. Almost any current computer system has the necessary hardware and software to create a podcast, and a variety of free solutions are available for uploading and “hosting” the podcasts. Thus, the barrier to creating and distributing podcasts is low, suggesting possible applications for K12 or higher education. For example, a science teacher might use the Science Talk podcast from Scientific American in the same way that she/he might have used an educational television program in the past – to provide enriched multimedia content to students. Alternatively, a science teacher might challenge students to create their own science podcasts in small groups around a particular topic or theme. Podcasts and digital video clips, because of their ubiquity and ease of creation, editing, and publication, offer exciting new possibilities for science educators to find ways to encourage their students to construct knowledge of science.
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Cross-References ▶ Broadcast Media ▶ Online Media
References Berry R (2006) Will the iPod kill the radio star? Profiling podcasting as radio. Converg Int J Res New Media Technol 12(2):143–162. ISSN:1748–7382, 10.1177/ 1354856506066522 Cuban L (1986) Teachers and machines: the classroom use of technology since 1920. Teachers College Press, New York Hoban GF (2007) Using Slowmation to engage preservice elementary teachers in understanding science content knowledge. Contemp Issues Technol Teach Educ 7(2):75–91. ISSN:1528–5804.http://www.editlib.org/ p/26211. Retrieved 16 Dec 2013
Multimodal ▶ Slowmation
Multimodal Representations and Science Learning Terry Russell Centre for Lifelong Learning, University of Liverpool, Liverpool, UK
What Is Meant by Multimodal Representations? The rising science education interest in multimodal representations reflects science educators’ drive to get to the heart of knowledge generation and change, as close to the causal link between students’ not knowing and coming to understand as it is possible to get. “Multimodality” is beginning to define a discrete field of research, though, as it is still in relative infancy, it is in need of being more fully articulated. The general definition of multimodal representations used in this
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entry is “the depiction or communication of an idea or ideas using more than a single expressive mode, either in synchrony or separately.” Because education has traditionally been so strongly mediated by written and spoken language, an important aspect of this definition is that it is inclusive of all forms of expression, beyond only the linguistic. The definition of “representation” is more ambiguous as the term may be used to refer to both “internal representations” (the existence of which some would contest) and “external representations.” While the use of the one word for both internal and external states is essentially ubiquitous, it is not helpful, but in both instances, the intention is to refer to something “re-presented” or “revealed in another manner,” in a form that differs from that of the referent. Any serious analysis of multimodal representations is drawn into a consideration of its underpinnings in cognitive and brain science as well as philosophy. In those disciplines, the difficulty of dealing with epistemology and consciousness has long been recognized, making an unequivocally clear steer for educationalists’ application an unrealistic expectation. However, such a fundamental problem shared across disciplines is helpful in highlighting difficulties to avoid as well as positive ideas to pursue. Whether in spite of or because of the nonlinguistic comprehensiveness of the definition of multimodal representations, among the frontrunners exploring this new territory are educators specializing in applications to the language arts. The obvious multiple modes to which these educators attend include expressive language (oracy, text) and receptive language (listening, reading), supplemented by the range of modes that add information to the genres of drama, poetry, literature, and language acquisition: gesture, images, nonverbal sounds, facial expressions, and so forth. These modes may arise independently and in combination, sometimes dovetailing complementarily, at other times offering a richness through redundancy of information. “Social semiotics” explicates the multimodal expressions that communicate meanings using such combinations of linguistic and nonlinguistic sign systems. Semioticians will
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claim, entirely plausibly, that it is only when the full complexity of the multimodal signs and processes being used are analyzed that the nuances of communicated information may be fully comprehended. Semiotic deconstruction of this nature has been applied to the analysis of science teachers’ classroom communications. Arguably, such communicative performances are but one step away from rhetoric, in the sense of being shaped by an intent to present information to persuade, or at least, with a clarity that conveys conviction and optimizes accessibility for the recipient. The commercial and academic interest of the advertising, marketing, and entertainment industries in multimodality is also easy to understand, with “multimedia” and “multimodality” sharing common ground in the potential to exploit a range of communicative possibilities to inform and persuade. Indeed, Marshall McLuhan’s still widely quoted declaration that “The medium is the message” can be seen as a herald of current interest. Computer scientists are also significant stakeholders, in both modeling the cognitive processes and exploiting the multimedia potential. Science educators and learners have access to an extensive array of information communication modes, far beyond language enriched by gesture. Science education can exploit the full range of human sensory systems and use instrumentation to extend the detection of the entire electromagnetic spectrum. In addition, various formats and systems have been invented for quantifying data. A huge selection of possible multimodal representations is used habitually in science: text; images; cross-sectional and labeled diagrams; 3D models and specimens; mathematical, graphical, pictorial images and photographs; chemical symbols and equations; video and audio recordings; magnified images; waveforms; and many more formats. All can be described at a simple level in terms of the basic sensory channels by which they are accessed, with layers of complexity added when used symbolically. What evidence is available to support an informed analysis and management of multimodality, such that claims that it can help to make a difference to teaching and learning processes
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and outcomes might be tested? Does the modality of a representation make a difference to an individual’s performance as a learner addressing a problem or faced with acquiring a new understanding through engaging in some form of conceptual change process? Or from the instructional side of the process, what difference might views about multimodality make that will be of benefit to teaching? An encouraging illustration of representational modes making a difference to performance is found in U-shaped growth, the interpretation of which has a bearing on this discussion.
U-Shaped Behavioral Growth U-shaped behavioral growth is a phenomenon that has been debated in child development literature for decades and which continues to be reported in various manifestations. This form of growth has usually been described in crosssectional samples by age, where the correct solution to a problem is achieved by a younger age group, drops out in a middle group by age, and reappears in the behavior of older subjects in the sample. The domains in which this developmental pattern of correct-incorrect-correct outcome is recorded are varied; the age ranges typically span something like 4–12 years. The theoretical relevance to a discussion of multimodality resides in the interpretation of the subjects’ underlying reasoning. The favored interpretation is that two representational formats are being used, with the attempted deployment but incomplete mastery of a more advanced format resulting in a temporary decline in performance. This can be illustrated by reference to the understanding of transformations associated with intensive physical properties. An intensive property does not change with changes in its extensive property. Temperature is an example: volumes of water at a given temperature may be mixed (changed in extent) but the resulting temperature remains the same. Strength or “sweetness” of solution is another intensive property. When volumes of liquid of the same concentration are added together, the ratio of
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solute to solvent remains constant even as the extensive property is increased. When presented with the combination of two volumes at the same temperature or of identical sweetness, younger children (around 4 years) predict correctly no change in the resultant sweetness or temperature, as do older subjects (around 12). However, those approximately in the middle of the 4–12 age range tend to predict an increase in temperature or sweetness. It is the interpretation of this kind of error that illuminates the potential impact of multimodal representations. The inference, based on qualitative interview responses, is that two separate representational systems are being deployed. While the younger respondents are referring to an intuitive representation of temperature and sweetness, those in the mid range, with the correct intention, are attempting to use a mathematical or quantitative representation of the problem. However, they are failing to deal with what is a ratio problem, perhaps because they focus on the numerator rather than the proportion. Their prediction is incorrect until such time as the two representations – the temperature/sweetness and the ratio – are consolidated to generate a consistent outcome. An important aspect of the theoretical perspective on this interpretation is that the correct-incorrectcorrect behavioral sequence can be thought of as a positive incremental conceptual progress, though it is masked by a behavioral setback. The generalization drawn is that it helps to have some interpretation of what representations are guiding thinking inside students’ heads if we wish to manage multimodal representations positively.
The Structure of the Brain and the Structures of Thinking Knowledge of the structure and organization of the human brain has increased rapidly in recent years but remains one of the outstanding major frontiers of science. Historically, there was a belief that bumps and irregularities discernible on the skull’s outer surface might provide clues about the differentiated activity driven from
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within the carapace. When hypothesized correlations between externally measured features of the cranium and subjects’ behavioral and emotional life failed to convince, interest in such approaches withered. However this left some interesting legacies, one such being the idea of human psychology being differentiated and structurally localized, as assumed in faculty psychology. Localized damage may result in specific impairments of psychological functioning, as brain trauma incurred in combat, strokes, and accidents confirms. For example, damage to the right cortex is associated with motor impairment, to the left with language degradation, yet healing often involves compensatory effects, suggestive of an interconnected or holistic brain functioning. The specific and the general view of brain functioning must both be acknowledged and accommodated in explorations of multimodality.
Is Intellectual Functioning Domain Specific or Domain General? A view of the brain as a general-purpose problem-solving organ, fully interconnected for internal communication of data, might lead to a different view of multimodal representations as compared with an assumption of modularity. Is intellectual behavior better thought of as domain general (interconnected and homogeneous) or domain specific (modular, dealing in an array of restricted, relatively narrow classes of object and properties)? In the latter part of the twentieth century, it was suggested that some structural predisposition was needed to explain the enormously complex language development achieved so rapidly by very young children and the notion of the functioning of the brain as modular gained prominence. Modules for face recognition (another precociously developed human skill) and for the “theory of mind” (the attribution of mental states of knowing to others) have also been suggested as modules. Such domainspecific capabilities at the starting point of development are assumed by some to be distinct and discrete rather than generalizable to all learning.
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Ideas About Modularity of Mind Jerry Fodor, a prominent philosopher and cognitive scientist, suggested in the 1980s that some psychological processes occur as modules, discretely packaged entities. He proposes a threetier organization: (i) a transducer process that transforms environmental signals into a form usable by “the system,” (ii) an input process that recognizes and organizes those transformed signals in different formats or “modes,” and (iii) higher-level cognitive functions performed on those inputs. Modules are defined by several particular characteristics, including automaticity and being encapsulated: they are separate from one another and from the third level. Conscious operations across modules are defined as possible only at the third level, where “the system” makes decisions and acts on the basis of all the domains of information from the input structures to which it has access. Only at the third level is there conscious management and the possibility of modules “interfacing” or “talking to one another.” Fodor’s formulation offers one way of making sense of the U-shaped growth curves described earlier. Consider again the temperature and sweetness examples used above. The system may have a representation of the taste of sweetness and the feeling of temperature, as well as spatial representations of volume and a mathematical representation of ratio. These must be consciously coordinated to generate a correct prediction to an intensivity problem. There are other complexities around the notion of modularity. Firstly, Fodor’s expression of modularity is known as “modest,” in contrast to more recent suggestions by evolutionary psychologists of “massive” modularity, with the proposal that all modules, including higher-level conscious operations, are function specific. This “massive modularity” hypothesis will not be discussed further here. More important is the recognition that those who argue for Fodor-style modules are adopting a nativist position that assumes developmentally predetermined modules. The nativist views of modularity described above are radically and fundamentally challenged by a developmental perspective that
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insists on a greater attention to the role of epigenesis in cognition.
Modularization as an Epigenetic Process Epigenetics is a growing field of study, stimulated by recent knowledge about the human genome. Exploration of the environmental variables associated with the expression of very specific genetic factors is made possible, genes being assumed to offer a potential that is realized in a dynamic union with environmental experiences. These kinds of enquiry challenge the paradigm of genetic determinism. They have their counterpart in neurodevelopmental studies that explore the cognitive correlates of genetically linked conditions having an impact on intellectual development (e.g., Williams syndrome and autism). The conclusion, as expounded by Karmiloff-Smith, is that modularization is a progressive process that can only be fully understood from a perspective that assumes very significant developmental plasticity. Neuroconstructivists point to evidence that some forms of environmental interaction over the time of a learner’s development give rise to specializations in processing in particular domains.
Developmental and MicroDevelopmental Change Karmiloff-Smith’s position is to expect uneven cognitive profiles, consistent with modularity and domain specificity rather than the nativist assumptions of unitary capacity. Environmental interaction over the time of a learner’s development gives rise to specializations in processing; genes offer a potential, realized in dynamic conjunction with experiences during ontogenesis, resulting in modularization. Microdevelopmental conceptual change follows a pattern involving three recurrent phases. In the first phase, the system’s focus is on data arising from interaction with the environment and this phase persists until behavioral mastery. The second phase describes the internal dynamics of the system. In the third phase, with internal
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mental computations more stable, what Karmiloff describes as a process of “representational redescription” (RR) occurs. That is, the system reflects on what it knows as a new representation emerges. An interesting contribution from brain imaging technology adds an empirical perspective to the narrative. Functional magnetic resonance imaging (fMRI), combined with a computational model, has been used to link neural activity (blood oxygen flow) to subjects’ thinking about word-image instances of concrete nouns. The resulting “brain pictures” manifest characteristic patterns linked to specific word-images, distributed across three to five locations in the brain. Factor analysis of the outputs gave rise to three main semantic factors for physical objects that were reified as “shelter,” “manipulation,” “eating,” and a fourth factor, “word length.” Furthermore, similarities in the patterns for the same words generated by different subjects were identified as similar “brain pictures” across the sample. There may thus be some support for a form of localization of function in this work.
Review and Reflection Figure 1 suggests a summary of the discussion above. J.J. Gibson used the term “affordances” to describe action possibilities latent in the environment, constrained by the limits of an agent’s repertoire of possible activities: this formulation applies equally to humans, to other animals, and to computers as intelligent machines. Affordances are regarded as latent in the environment and measurable; brain-body-environment permutations afford and limit possibilities for activity. An adaptive and evolutionary perspective suggests that the sense organs must be integral with perceptual systems to allow sense making, whereby values, possibilities, and meaning (e.g., “looks good to eat”) are directly perceived. Gibson’s formulation seems not inconsistent with the notion of modularity. There are challenges to understanding in the processes indicated in Fig. 1, particularly
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1 EXTERNAL A range of energy forms in the environment is available for transfer from objects and events. Environmental affordances link environment, brain and body.
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2
3 INTERNAL PRE-CONSCIOUS
Energy from external sources impinges on sense receptors in human physical systems.
Brain inputs from receptors are translated into cognitively useable form, with modularisation and pre-conscious processing
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5 EXTERNAL CONSCIOUS
Mental activities are involved in meaning creation (‘internal representations’).
Meanings are articulated using a range of formats, communicated externally to the environment as notations (‘external representations’)
Multimodal Representations and Science Learning, Fig. 1 Processes in the formation of representations
between categories 3 and 4, the step across from neural physical correlates to mental events. Computer scientists might describe the process as analogous to binary code being translated to ASCII format. Neuroscientists point to basic neural processes including the growth of protrusions in the dendrites that carry the estimated one hundred thousand billion synapses and the transfer of proteins that, it is believed, play a critical mediating role in learning. Roger Penrose offers a quite different speculation that the waveparticle duality of quantum theory could have value in explaining consciousness, challenging the classical physicalism of reductionists and computationalists. The inescapable fact for science educators is that, however much is learned about physical brain processes, there is a chasm in our understanding of the link between the physical and mental events in the brain. The physical and mental descriptions are explanatory categories of two different orders that do not translate one to the other. It is in categories 4 and 5 of Fig. 1 that productive research and development in science education becomes a realistic enterprise.
Implications for Research, Development, and Pedagogy Cautions: Some theoretical formulations are inconsistent with the arguments as set out
above. For example, the theory of multiple but distinct intelligences, suggesting as it does that individual learners have different qualities of mind, is not consistent with the interpretations of multimodal representations discussed here. In its favor, the theory of multiple intelligences redresses the exaggerated weighting that education has traditionally bestowed upon linguistic modes. While the proposition that learners may favor different modalities at different times and in different circumstances is reasonable, to label some students as limited to particular qualities of learning as learning style adherents have advocated could be doing students a great disservice. Multimodality suggests that instruction should sample a rich variety of representational modes to offer all students. To limit individual learners to adopting constrained modes of learning is unacceptable once we accept that all learners with intact nervous systems have access to the same range of multimodality. Individual students might favor particular expressive modes, but these are more likely to be attributable to their sociocultural histories than inherent limitations. Modularization and culture: Modularization of thinking implies that certain domains are likely to become prevalent in an individual’s thinking, operate faster, and perhaps to some extent become more automatic. Students’ habitual modes of thinking in modern societies are likely to be different from former times when the ubiquity of text, literacy, and quantification of most
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aspects of our current world and our experience of it were absent. In recent decades, the advent of digital media has been revolutionary for mental life and may account for the ontogenetic emergence of a changed quality in the nature of intelligence. This is one possible interpretation of a fall over a 30-year period in the level of intellectual functioning on a Piagetian task as measured by Michael Shayer and colleagues. Certainly, many twenty-first century students have access and are exposed to a range of multimedia digital representations beyond the imaginations of earlier generations. Such students are likely to be highly motivated by opportunities to express their understanding in ways familiar from the converging media of broadcast television and the Internet. Another perspective on a possible changing quality of mental life is that other activity-initiated representational possibilities might be closed down by increasingly sedentary lifestyles. Some evolutionists suggest that a critical period of motor activity is characteristically essential in mammalian development but is being missed or neglected in favor of digital lifestyle options. On the positive side, science educators are constantly confronted with problems as to how to represent inaccessible concepts that might be rarely occurring, dangerous, or at the extremes of scale. Modern multimedia enriches the range of representational possibilities and needs to be exploited in such circumstances. Translation and triangulation of multimodal representations: A defining feature of external representations is that any single form can only ever approximate to that which is represented: some features will map propitiously, others less well. Metacognition is acknowledged by educationalists as important in meaningful learning, and the generative meaning-creation behavior required in category 4 requires hard brainwork. The meanings carried by representations cannot be assumed to be self-evident or made sense of by rote. Whether called “representational redescription” or “metarepresentation,” the internal generative experience is one of the conscious explicitations, an act of fixing meaning and belief from inchoate to developed form.
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Cognitive activity fills in the gaps as part of the sense-making process between subliminal and conscious mental operations. Thoughts may need to be articulated externally in diagrams, speech, writing, or some other form in order to be fully realized, but often, the expression does not emerge as intended. While verbal learning is a common shortcut, it may lack the underlying domain-specific firsthand experience of meaning making that is accessed via direct experience. There are occasions when it is important for science educators to bear in mind that multimodality implies very much more than different wrappings for the identical informational package. Assessment is one such context. Laboratory-based activities in science are highly valued but relatively expensive in management, time, and resources. It would be a cost-effective proposition to dilute laboratory and practical experience with other forms of thoughtful representational activity, both in teaching and in assessment. Such pragmatic management decisions should not be dismissed out of hand on the grounds of nonequivalence. The selected modality makes a difference, and switching modalities, for whatever reason, requires careful empirical consideration of the effects of such changes. For example, there do not seem to be any grounds for categorical assertions on either side that practical work in science can or cannot be assessed by other than practical means. Drawing on representations from different modalities is intended to facilitate sense making by triangulating, and thus reinforcing, understanding. Students can be encouraged to reflect on the value of different versions or formats, critically scrutinizing the mapping between them. This critical activity is advocated as an explicit, external, and self-aware metacognitive activity. It is recommended as a strategy for accessing the conventional formats used in science teaching, giving students a more nuanced consideration of what is “real.” This case-by-case approach to standard representations supports thinking and mediates learning. Since meaning making is such a personal activity, handing over
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ownership to students, rather than attempting to impose a rigid representational orthodoxy, offers them the opportunity to take creative responsibility for their own learning. Another pedagogical perspective takes a broader view, from individual cases to the strategic place of representations per se in scientific endeavor. The argument here is that students can be encouraged to consider, from an overarching perspective, the role and value of representational forms and the purpose they serve as used by scientists. This broader perspective, labeled “metarepresentational competence” (MRC), is important in adding a reflexive dimension which helps to bring students’ own awareness of their science learning procedurally closer to the way professional scientists operate.
Cross-References ▶ Constructivism ▶ Neuroscience and Learning ▶ Scientific Visualizations ▶ Visualization and the Learning of Science
References Coffield F, Mosely D, Hall E, Ecclestone K (2004) Should we be using learning styles? What research has to say to practice. Learning & Skills Research Centre. 1540/ 05/04/500 ISBN 1 85338 914 5. http://itslifejimbutnotasweknowit.org.uk/files/LSRC_LearningStyles.pdf. Accessed 6 Nov 2013 diSessa A (2004) Metarepresentation: native competence and targets for instruction. Cogn Instr 22(3):293–331 Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston Just MA, Cherkassky VL, Aryal S, Mitchell TM (2010) A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS ONE 5(1):e8622. doi: 10.1371/journal. pone.0008622 Karmiloff-Smith A (2012) Is development domain specific or domain general? A third alternative. In: Carver SM, Shrager J (eds) The journey from child to scientist. Integrating cognitive development and the education sciences. APA Books, Washington, DC, pp 127–141 Shayer M, Ginsburg D, Coe R (2007) Thirty years on – a large anti-Flynn effect? The Piagetian test volume & heaviness norms, 1975–2003. Br J Educ Psychol 77:25–41
Multiple Intelligences
Multiple Intelligences Rebecca Cooper Faculty of Education, Monash University, Clayton, VIC, Australia
Multiple Intelligences The theory of multiple intelligences is predominantly attributed to the work of noted psychologist Howard Gardiner during the late 1970s and early 1980s. Gardner conceived intelligence as having multiple rather than singular forms and developed a set of criteria for identifying unique intelligences, which included: • It should be seen in relative isolation in prodigies, autistic savants, stroke victims, or other exceptional populations. • It should have a distinct neural representation, such that its neural structure and functioning should be distinguishable from other human faculties. • It should have a distinct developmental trajectory; different intelligences should develop at different rates along distinctive paths. • It should have some basis in evolutionary biology. • It should be susceptible to capture in symbol systems. • It should be supported by evidence from psychometric tests of intelligence. • It should be distinguishable from other intelligences through experimental psychological testing. • There should be identifiable mental processes that handle information related to each intelligence (Davis et al. 2011). Gardner initially identified seven intelligences that he developed as a theory of multiple intelligences and later, using the above criteria, Gardner added an eighth intelligence as described in the table below (Table 1). Given Gardner’s understanding of intelligence of being pluralistic, it is not surprising that his
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Multiple Intelligences, Table 1 Description of eight multiple intelligences
Multiple Intelligences in the Classroom
Intelligence Linguistic
The educational implications of the theory of multiple intelligences has been summed up by Gardner, “an educator convinced of the relevance of multiple intelligence theory should ‘individualize’ and ‘pluralize.’” By “individualize,” Gardner means that an educator should know the intelligence profile of each student in their class so well that they are able to make adjustments to the teaching and assessment that are used. By “pluralize” Gardner means giving consideration to the topics or concepts that are most important and ensuring that they are presented through multiple modes of delivery, while at the same time highlighting the importance of the multiple modes of delivery as a way of considering what it means to understand something well.
Logicalmathematical Spatial
Musical
Bodilykinesthetic Interpersonal
Intrapersonal
Naturalistic
Description An ability to analyze information and create products involving oral and written language such as speeches, books, and memos An ability to develop equations and proofs, make calculations, and solve abstract problems An ability to recognize and manipulate large-scale and fine-grained spatial images An ability to produce, remember, and make meaning of different patterns of sound An ability to use one’s body to create products or solve problems An ability to recognize and understand other people’s moods, desires, motivations, and intentions An ability to recognize and understand his or her own moods, desires, motivations, and intentions An ability to identify and distinguish among different types of plants, animals, and weather formations that are found in the natural world
(Gardner 1993, 2011; Davis et al. 2011)
theory of multiple intelligences included the notion that each individual has a profile that includes all eight intelligences at various levels of strength and weakness. However it is incorrect to say that an individual would demonstrate no ability in a particular intelligence or that everyone would be classed as gifted in at least one intelligence. In fact Gardner makes only two primary claims: • All individuals possess the full range of intelligences. • No two individuals, not even identical twins, exhibit precisely the same profile of intellectual strengths and weaknesses (Davis et al. 2011, p. 492). Gardner has continued to research his theory of multiple intelligences and believes he has “suggestive evidence. . . for a possible existential intelligence (“The intelligence of big questions”)” (Gardner 2011, p. xiv).
Cross-References ▶ Constructivism: Critiques ▶ Didactical Contract and the Teaching and Learning of Science ▶ Representations in Science
References Davis K, Christodoulou J, Seider S, Gardner H (2011) Multiple intelligences theory. In: Sternberg R, Kaufman S (eds) Cambridge handbook of intelligence. Cambridge University Press, Cambridge, pp 485–504 Gardner H (1993) Frames of mind: the theory of multiple intelligences, 2nd edn. Fontana Press, Great Britain Gardner H (2011) Multiple intelligences: the first thirty years. In: Frames of mind: the theory of multiple intelligences, 30th anniversary ed. Basic Books, New York, pp ix–xxvi
Museum Learning ▶ Technology for Informal and Out-of-School Learning of Science
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Museums Paulette M. McManus Institute of Education, London University, Mere, Wiltshire, UK
In science museums you may find iconic artifacts like parts of Watson and Crick’s original model of DNA; significant objects like fossils; a series of objects, artifacts, or models illustrating a developmental process or concept; dioramas of all sorts; singular chronological displays; taxonomic interpretations of the natural world; classification displays ranging from minerals to jet engines; explanatory graphics and computer games; and so on. Permanent exhibitions provide well-researched, detailed communications rather than accounts of the latest discoveries which may well be presented in temporary exhibitions. Museums are saturated with objects and the words interpreting them. The eclectic mix is part of their charm. All museums are, like many scientific activities, expressions of fundamental human cognition patterns and psychological behaviors. Think about curiosity, delight in novelty and the unique, linguistic pleasure in naming and categorizing, love of collecting, delight in observation and measuring, brain and hand-eye coordination allowing the making of incredibly fiddly things, spatial intelligence prompting us to be imaginers of monumental manufacture, appreciation of the natural world, ties to home territories, and respect for the well-made object, or well thought out theory, which leads to aesthetic appreciation. Museums are cultural institutions reflecting varied expressions of natural tendencies.
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replaced in a London national museum). Because exhibitions change at such an exceptionally slow pace compared to other forms of modern media, they are seen as an attractive, easy area for research and commentary by academics from emerging or developing fields such as media studies, cultural studies, anthropology, sociology, ethnology, political policy, development studies, and space syntax, along with other computingbased applications. Therefore, much of the critical writing on museums is written from a singular, academic point of view and fails to take account of museum histories, their conservative nature, the time scales to which they work, their restrictive budgets, their concrete communicative style, and their public popularity (McManus 2011). Critical views have also been expressed by science museum curators who question the impression of the inevitable progress of science inherent in chronologically ordered displays. Within the museum profession there is discussion about communicating the social implications of technological developments, the problematic nature of science as a cultural activity, and how communications can be biased by the social and academic backgrounds of museum people. There is also a vibrant field of museological writing devoted to museum communication, audience studies, evaluation methodologies, and museum management, in which a fashionable tendency to adopt learning theories long after they have been abandoned by formal educationalists is evident. Current relativist interest in learning style theory and constructivism is seen by some as an excuse for inadequate conceptualization and preparation of communications for a general audience.
Museums in Their Historical Contexts Communication Research in Museums Museum exhibitions are a form of 3D media which people walk through in a public, comfortable, and usually publicly funded space. They generally have a long life, maybe up to 30 years or more (recently a 100-year-old exhibition was
In all cultures, there have probably always been treasuries of objects with religious and kingly significance. Museum institutions are European products of the seventeenth and eighteenth centuries of the Age of Enlightenment. They have spread around the world as a result of
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colonization, twentieth century globalization, and, recently, as a result of development policies and the rise of mass tourism destinations. Collections are the foundation of museums and the exhibitions about them may seem timeless. As a result, museum presentations vary according to their institutional histories, so large national science museums often offer a palimpsest of past scientific and technological preoccupations (McManus 1992). The ancestral form of the museum is the Cabinet of Curiosities created during the Enlightenment in small rooms and galleries in the houses of wealthy men. Here they could show and discuss with their friends interesting rarities, specimens, ethnographic materials, scientific instruments, coins, and antiquities. Older public museums are derived from subject matter breakdowns of the Cabinet of Curiosities collections. For example, the Ashmolean Museum, Oxford, Britain’s first public museum, was founded in 1683 from the collections of John Tradescant, while the British Museum, London, was founded in 1753 from the collections of Hans Sloane. The “consumer characteristics” of such museums were, and sometimes still are, object saturation and authoritatively delivered information. In them, the curators privately researched the collections in tenured academic style, while the public activity of education and interpretation was undertaken by hired guides, educators, or designers. This historic split in status is mirrored in the institutional culture of many museums today. In the 1960s and 1970s, there was an increasing desire to alter the focus of displays in the older science museums from a taxonomic display of objects to the presentation of explanations of interdisciplinary scientific ideas and concepts such as evolution, ecology, and atomic power. Gradually, a new approach to the visiting public, often based on mainstream educational theory, produced exhibitions with carefully structured information and engaging displays with which people could interact. Under these developments the educational function of museums came to the fore, and new museum professions came to include evaluators and researchers into learning
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in informal museum environments. The curator became subject matter expert rather than the initiator of exhibitions. In the early nineteenth century, collectionsbased museums, discussed above, were slowly joined by science and technology museums concerned with training, the world of work, and scientific advance. Originally these were established to meet the practical needs of industry. For example, the V&A, London, was founded to promote design training. Mid-century, such pragmatic objectives, were rapidly overtaken by influences from a spate of popular trade exhibitions and world fairs. The fusion of the training-orientated, serious technology museum and the popular industrial exhibition with its beam engines and willingness to allow enjoyment gave rise to our familiar science and technology museums. Around the mid-nineteenth century, there was also a rise in the foundation of provincial public museums which sought to provide sources of liberal education in an age without compulsory schooling. From the mid-nineteenth century up to today, the museum audience in general has become less academic or educated middle class and more general in character. However, it remains the case that an individual’s level of education is the strongest indicator of the likelihood of museum attendance. In recent times, museums devoted to the transmission of scientific ideas and concepts, rather than the building of collections and scholarly research into them, have arisen. Their primary objective is public education and they are often offshoots from educational institutions. Such museums tend to present thematic exhibitions of current contemporary significance and to contain interactive exhibits. Nowadays, themes could include heredity, sustainability, global warming, and so on. Examples of such museums are Palais de la Decouverte, Paris (1937), New York Hall of Science (1964), Lawrence Hall of Science (1968), and the Exploratorium, San Francisco (1969). Science centers could be said to be decontextualized scatterings of interactive devices first pioneered in this range of modern public education museums.
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Differences Between Formal and Informal Education Museums are prime examples of informal educational environments, so it is important for museum people to conceptualize a philosophy of informal education and to avoid mimicry of the attitudes, teaching style, and methods of schools and colleges. In formal educational situations, where you will learn, who you will learn with, whether you are qualified to learn, who you will learn from, what you will learn, how long you will be given to learn it, and agreement on what you have learned and your level of understanding are matters largely out of control of the individual learner. As a result of these restrictions on the individual, formal institutions are very efficient, admirable means of communicating knowledge throughout societies for the benefit of those societies and the individuals within them. Formal education has developed a pedagogy which, some say, applies to a somewhat restricted range of human learning mechanisms and behaviors. Distinctive forms of evaluation have been developed to assess learning under these enhanced, prescribed conditions. Most museums have an educational remit built into their trustee documents, but efforts to measure learning in the museum environment using methods from the formal education sector have failed. Here lies a fundamental difference between the two sources of knowledge and understanding. Informal education is entirely free choice in every way and is largely a leisure activity. As far as museums are concerned, people can choose to visit when they feel like it and age or level of experience are not barriers – all are welcome. Museum visitors can attend to exhibits within exhibitions for as long or short a time as they wish, or they can walk straight past them. Since museum visitors arrive with widely different levels of understanding, personal expectations, and differing social contexts (families, friends, or alone), museum professionals must constantly deal with multiple audiences for their communications. Accordingly, useful museum evaluations are concerned with investigations into conditions which would
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support learning in the motivated enquiries about what sense people make of their experiences, and descriptions of visitor behavior (which can be quite consistent across all museum types). In recent years, public-facing museums have come to understand the differing segments of their increasingly well-educated audiences. Science learning is strongly supported in science museums because most museum evaluation methodologies have been developed in them. Other sources of informal education include television and radio science programs, science sections in newspapers and magazines, botanical gardens, zoos, nature reserves, archeological sites, and historic houses. The intention to inform during “worthwhile” leisure time unites them all.
Out-of-School Museum Impacts All types of museum collection can be splendid, rich places for anyone to find out about scientific concepts, processes, and technical applications. However, museums vary and are often very individualistic institutions. Those wishing to use them to teach out of school may have to adopt a lateral thinking approach to tailoring communications and really get to know their local museum. For example, at the Institute of Education, London, beginning mathematics teachers are taken to visit the nearby British Museum to work with Sumerian clay calendar tablets. At another museum, an education section may offer an “off the shelf program” which will just suit curricular needs. Some museums will offer educators Webbased materials, but because the essence of museum use is witness of primary evidence and the sight of “real things”, visits are best, if possible.. A well-planned school visit to any museum will involve a teacher who knows the institution they wish to visit and will not panic about losing control when out of classroom, preparation teaching about the planned experience, quick settling down of excited children in a novel environment, and the allowance of lots of free-choice activity after the planned topic has been dealt with along with follow-up at
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school afterward. It is important that the informal educational dimensions of museums are catered for. It is known that children may pester their parents for a return visit to the museum after a school visit. This is especially if they have been taken out of their normal, everyday environment so that they have glimpses of a bigger, more varied social and natural world and have become curious about it. Such visits can leave people with long-lasting visual and episodic memories. Such powerful impacts are reasonable typical outcomes of visits to intentionally educational environments open to the general public (McManus 1994).
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▶ Interactive Science Centers ▶ Learning Science in Informal Contexts ▶ Science Exhibits ▶ Visitor Studies
References McManus PM (1992) Topics in museums and science education. Stud Sci Educ 20:157–182 McManus PM (1994) Memories as indicators of the impact of museum visits. Mus Manag Curatorship 12(4):367–380 McManus PM (2011) Invoking the muse: the purposes and processes of communicative action in museums. In: Fritsch J (ed) Museum and gallery interpretation and material culture. Routledge, New York/Oxon, pp 26–34
Cross-References ▶ Excursions ▶ Explainer
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