Apr 3, 2014 - college and 21st century careers (PARCC, 2010, p. 35). In 2011, member states of ...... Two Pearson Correlation tests were performed, one for ...
HIGH SCHOOL STUDENTS’ COLLEGE CAREER READINESS SKILLS IN VIRTUAL AND FACE-TO-FACE ADVANCED PLACEMENT COURSE SETTINGS
A dissertation submitted by Patricia Murthy
Submitted in partial fulfillment of the requirements for the degree of Doctor of Education at Dowling College, School of Education, Department of Educational Administration, Leadership and Technology
Dowling College Oakdale, NY 2015
ProQuest Number: 3664074
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The dissertation submitted by Patricia Murthv for the degree o f Doctor o f Education is approved
m Sa-Sofiafeerfe, Ed.D. Chair
jshalhPeffy, Ph.D. Design Specialist
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Thomas K. Short, Ed.D, External Reader
Stephanie Tatum, Ph.D. Reader
y ] j mu J / y i „ JA/Qrjuj MiphSpl Shane Highera,uEd.iy Reader
Dowling College Oakdale, NY 2015
ABSTRACT
The purpose of this study was to examine college and career readiness (CCR) skills of High School students in both traditional face-to-face and virtual Advanced Placement course settings. Given the current expectation of academic rigor combined with increased funding for virtual learning opportunities, a study such as this is both relevant and necessary. The variables of self-regulation, cognitive presence, socially active learning, technology beliefs and fear of failure were measured. The participants, 134 AP students in a Long Island High School, responded to an online survey comprised of questions about CCR skills and technology beliefs. The instrument was a 5-point Likert scale based on previously juried instruments: Self-Regulation Questionnaire of Brown et al. (1999); Community of Inquiry (Col) framework of Arbaugh et al. (2008); and Participatory survey items based on the work of Jenkins (2006) and Rovai (2002). Descriptive analysis of the CCR variables revealed HS students agreed less with technology beliefs and more with Self-Regulation and Fear of Failure. Differences in CCR skills in each learning setting revealed students had higher prior GPA in the face-toface AP course setting and higher Fear of Failure in VAP course setting. Several relationships exist between self-regulation, cognitive presence, social presence, participatory learning and technology beliefs in each setting. A Logistic Regression Analysis predicted students taking traditional face-to-face and virtual AP courses in the variables Cognitive Presence and Fear of Failure. Also, some gender differences were found for prior GPA and Course Grade in both settings. A summary of students’ openended comments is included as well.
DEDICATION In writing this document I have perused various dedications and a question from one of Dr. Manley’s classes kept coming to mind: what is the most important value you have? Many in the class answered their children (I have four incredible ones to value), some answered their soul mate (mine is a gift of matchless value) and some their faith (which I hold above all else). I answered last and said it was my freedom that I value most. It is to my freedom that I dedicate this paper, without which I could not be the wife, mother, scholar, educator, thinker, creator, or believer that I am. On this day, at the writing of these words, America embraces such freedom, referring to it as “God-given” and “inalienable” (Declaration of Independence, July 4,1776). The future is uncertain. Today the Bible itself is illegal in 52 countries and to declare Jesus Christ as Lord could cost you your life in places near and far. I do not take for granted the freedom I have had to learn, write and read what I choose. I am free to worship the Living God and to study the Living Word. So, to Raj, Josh, KK, Hannah and Walker: the Murthy’s- we are free to be who we are- deep, and rich and ceaseless.
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ACKNOWLEDGEMENTS This truly has been an experience of a lifetime and I value each and every person whose paths I crossed during this journey: professors, mentors, cohort members (“Up” and others), conference presenters, cafeteria and custodial staff, security, tech support and the guys who waved me in at the gate each weekend. You all blend together, (though at times like a cacophony), into an indelible and precious memory. Dr. Morote, your ears must ring at the many times I speak of your brilliance, creativity and just plain Peruvian coolness. I get the feeling you are the kind of person who intuitively knows when someone is eternally grateful: I am. Dr. Perry, the day I was introduced to you, I knew I would glean so much from you. I thank you for your endless patience, kindness and encouragement. Dr. Tatum, you are a class act. You are impeccable and immovable from your high expectations, consistent with the first time I met you 12 years ago as a new professor of Educational Research. You have been such an exemplary model of excellence. Dr. Short, my cyber-angel, you were sent from the virtual heavens above to inspire me, cajole me and keep me moving. Each time you said that I would make it gave me a spurt of energy. And, I loved your data! I also need to thank and acknowledge Dr. Jared Bloom and Mr. James Corcoran for their benevolent assistance. These two educators helped me solely as a fellow educator, not knowing me or gaining anything- you are true professionals.
Finally to Saundra Simonee, a scholar I never knew, and whose life was cut short just after completing this program. It was a reminder to me that the papers of humanity are perishable but the Spirit of a quickened soul is not. Rest in peace.
TABLE OF CONTENTS
ABSTRACT.............................................................................. DEDICATION.........................................................................................................................iv ACKNOWLEDGEMENTS..................................................................................................... v TABLE OF CONTENTS.......................................................................................................vii LIST OF TABLES................................................................................................................. xii LIST OF FIGURES.............................................................................................................. xiv CHAPTER 1...............................................................................................................................1 Introduction................................................................................................................... 1 Background of the Study.............................................................................................7 Purpose of the Study.................................................................................................... 7 Problem Statement....................................................................................................... 8 Research Questions...................................................................................................... 9 Research question one.....................................................................................9 Research question tw o.....................................................................................9 Research question three...................................................................................9 Research question fo u r ..................................................................................10 Research question fiv e ...................................................................................10 Research question six.....................................................................................10 Definition of Major Variables and Terms.................................................................10
Virtual Learning............................................................................................. 10 Advanced Placement Course........................................................................ 10 Face-to-Face (F2F) Learning....................................................................... 11 College and Career Readiness (CCR)..........................................................11 Self-Regulation............................................................................................... 11 Participatory Learning..................................................................................11 Fear o f Failure............................................................................................... 12 Web 2 .0 ...........................................................................................................12 Presence..........................................................................................................12 Cognitive Presence........................................................................................ 12 Social Presence.............................................................................................. 13 Motivation.......................................................................................................13 Millennial Sub-Group Generation Z.............................................................13 Setting 13 Conceptual Rationale................................................................................................. 13 Summary..................................................................................................................... 19 Significance of the Study...........................................................................................20 Limitations and Assumptions................................................................................... 21 CHAPTER II........................................................................................................................... 22 Introduction.................................................................................................................22 Self-Regulation............................................................................................... 23
Cognitive and Social Presence.....................................................................29 Participatory Learning................................................................................. 37 College and Career Readiness...................................................................... 42 Fear o f Failure............................................................................................... 43 Technology Beliefs o f Millennials.................................................................46 Summary.....................................................................................................................48 CHAPTER III......................................................................................................................... 49 Introduction.................................................................................................................49 Original Research Questions..................................................................................... 49 Research Question One..................................................................................50 Research Question Two.................................................................................50 Research Question Three.............................................................................. 50 Research Question Four............................................................................... 50 Research Question Five.................................................................................50 Research Question S ix...................................................................................51 Selection of Setting and Subjects.............................................................................. 51 Design and Methodology...........................................................................................51 Survey Instrumentation.............................................................................................. 52 V alidity....................................................................................................................... 53 Content............................................................................................................53 Construct.........................................................................................................53
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Reliability....................................................................................................................57 Data Gathering Procedure..........................................................................................57 Data Analysis............................................................................................................. 58 Refined Research Questions......................................................................................58 Research Question One................................................................................. 58 Research Question Two................................................................................ 58 Research Question Three.............................................................................. 59 Research Question Four............................................................................... 59 Research Question Five................................................................................ 59 Research Question S ix...................................................................................59 Limitations..................................................................................................................60 CHAPTER IV ......................................................................................................................... 61 Introduction.................................................................................................................61 Research Questions.................................................................................................... 64 Research Question One..................................................................................64 Research Question Two.................................................................................70 Research Question Three...............................................................................71 Research Question Four................................................................................75 Research Question Five.................................................................................78 Research Question S ix ...................................................................................79 CHAPTER V .......................................................................................................................... 83
INTRODUCTION..................................................................................................... 83 SUMMARY AND CONCLUSIONS.......................................................................84 Research Question One..................................................................................84 Research Question Two................................................................................ 88 Research Question Three.............................................................................. 90 Research Question Four................................................................................93 Research Question Five.................................................................................94 Research Question S ix...................................................................................96 Conclusions.................................................................................................................98 Recommendations...................................................................................................... 99 APPENDIX A ........................................................................................................................112 INFORMED CONSENT STATEMENT....................................................... 112
Survey Instrument: VAP Study........................................................................114
LIST OF TABLES Table 3. 1 Original Survey Dimensions, Items and Score Range..................................... 52 Table 3. 2 Deleted Items from Survey Instrument............................................................. 54 Table 3. 3 Factor Loading - Self-Regulation......................................................................54 Table 3. 4 Facto Loading Cognitive Presence....................................................................55 Table 3. 5
Factor Loading Socially Active Learning........................................................ 55
Table 3. 6
Factor Loading Technology Beliefs.................................................................56
Table 3. 7 Factor Loading Fear of Failure......................................................................... 56 Table 3. 8 Raw Score Range/Scale Reliability after Factor Anlysis................................ 57 Table 4. 1 Distribution by Particpant’s Grade Levels....................................................... 62 Table 4. 2 Distribution by Particpant’s Gender.................................................................63 Table 4. 3 Advanced Placement Course Setting................................................................ 63 Table 4. 4 Participant’s GPA Before AP Course............................................................... 64 Table 4. 5 Desriptives for Particpant’s n Each CCR Variable.......................................... 65 Table 4. 6
Item
Analysis for Self-Regulation............................................................66
Table 4. 7
Item
Analysis for Cognitive Presence..................................................... 67
Table 4. 8
Item
Analysis for Socially Active Learning............................................68
Table 4. 9
Item
Analysis for Technology Beliefs..................................................... 69
Table 4. 10 Item
Analysis for Fear of Failure..............................................................70
Table 4. 11 Independent Samples t test Comparing CCR Skills in F2F and VAP............71 Table 4. 12 Correlation Among Variables F 2 F ................................................................... 73 Table 4. 13 Correlation Among Variables VAP...................................................................75 Table 4. 14 Omnibus Test of Model Coefficients................................................................ 76
Table 4.15 Model Summary.................................................................................................. 76 Table 4.16 Coefficients Table................................................................................................77 Table 4.17 Variables in the Equation.................................................................................... 77 Table 4.18 Number of Particpants’ Completion of Open-Ended Survey Question
78
Table 4.19 Key Word/Theme Responses Open Ended Survey Question...........................80 Table 4.20 2X2 ANOVA Between Groups Comparing GPA by Gender and Setting......80
Table 4.21 Estimated Marginal Means for Prior G PA ........................................................ 81 Table 4.22 2X2ANOVA Between Groups Comparing Course Grade by Gender/Settin. 81
xiv
LIST OF FIGURES Figure 1
Community of Inquiry Diagram......................................................................... 16
Figure 2
International Lab Network Framework Diagram.............................................. 18
Figure 3
International Lab Network Definitional Elements of Framework ..................19
CHAPTER I INTRODUCTION Introduction In the United States, preparing K-12 students to be college and career ready has not always been a focus of the education system. It was throughout the 20th century that a shift began, from industries that were primarily production occupations, like farmers and foresters, to those of professional, technical, and service occupations (Fisk, 2003). That century catapulted into the 21st century technological environment, and was posed with a new set of complex needs. Learning skills never before considered became a focus o f public education’s need to contend in a competitive global society. Currently, students who are college and career ready must place content and skills on a firm foundation of technology application and understanding. It’s a whole new world: both flat and open. According to the book, The World is Flat by Thomas Friedman, flattening will result in ‘running faster to stay in place’ because technology has created a smaller, faster world (Friedman, 2009). The implication of openness, according to Curtis Bonk in his book, The World is Open (2009) is that technological development and the Internet “opened up learning to the point where anyone can learn anything from anyone else at any time” (p. 7). Many say public schools today are broken, in that they are unable to equip students to meet the new 21st century demands. However, Sugatra Mitra, a professor and researcher of technology education, pointedly said in his award winning TED Talk
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(2013), “schools of today are not broken, but obsolete.” He asserts that although we may not know what future jobs will look like, we do know “that people will work from wherever they want, whenever they want, in whatever way they want,” (TED Talk, 3:42) because this new work landscape is immersed in technology. In 2007, The National Center on Education and the Economy (NCEE) reported that, “The core problem is that our education and training systems were built for another era. We can get where we must go only by changing the system itself’ (p. 7). The shift in skills from farmer to financer developed over the previous century is no comparison for the lightning speed demands of the 21st century in the past ten years. K12 public schools are now preparing students for a future that is nearly inconceivable, and yet traditional public school settings remain the central hub for this learning. The NCEE (2007) underscored the 21st Century American worker’s skill-base: creativity and innovation, facility with the use of ideas and abstractions, the self-discipline and organization needed to manage one’s work and drive it through to a successful conclusion, that is, the ability to function well as a member of a team. Likewise, Forbes.com (n.d.) (2014) published the top 10 skills necessary for career success, including novel terms such as “Cognitive Load Management” (handling of huge amounts of information), “New Media Literacy” (digital fluency) and ’’Social Intelligence” (the ability to connect with people). Also listed “Virtual Collaboration,” a must have skill that is practiced in effective functioning as a leader or member of a virtual environment (Gorsht, 2014). Such skills are to be fostered and developed, starting as young as possible in the training ground called school. Even the Association for Career and Technical Education (ACTE) published a formal definition of career readiness in
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April 2014 to include the employability skills of critical thinking, problem solving, collaboration and technology use. The ACTE also recognizes that today, career-ready core academics and college-ready core academics are essentially the same, creating overlapping in the preparation of students. So then, who are our “college and career ready” graduating High School students? The College Board Standards for College Success (2006) identifies students who have completed an Advanced Placement (AP) course as one such indicator. The historical research of Willingham and Morris (1986) compared college over-all success of AP students to non-AP students for 4 years. A pattern was identified in which matched pairs of students showed those with AP courses had higher academic success in college. Also, it was seen that the AP subject area was a predictor of the areas of college coursework study for those students. Studies have also shown that students with AP courses have positive college outcomes (Mattem et al., 2009; Allen, 1999; Dodd et al., 2008; Preston, 2009). The grand leap of face-to-face learning to distance learning has developed over a few centuries, from the correspondence courses in 18th century Europe to today’s virtual education environments across the globe. Currently, all U.S. public school institutions have e-leaming options for students and virtual schools afford learning opportunities to students in both post-secondary and public K-12 schools (Watson & Kalmon, 2005). It is the rapid growth of online learning in K-12 schools that is notable and considered one of the fastest growing trends in educational uses of technology (U.S. Department of Education, 2010). Close to two million online courses are taken by public school students annually (Queen et al., 2011) and the number of students in full-time online
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schools is four times what it was a decade ago, with enrollment growth from 50,000 to 250,000 in just one year (Watson, Murin, Vashaw, Gemin, & Rapp, 2013). In 20122013, U.S. students in traditional K-12 schools enrolled in nearly 750,000 online courses, many of which were courses typically not offered in the students’ home school such as Mandarin Chinese or AP Physics (Barshay, 2013). In the United States, public education is primarily a state and local responsibility and must ensure equitable access to learning experiences for all students, especially students in typically underserved populations—low-income and minority students, students with disabilities, English language learners, and students in rural areas. Institutional capacity must be built for transformation in meeting all of these needs and technology adds a layer of complexity. Institutional change will need to leverage available technology and take advantage of the various digital and participatory learning that is available to students today (Davidson & Goldberg, 2012). Though the U.S. Department of Education (DOE) has not yet mandated states to include online learning options in K-12 public education in the 2004 National Technology Plan, they predicted an “explosive growth” in online learning across the country. According to Keeping Pace (2013), many states are increasing virtual learning within their K-12 program by requiring virtual course offerings some requiring at least one virtual course before graduation (Loertscher & Koechlin, 2013). Funding through federal initiatives such as Race to the Top (RTTT) has given states an inducement to apply for money targeted for specific programs, particularly technology based initiatives. Currently, virtual K-12 programs vary from state to state and continue to be determined by zip code. Virtual saturation at one extreme is Florida’s Virtual Learning
School (FLVS), a public K-12 program that enrolled nearly half a million students in 2013 and provides a full range of online opportunities across the state (Keeping Pace, 2013). FLVS also provides course access to other states, particularly, rural areas within states seeking AP course offerings. To the other extreme, a state like New York has minimal online learning opportunities. This is despite a resounding response from surveyed NYS educators who said there is a vital need for increased virtual learning opportunities beyond remediation and credit recovery (NYSED Report, 2011). Though the need for increased virtual opportunities is cited, virtual innovation in NY is only beginning. Virtual Advanced Placement Program In 2012, NYSED made an unprecedented move towards creating virtual learning opportunities in the state’s public schools. Under New York State’s new virtual learning initiative, over $17 million in RTTT funds were awarded to districts that would give all students better access to Advanced Placement tests, i.e., the Virtual Advanced Placement (VAP) Program. According to the NYSED website, VAP is “targeted to improve access to online and blended,” to AP courses for low-income students specifically. That is districts that do not offer an AP class on site and considered a low socio-economic status district will be able to offer academically eligible students via this virtual learning option: VAP. O f the total grant funding made available statewide; Long Island districts were awarded approximately $3 million dollars to develop online and blended AP courses to make available to students who otherwise would not have access. This was the largest allocation in NY State, exceeding even large city systems like New York City and
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Buffalo City, who received $1 million and three quarters of a million respectively. For Long Island, the funds were distributed to three individual districts: Lawrence ($259,460), Huntington ($348,763), and South Huntington ($499,956) and an additional 10 districts via a Nassau BOCES consortium ($2,000,000). This is the first formal attempt at initiating virtual learning opportunities within the K-12 public education system in New York State albeit limited. Virtual learning has become an integral component in course offerings at the postsecondary level, and has gained momentum in recent years. Students in classes with an online learning component performed better than those enrolled solely in on-campus classes (U.S. Department of Education, 2010). Trends and practices of the college setting are a pre-cursor to changes in the K-12 public school setting. It is likely then, that college and career readiness requirements for K-12 will include online learning experiences as a basic 21st century expectation for all students (NETP, 2010). A critical analysis of students’ in the VAP program is necessary in order to develop optimal online learning programs in the future. Virtual learning is destined to increase in New York due to a myriad of reasons: money, access, equity, and 21st century demands. The greatest mistake however will be moving such an initiative forward without a true examination of its effectiveness with respect to impact on college and career readiness. In the words of a NYS educator that was included in the final remarks o f the NYSED online learning needs assessment report (2011) it was said: Online learning must not simply re-create the ancient NYS High School Curriculum in the online space or else we will re-create all the problems that exist now and thus will not be better serving those students who cannot be successful in this model. Instead, online learning should be a
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catalyst for CHANGE. Across the board, change is needed to offer flexibility on a local level to meet student needs. We must embrace 21st century modes of learning and break free from a set of course requirements and content that is over 50 years old in NYS! (p. 40) Examination of Advanced Placement students in both a face-to-face and virtual settings is a worthy study. Determining whether student college and career readiness key indicators are influenced in virtual learning settings compared with traditional face-toface will be a valuable finding for future growth and planning. It is expected that key targeted CCR skills, such as self-regulation, cognitive presence, social presence, and participatory learning are more pervasively attained in the virtual learning setting, beyond that of advanced content acquisition only. Background of the Study This research will focus on the educational experience of High School students enrolled in the NYS Virtual Advanced Placement (VAP) program in one Long Island district. Virtual learning settings are unique in obvious ways, however, the impact on a student’s college and career readiness within that learning setting is of special interest given the expectations of the newly adopted and administered Common Core State Standards (CCSS). The student’s self-reported perception of the degree to which self regulation, cognitive and social presence and participatory learning dimensions were impacted through the educational experience of a VAP course will be examined. In order to offer students advanced placement courses, which would otherwise be unavailable to them, districts have opted for virtual learning offerings. Purpose of the Study The purpose of this study was to examine HS Students’ college and career readiness dimensions of self-regulation, cognitive presence, social presence, participatory
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learning behaviors and technology beliefs in both face-to-face and virtual AP course settings. High School students enrolled in Virtual Advanced Placement (VAP) course setting and face-to-face AP course setting were surveyed on perceived college career readiness (CCR) skills for dimensions of self-regulation, cognitive presence, social presence, participatory learning behaviors and technology beliefs. Academic achievement and student demographics (grade level, gender, prior grade point average and course grade) data was also being examined in both learning settings. Problem Statement Virtual learning settings continue to become a viable option for K-12 students across the country, whether for issues of equity, course access, course recovery or advancement. Time and money are being allocated to establish programs, facilitating the virtual learning setting with little understanding of the complexities involved in successful implementation. This study examined the perceived CCR dimensions of self-regulation, cognitive presence, social presence, participatory learning behaviors and technology beliefs in virtual and face-to-face AP course settings. In addition, descriptive information will be analyzed by demographics and predisposed technology beliefs to investigate other relationships that may exist. Two groups will be surveyed, HS students learning in faceto-face and virtual AP course settings. Differences between these two groups were analyzed. Absent from the literature is an examination of whether students in a virtual learning setting are influenced differently with respect to key college and career readiness indicators as compared to face-to-face AP courses. While the literature indicates that
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high-level academic rigor is identified with the Advanced Placement program and that virtual learning opportunities have great educational potential, few of them indicate the influence of these combined with respect to college and career readiness. Research Questions The characteristics (gender, grade level, prior grade point average [GPA] course subject, course grade earned) of HS students in face-to-face, and virtual AP course settings was gathered. Research question one How do High School students describe their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings? Research question two How do High School students differ in their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings according to gender, and prior grade point average [GPA]? Research question three What is the relationship between the CCR dimensions of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs, gender, and prior grade point average [GPA] in traditional face-to-face and virtual AP course settings?
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Research question four How do the CCR dimensions Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs gender, and prior grade point average [GPA] predict High School students taking traditional face-to-face and virtual AP course settings? Research question five How do High School students in traditional face-to-face and virtual AP course settings describe their course experience? Research question six How do High School students’ characteristics of gender and prior grade point average [GPA] compare in traditional face-to-face and virtual AP course settings?
Definition o f Major Variables and Terms Virtual Learning Education in which instruction and content are delivered primarily over the Internet (Watson & Kalmon, 2005). The term does not include printed-based correspondence education, broadcast television or radio, videocassettes, and stand-alone educational software programs that do not have a significant Internet-based instructional component (U.S. Department of Education, 2010). Advanced Placement Course Courses that offer rigorous college-level curricula and assessments to students in high school. (College Board, 2003).
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Face-to-Face (F2F) Learning The delivery of course material that occurred in a brick and mortar environment was one in which the teachers and students were present in the same place and at the same time. For the purpose of this study, face-to-face learning referred to the AP courses taught in the classroom. College and Career Readiness (CCR) Refers to the content knowledge, skills, and habits that students must possess to be successful in postsecondary education or training that leads to a sustaining career. A student who is ready for college and career can qualify for and succeed in entry-level, credit-bearing college courses without the need for remedial or developmental coursework (EPIC, 2012). Self-Regulation “Self-directive process by which learners transform their mental abilities into academic skills (self-regulation of learning involves more than detailed knowledge of a skill;) it involves the self-awareness, self-motivation, and behavioral skill to implement that knowledge appropriately” (Zimmerman, 2002, p. 66). The process whereby students activate and sustain cognitions, behaviors, and affects, which are systematically oriented toward attainment of their goals (Schunk & Zimmerman, 1994, as cited by Boekaerts, 1997, p. 171); this definition is reinforced by Brooks (1997), who argues that it is active and goal directed resulting from self-control of behavior motivation and cognition (cited in Mark McMahon & Joe Luca, 2001). Participatory Learning
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Community members having social interactions with other members shared collaborative projects and content creation within a common subject of study (Jenkins, 2006). A student’s sense of belonging to a learning community including a sense of obligation to other community members (Rovai, 2002). Members of a participatory community feel that they have some degree of social connection and share creativity freely with other members. (Jenkins, 2006). Fear o f Failure Fear of failure can be defined as the use of energy as a motivation to avoid a negative possibility (Elliot, 1999; Elliot & Sheldon, 1997). Fear of failure is directly related to how people define and perceive an academic failure. Web 2.0 “Web 2.0 refers to the current generation of Internet applications that allow users to collaboratively generate their own content” (Oliver, 2010, p. 50). Presence “In an online course, the simplest definition of presence refers to a student’s sense of being in and belonging in a course and the ability to interact with other students and an instructor, although physical contact is not available” (Picciano, 2002, p. 22). Cognitive Presence The extent to which learners are able to construct and confirm meaning through sustained discourse in a critical Community of Inquiry (Col). (Garrison, Anderson, & Archer, 2001). Garrison et al. (2000) defined cognitive presence as the extent to which participants are able to construct meaning through sustained communication, such as reflection or discourse. Garrison et al. (2008) elaborated on the definition of cognitive
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presence by stating that it is the exploration, construction, resolution, and confirmation of understanding through collaboration and reflection in a Col. Social Presence The ability to project oneself as an actual person (one’s full personality) both socially and emotionally in the online environment (Garrison et al., 2000). A measure of the feeling of community that a learner experiences in an online environment Tu & Mclssac, 2002). Motivation Autonomous, self-determination to achieve the learning goal of course completion (Ryan & Deci, 2000). Millennial Sub-Group Generation Z Students who were bom 1995-2012 and will grow up in a highly sophisticated media and computer environment with more Internet savvy than any previous generation. Setting The course environment in which the content is delivered. That is, face-to-face or virtual in this study. Conceptual Rationale There are two theoretical thrusts relevant to this study. The first is a framework that provides order and guidance into the complexities and dynamics of online and blended learning settings. This is the Community of Inquiry (Col) framework. The second perspective is that of college and career readiness (CCR) skill acquisition in the K-12 educational experience.
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Learning settings have long been the subject of much research, debate, and costefficacy reporting. Whether it is the one-room schoolhouse of the 1700’s, the open classroom o f the 1970’s, or a 21st century computer lab, learning settings are where students are gathered to learn. Issues relating to student engagement, achievement and motivation have been analyzed for determining best practices and systematic improvement. Arguably, setting plays a major role in determining the success or failure of student learning (Zhao & Kuh, 2004) and virtual learning settings are no exception. Distance learning in one form or another has been in existence for decades, even centuries (Caruso, 2012), but it is in the past 10 years that researchers and educators alike are seeking to identify the promise or plight in online or virtual learning. Currently, post secondary institutions are experiencing a type of eclipse where online learning is over shadowing on-site enrollment. Massive open online courses (MOOCs as the acronym would have it), is a current trend in educational technology at the university level that is causing much debate. Reactions of opposing perspectives range from “nightmare” to “tremendous opportunity” (Carter, 2013). It is reported that MOOCs have had student audiences reaching hundreds of thousands; even millions of students (Theisen, 2013). It stands to reason that virtual settings are alternative learning communities worthy to be analyzed. The online learning musing in higher education is only a foreshadowing of the virtual environment impact on K-12 institutions. In 2013, online learning opportunities for K-12 students were steadily rising across the U.S., and anticipated to continue (Keeping Pace, 2013). Retaining a sense of learning community is as important for students in a virtual environment as it is face-to-face. Osterman (2000) reviewed
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research on students’ need for belonging to a community of which much is based on Maslow’s belief in belonging as essential. McMillan and Chavis’s (1986) perspective is that community is both geography and relational. Belonging is belonging, no matter the walls or lack thereof. Blum’s research (2005) on student connectedness in schools parallels studies on connectedness in virtual learning environments (Rovai, 2002) in that connectedness significantly impacts dropout rates in both settings. The stage-environment fit theory indicates that behavior, motivation, and mental health were influenced by what was termed a fit between the student and the social environment (McNeely, Nonnemaker, & Blum 2002). When considering student success in a virtual learning environment, it is necessary to include connectedness as an added measure of complexity in determining an optimal educational experience. As the boundaries of a classroom expand beyond traditional four walls, a student’s sense of connectedness is of great interest to educators who utilize virtual delivery of instruction. It was Garrison, Anderson, and Archer (2003) who developed a comprehensive framework for identifying social and cognitive dimensions of studying online learning. This theoretical model called Community of Inquiry (Col), is a process model that views the online learning experience from the over-lap of social, cognitive, and teaching presence. (See Figure 1). This work hinged on the constructivist approach to learning of Dewey, in which the interaction between learning experiences and acquired knowledge is paramount to optimizing learning. Dewey (1958) construed that individual development was dependent upon community and that learning was essentially a social activity. Dewey presented the notion that students should function as a social group and asserted
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that the quality of education “is realized in the degree in which individuals form a group” (p. 65).
Community of Inquiry
PRESENCE
j
I
COGNITIVE PRESENCE^
EDUCATIONAL EXPERIENCE
TEACHING PRESENCE (8buetuM#FmoM»)
C om m un icatio n M edium
Figure 1: Community of Inquiry
Like Dewey, Vygotsky (1978) also emphasized the social nature of learning and proposed that there was an essential feature in a child’s development in what he termed the “proximal development zone” (p. 85). That is, when students’ social and cognitive developmental needs are optimally paired with peer and/or teacher assistance, it will result in learning achievement. Subsequently, this theory promotes the idea in which students must play an active role in seeking and receiving information while learning and that it stimulates a need to interact with others in the learning environment. Spitzer (1998) argued that educators must recognize that technology and social context are as important in distance learning formats as any other and that online learning environments can more easily result in learner isolation. The entire structure for
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communication is through technological means therefore subject to a lack in the physical human element. Theories and constructs about the necessity of building online learning communities were solidified in the review of research conducted by Winn (2002). In an age o f simulations, virtual classrooms, and teleconferencing, learning groups are not defined geographically but by common purpose, noting that most learning environments whether created or supported by technology, are social in nature (Winn, 2002). According to the Partnership for Assessment of Readiness for College and Careers (PARCC) consortium, student assessment tasks should “measure rigorous content and students’ ability to apply that content” while eliciting “complex demonstrations of learning and measure the full range of knowledge and skills necessary to succeed in college and 21st century careers (PARCC, 2010, p. 35). In 2011, member states of the Innovation Lab Network (ILN), including New York, together with Council of Chief State School Officers (CCSSO), shared the belief that their states’ education systems needed transformation in preparing all students for postsecondary learning, work, and citizenship. They committed to agreeing upon a definition of college and career readiness. Researchers and national thought leaders provided guidance regarding the kinds of skills that most directly support college and career readiness and lifelong success (CCSSO, 2013). Within the foundational thinking of ILN Framework is David Conley’s “Four Keys to College and Career Readiness” which has served as one its primary influences. In general terms, the ILN Framework is the over-lapping domain of knowledge, skills and dispositions that define CCR. (See Figure 2). Each of these domains is described in
detail with definitional elements (CCSSO, 2013 p. 6). Embedded in the framework is the assumption that “all students must cultivate increasingly complex higher-order cognitive and meta-cognitive skills that will allow them to engage meaningfully with the world around them (p. 5).
Figure 2: ILN Framework
According to the College Board (2003), knowledge includes rigorous content and application, which was the historical impetus for the Advanced Placement program when it was established just after WWII. The intent was to advance highly motivated students quickly toward college; therefore AP courses naturally fulfill this domain’s definitional elements. The remaining domains of skills and dispositions need careful attention when considering the virtual environment. The ILN lists definitional elements that point toward self-regulatory behaviors including time management, goal setting, study skills and self-control. Cognitive and social presence elements of self-awareness, social
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awareness, resilience, persistence and adaptability are listed. Participatory behaviors such as collaboration, communication and leadership are additional skills and dispositions that lead to CCR. (See Figure 3). K now ledge
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Figure 3: ILN Definitional Elements of Framework
Though educators across the globe may spend countless hours in discourse about how to prepare K-12 students for life after high school, most agree that there are essential skills and knowledge that includes technology for future 21st century citizenship. The rigorous curriculum of an Advanced Placement course has been established as academically challenging however delivering such content virtually may impact college and career readiness skills differently than in traditional face-to-face. Summary In summary, college and career readiness skills are not new to K-12 expectations, though the newly adopted Common Core Standards have developed a trajectory toward that goal. New York State outlines the over-lapping domains of knowledge, skills, and
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dispositions that define college and career readiness. The Advanced Placement program is a longstanding rigorous academic course offering to High School students and determining whether such courses offered virtually has an influence on CCR is a worthy investigation. This may be termed a perfect e-storm with the convergence of rigorous academics, virtual learning and preparation for 21st century demands all pointing to college and career readiness as 2015 approaches. Significance of the Study The significance of this study is that the virtual learning setting may impact a student’s perceived college and career readiness dimensions of self-regulation, cognitive presence and social presence, and participatory learning behaviors. Examinations of the difference between virtual and face-to-face settings in terms of its benefits have been done ( U.S. Department of Education, 2010) but the rigor of an AP course that is embedded in a technology environment may yield higher levels of college and career readiness skill acquisition. CCR has propelled to the forefront in predicting student success at the college level. Given the longstanding ideal that Advanced Placement courses are excellent preparation for college success, it is a fit for investigating a comparison of face-to-face and virtual settings. One of the implications of establishing a statewide goal of CCR is that states must create institutional commitment and that means redesigning delivery systems. According to the Innovation Lab Network Framework (2013) it will be necessary to set conditions “where students have multiple, anytime/anywhere, high-quality pathways to demonstrate progress and mastery... accessible 24X7” (p. 8). Such conditions are afforded in the virtual learning setting.
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Limitations and Assumptions This study was limited to the analysis of traditional face-to-face AP courses and virtual AP courses offered in a HS located in suburban Long Island. The students in both settings were required to meet the necessary criteria for taking an AP course and possessed the necessary pre-requisites. It is assumed that survey participants in this study are not deceptive with their answers, and that the participants answer questions honestly and to the best of their ability, including prior GPA and Course Grades Earned. The surveyed students will participate anonymously. It is assumed that this study is an accurate representation of the administration of the virtual course option. The survey of high school students was delimited to only a suburban Long Island district offering the Virtual AP option in a demographically mixed area of middle to low wealth. This sample was chosen because it was a recipient of the grant funding for the VAP program. The selection of AP courses for the study was limited to those that had both a face-to-face and virtual offering. The level of experience of the faculty teaching in the online format was not taken into consideration. The classes selected were naturally occurring as course offerings. Therefore, a random selection of students was not possible. The limited number of students who participated in the study did not allow the conclusions to be generalized.
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CHAPTER II REVIEW OF THE RESEARCH LITERATURE Introduction The study of learning environments is a valuable conduit for determining key indicators for successful learning, often defined in terms of student achievement. Researchers have also studied the skills necessary for success at the college level and most recently New York has established a college and career readiness (CCR) framework in order to guide K-12 education toward that goal. The primary influencers for the framework are taken from Conley’s “Four Keys to College and Career Readiness,” the Partnership for 21st Century Skills’ “Framework for 21st Century Learning,” and the Hewlett Foundation Education Program’s definition of deeper learning” (CCSO, 2013). Included in the CCR framework is both practiced, applicable technology skills and rich and rigorous content knowledge as identified in the Advanced Placement course offering. The Virtual Advanced Placement program is a setting naturally embedded with technology applications and rich content knowledge, perhaps one with optimal CCR conditions. According to Allen and Seaman (2007), over 3.5 million students, or 20 percent of those who enrolled in higher education, had taken at least one online course by the fall 2006 semester. Higher education institutions believed that including online course options was important to their strategic planning, and that incorporating them into their long-term
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goals (Allen & Seaman, 2007). Preparing students in the K-12 setting toward honing CCR preparation must then include virtual learning experiences. Thirty years ago, A Nation at Risk (1983) called for computer programming to be included as a “new basic” in schools and the No Child Left Behind Act o f 2001 deemed it essential that all students be technology literate by entrance into high school (NCLB, 2001). A decade later, in 2010, the Common Core Standards were adopted to place all K-12 students on a trajectory toward CCR. College and career readiness skills go well beyond knowing how to surf the Internet or study for an AP exam. Skills for success at the college level include self regulation behaviors, cognitive and social presence and participatory learning, all critical for students to achievement (N.Y. Department of Education, 2013). These skills practiced and applied in technology rich learning environments are central to best practices in CCR skill development. Virtual learning communities then, add a layer of technological complexity that may influence such a skill base. Research in the area of promoting CCR in the K-12 virtual setting is important to guiding educators toward developing highly effective learning opportunities. This is relevant because of issues regarding equity in access to AP courses that are now viable through virtual offerings. Utilizing rich content as offered in an AP course combined with a virtual, setting may optimally cultivate self-regulatory skills, cognitive, social presence and participatory learning practice for students. Self-Regulation Self-Regulation skills have been identified as important factors in student learning through a plethora of cross-disciplinary studies, including foundational theories of Zimmerman (1989, 2000, & 2002). Self-regulation is proactive development of
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personal skills and strategies when learning that correlates with indicators like motivation, goal setting, and self-control. Zimmerman’s research on self-regulatory learning (SRL) delineated it as the “transformation of mental abilities into academic performance skill and that students are meta-cognitively, motivationally, and behaviorally active participants in their own learning process” (Zimmerman, 1986a, p. 65). Self-regulation is hinged to a student’s own participation in the learning process. Zimmerman (1986a) also proposed a reciprocal view of self-regulation in which behavior, environment and self-functioned triadically and that environmental and behavioral influences were relative to self. For example, proactive manipulation of the environment (finding a quiet study space) exhibited in a behavior (actually creating it) may result in improved learning. The notion that a student has effective control over such learning would also be indicative levels of self-efficacy. Self-efficacy has been identified as a key variable affecting self-regulated learning (Bandura, 1986; Rosenthal & Bandura, 1978; Schunk, 1986; Zimmerman, 1986a). Kurtz and Borkowski (1984) found that higher quality learning strategies and self monitoring was used by students with high self-efficacy as opposed to low. Also a positive relationship was seen between the use of metacognition and motivation. Self regulated learning does not occur automatically. The ability to self-regulate depends on motivational factors such as commitment to personal goals, beliefs about the outcomes o f personal actions, and self-efficacy beliefs about their capabilities to perform actions at designated levels. It is the approach to learning with goals and the extent to which they believe they personally are capable of achievement.
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Zimmerman (2000) included goal setting and planning, organizing and transforming, seeking information, and rehearsing and memorizing among metacognitive categories of self-regulation. Learning in the virtual environment heightens the need for self-regulatory behaviors since many of the learning decisions required for success, ordinarily made by a face-to-face tutor or teacher, will be left primarily to learn. Accordingly, self-regulation refers to the self-generated thoughts, feelings, and actions for attaining one’s goals (Zimmerman, 2000). McMahon and Luca (2001) investigated the conceptual framework for identifying self-regulatory skills and created a synthesized version that includes both the affective and cognitive aspects of self-regulation. Meta-cognition and self-concept were seen as primary enablers, while self-monitoring and motivation were seen as subordinate processes in self-regulation. The survey instrument for measuring self-regulation behaviors, The Learning and Study Strategies Inventory (LASSI; Weinstein & Palmer, 1988) was also examined. It provides an assessment in 10 learning and studying scales as listed in McMahon and Luca (2001): 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Attitude towards studying and motivation for success Motivation, diligence, self-discipline, and willingness to work hard Use of time management principles for academic tasks Anxiety and worry about school performance Concentration and attention to academic tasks Information processing, acquiring knowledge, and reasoning Selecting main ideas and recognizing important information Use of support techniques and materials Self-testing, reviewing, and preparing for classes Test strategies and preparing for tests, (p. 429)
Zimmerman (2002) studied self-regulation to define essential qualities of academic self-regulation. He found that students with self-regulatory behaviors have been recognized as more likely to succeed academically but also to view their futures
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optimistically and there is a relationship between self-regulation and “perceived efficacy and intrinsic interest” (p. 66). Learners must believe they can learn, whatever the task, and they need to be motivated. In a study o f 145 participants, gender differences were analyzed with respect to motivation and achievement in a self-regulated online learning environment (Yukselturk & Bulut, 2009). The Motivated Strategies for Learning Questionnaire (MSLQ) was used to determine students learning strategies as they relate to achievement o f two variables: self-efficacies for learning and task value. For these variables, it was seen that female students had greater variance than males. Yukselturk and Bulut (2009) used a stepwise regression method and MANOVA to discover no significant difference between genders relating to the variables of self-regulation. Creating optimal learning conditions in the any learning environment is an important consideration and utilizing valid assessment instruments helps to raise awareness and skills. A large-scale study of college students done by Schreiner, McIntosh, Nelson, and Pothoven (2009), surveyed 6,617 undergraduate college students using a 32-item instrument, The Thriving Quotient (Schreiner et al., 2009), to measure student success in college. The study was conducted to identify the psychological functions of college students that were amendable to change in order for interventions to be delivered for successful college experience. The five malleable factors identified in the study: positive perspective, engaged learning, academic determination, social connectedness, and diverse citizenship, together explain up to 20 percent of the variation of a student’s outcomes such as GPA, program completion goal, learning gains, learning satisfaction, and perception of their fit to the institution (Shreiner, 2010).
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A controlled experiment conducted by Rodicio, Sa'nchez, and Acun~a (2012) examined whether learners needed systems of support for self-regulation, ranging from minimal to extensive with two variants of intermediate support in between. Eighty-six undergraduate students with little prior content knowledge were presented with a multimedia presentation involving animations to learn plate tectonics, and given one of the four support levels. The participant group with the highest-level support out performed the other groups indicating self-regulation support is necessary for complex conceptual knowledge acquisition. Cohen (2012) investigated the difference between college students who are effective self-regulators and those who are not. One such difference is that self regulators self-assess more often, are interested in the subject matter and more readily able to admit if they do not understand something. Also identified in an examination of the researcher was that self-regulated learners set clear and realistic goals use strategies, self-monitor, and evaluate their progress. Self-regulators also complete tasks on time and have high levels of motivation (Cohen, 2012). A Meta-Analytic Review of Implicit Theories and Self-Regulation completed by Burnette, O'Boyle, VanEpps, Pollack, and Finkel (2013) investigated the implicit theories o f associated with self-regulation. The setting/operating/ monitoring/achievement (SOMA) model was used to synthesize the theories with self-control theory and used a feedback loop structure of self-control theory to outline the direct links between implicit theories and the three primary self-regulatory processes. Results from 28,217 participants that were drawn from 113 independent samples revealed which self-regulatory processes are most strongly predicted by implicit theories and which self-regulatory processes most
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strongly predict achievement. Whether implicit theories were related to the selfregulatory processes of goal setting, goal operating, and goal monitoring were investigated. The over-all findings include the necessity for goals to be specific, and that goal achievement was strongly correlated with goal monitoring. That is, expectations for success were positive and strongly correlated with achievement. Ultimately, it was that mindsets matter when it comes to self-regulation (Burnette et al., 2013). A study of the interaction of predictors that contribute to student satisfaction in online learning settings was concluded by (Kuo, Walker, Belland, & Schroder, 2013). A regression analysis was performed to determine the contribution of predictor variables to student satisfaction and the role student background on the prediction o f student satisfaction in online learning. Kuo et al. (2013) found good predictors of student satisfaction were: leamer-instructor interaction, learner-content interaction, and Internet self-efficacy, however, the findings also suggested that student interaction and self-regulated learning did not contribute to student satisfaction. The largest variance in student satisfaction was learner-content interaction. Additionally, gender, class level, and time spent online per week seemed to have an influence on leamer-leamer interaction, Internet self-efficacy, and self-regulation. The research and investigations of student self-regulation are important in all learning environments and encompasses the internal, meta-cognitive processes that influence successful learning. It is the development of life-long learning skills. Skills like goal setting, strategies to achieve the goals and time management are key processes that must be personally adapted for each learning experience in every learning environment.
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A student’s physicality in a classroom does not always result in “presence” in that learning environment and relative value has been placed on student engagement as a measure of importance in students reporting a sense of belonging at school (Manley & Hawkins, 2010). Engagement at school and acceptance by peers and teachers are key factors related to student achievement (Goodenow, 1992; Finn & Voelkl, 1993). Student engagement in lessons was found by Grimaldi (2009) to be directly related to the quality of interaction between student and teacher. Examination of student engagement is a key factor in online learning environments as well. Dewey (1958) postured that the process of reflection had to do with one’s own mind and that inquiry led to deep and meaningful learning. Reflective inquiry is a cognitive process that is embedded in the learning despite the setting. The study of cognitive presence in online environments has been a focus of virtual learning researchers in that space and time varies so widely. Moore (1989,1990) developed a theory that interaction and design were recognizable variables in distance learning. He defined three core interaction types: leamer-teacher, leamer-leamer and learner-content. Through his work on transactional distance, cognitive and social presence was identified as important factors with respect to interaction. Cognitive and Social Presence Presence may be more accurately defined as a series of behavioral actions in a learning setting rather than physical attendance or proximity in a setting. Limiting a definition of student presence in face-to-face interactions has evolved and expanded with online learning. According to Picciano (2002), who studied online social and cognitive interactions, a high, positive correlation (.67) was found between perceived social
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presence and perceived learning of students in an online course. Though results could not be generalized because of sample size, and no statistically significant correlation between perceived social presence and grades in the final exam noted, the study recognized an emerging element in online learning. Cognitive presence is a recognizable factor in online student learning. Tu and Mclsaac (2002) identified social context, online communication and interactivity as primary elements in a student’s online presence. Further, social presence was an important predictor of online interaction, impacting the student success level as indicated in both achievement and satisfaction. Social presence was determined as “the degree of feeling, perception and reaction” (Tu & Mclsaac, 2002, p. 140), of being connected to another intellectual entity through text-based communication. That is, a student’s perceived feelings of interactive connections to peers and teachers. Short, Williams, and Christie (1976) hypothesized that the inability of a communication medium to transmit nonverbal cues has a negative effect on interpersonal communication and argued that social presence affects the nature of an interaction. While Short et al. (1976) focused on the“limitations” that a medium imposes on the interaction, Garrison (2007) focused on the way people “overcome” these constraints. Garrison (2007) said that social presence in an educational context was “to create the conditions for inquiry and quality interaction.. .in order to collaboratively achieve worthwhile educational goals” (p. 64). While social presence is essentially interaction with other participants it can be developed through collaborative learning activities (Richardson & Swan, 2003; Rovai, 2002). Cognitive presence relates to the thought movement of “exploration, integration and application” (p. 65) after participants have
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cycled through the inquiry stage of understanding in the online environment. Cognitive presence and social presence are essential in the development of a community of inquiry. Garrison’s study o f cognitive presence and online learning began with a focus on inquiry. Through the development of the model Community of Inquiry (Col) framework, in which learners must “construct and confirm meaning through sustained discourse”, cognitive presence, teaching presence and social presence were identified as three essential components in online learning (Garrison, Anderson, & Archer, 2000). Further Garrison et al. (2001) placed cognitive presence with a model of the practical inquiry (PI). The phases o f PI are defined in terms of triggering event, exploration, integration and resolution, which describe cognitive presence in an educational context generally and in online learning specifically. Social presence categories were defined as emotional expression, open communication, and group cohesion and that emotion are inseparably linked to task motivation and persistence. Garrison et al.’s work (2001) was a gateway to further study of the potential of creating an educational community of inquiry in computer-mediated environments (i.e. virtual). Swan and Richardson (2003) examined the role of social presence in online courses as it related to students’ perceived learning and satisfaction with the instructor. Of 97 college students surveyed, those with high overall perceptions of social presence also scored high in terms of perceived learning and perceived satisfaction with the instructor. The implication of this study revealed that the independent variable, perceived social presence, was responsible for 46 percent of the variability of the dependent variable, perceived learning. In addition, 59 percent of those surveyed said interaction, feedback, and other students’ perspectives were most beneficial. Their hypothesis was
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confirmed in that correlational analyses clearly showed a relationship between students’ perceived social presence and students’ perceived learning (Swan & Richardson, 2003). Garrison and Cleveland-Innes (2005) studied the relative nature of cognitive presence with interaction, finding that the biggest adjustment for students in the online environment was most directly associated with interaction. The role of structured collaboration on higher-order thinking was highlighted in the study and the condition in which deep learning emerges in an online collaborative environment was explored. In this study establishing social presence was shaped primarily through peer-interactions. The Study Process Questionnaire (Biggs, 1987) was used to measure the shift in students’ approach to learning, in order to identify the nature of interaction. It was found that when students’ interactions were more structured and systematic, there was deeper and more meaningful learning. Also, it was the quality of the interaction for a sustained manner that resulted in deeper learning. (Garrison & Cleveland-Innes, 2005). The study by Russo and Benson (2005) investigated the relationship between student perceptions of others in an online class and both affective and cognitive learning outcomes. Communication behaviors of students that contribute to the overall learning dynamic were discussed. Students were surveyed at the end of an online college course and according to the findings, a strong relationship between a student’s own perception of their online presence and the points they earned in the class. Also, perceptions of the presence of others were significantly correlated with scores on affective learning and particularly on student satisfaction with their own learning. This study highlights the importance of interaction among students to attitudes about the class and ultimately to satisfaction, achievement and course completion (Russo & Benson, 2005).
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The two goals of online education, presence and interaction, were reviewed by York, Yang, and Dark (2007), with an investigation of the determining guidelines for the transition between face-to-face and virtual learning environments. The researchers used a security assurance class as an exemplar of building a learning community that transitioned from face-to-face to online. Each element of the course is addressed to form a guideline of online course delivery with a focus on creating a learning community. By improving the level and nature of interaction there was an increase in a students’ sense of presence. It was determined that creating opportunities for interaction through discussion there was an increase social and cognitive presence (York, Yang, & Dark, 2007). Garrison and Akyol (2009) studied cognitive presence in a community of inquiry and its association with perceived and actual learning outcomes. They highlighted the relationship between educational ideas and technological capabilities to ensure the successful use o f instructional technology. The caution is that when educators became too focused on the technology, it is easy to lose sight of the educational goals. In addition to the Col, the practical inquiry (PI) model identified that students reached high levels o f cognitive presence and high learning outcomes of knowledge building processes that occur in online discussions. An investigation by Darabi, Arrastia, Nelson, Comille, and Liang, (2011) analyzed the associations of four discussion strategies with the practical inquiry (PI) phases o f cognitive presence in an online college course. Participants randomly assigned to discussion strategies described as structured, scaffold, debate, and role-play. Associations between PI phases and the discussion strategies were analyzed using a chisquare test to determine significance. Phases of exploration and integration had the
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highest indicators o f cognitive presence across each strategy, leading to speculation that the interacting elements require learners to construct deeper explanations for the processes generated by that interaction. The findings suggested that multiple perspectives expand and extend cognitive presence. Further, when designing strategies that promote social presence in online learning using the Col, cognitive presence is as important. Darabi et al. (2011) frames cognitive presence in phases of triggering events, exploration, integration and resolution, all crucial for deep knowledge construction. This was a mixed-method study, with an examination of the contribution of four scenariobased online discussion strategies - structured, scaffolded, debate, and role-play - to the learners’ cognitive presence (the outcome of the discussion). The discussion postings within each strategy were segmented and categorized according to the four phases and compared. It was concluded that the discussion strategy in which learners were required to take a perspective in an authentic scenario lead to cognitive presence, and, therefore, deeper, higher level learning. Deep and meaningful learning approaches in a community of inquiry were the focus of a study conducted by Akyol and Garrison (2011). The caution, however, is that online interaction itself does not necessarily promote deep learning unless the interaction is specifically focused on quality critical discourse. This study used a triangulated, mixed model o f qualitative and quantitative methodology to examine transcript analysis, learning outcomes, perceived learning, and satisfaction and interviews to determine learning processes and outcomes. One conclusion, based on the findings, was that cognitive presence in a community of inquiry is strongly associated with high levels of perceived learning.
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Leong (2011) investigated student interest and its relationship with social presence, cognitive absorption, and student satisfaction in online learning. Student interest is a term noted by the authors as used interchangeably with intrinsic motivation. A hypothesized structural equation model was used to study the variables that may influence interaction in online learning environments. One finding was that interest affects social presence, and satisfaction directly and that interest influences satisfaction indirectly through social presence and cognitive absorption. They found a significant relationship between interest and social presence indicative of the necessity for intrinsic motivation on social presence in online settings. Linking cognitive results with students’ online behavior (technological presence) was essential to Gregori, Torras and Guasch (2012) determining whether online interaction or individual differences impacted achievement. An analysis of 2,130 written computer-mediated communications from 88 participants in four prototypical online learning activities was completed to determine the influence o f technological presence on the quality of learning. Technological presence was found to be an important consideration for instructional and technological design. Results of a study conducted by Hosier and Arend (2012) revealed that student perceptions o f cognitive presence do not differ between face-to-face and online classes and that discourse facilitation is a key to prompting critical thinking or cognitive presence. Students felt critical thinking was positively influenced when discussions kept everyone focused and participating in a meaningful level. Whether the domain is English language arts, mathematics, sciences, social studies, history, art, or music, 21st century competencies and expertise such as critical thinking, complex problem solving,
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collaboration, and multimedia communication should be woven into all content areas (Hosier & Arend, 2012). According to the National Education Technology Plan (2010), “these competencies are necessary to become expert learners, which we all must be if we are to adapt to our rapidly changing world over the course of our lives” (p. VI). This involves developing deep understanding within specific content areas and making the connections between them. Social presence is important as a support for cognitive and affective objectives of learning through sustained interactions (Jin, 2009) and lack of social presence may lead to a high degree of frustration, and a lower level of affective learning (Garrison et al., 2010; Ke, 2009). Social presence is regarded as an important issue for enhancing and improving effective instruction in online learning environments (Garrison et al., 2000). The research of Wei, Chen, and Kinshuk (2012) found that that social presence had significant effects on learning interaction, which in turn significantly effects learning performance. Social presence was examined through three sub-constructs namely co presence, intimacy, and immediacy. These sub-constructs had correlation coefficients of above .70 indicating they all belonged to the main construct of social presence. This study showed social presence has significant effects on learning interaction as seen in the path analysis that was conducted (p. 538). The Col framework was found to be a useful theoretical tool as seen in the study done by Garrison et al. (2010). The complexities of the causal relationship between teaching, social and cognitive presence is worthy of continued study as the online learning environment continues to expand. A body of literature is beginning to grow that suggests an influence social and cognitive presence on learning outcomes. Creating
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social connection within learning environments is important to online course development due to the isolated nature of this type of instructional setting. Students in the virtual setting develop social and cognitive skills to the degree that participatory opportunities lend it to learning. Participatory Learning The study o f participatory behaviors is naturally set in the virtual environment; that use of the Internet, itself, requires active participation, no matter the application or purpose. Connectedness, collaboration, and content creation have been identified as participatory behaviors (Jenkins, 2006), and understanding of the “hidden curriculum,” is important, as it affects students’ future successes in both school and work. According to George Veletsianos, an assistant professor of instructional technology at the University o f Texas at Austin, “At the center of the participatory Web is the social interaction between individuals” (Parry, 2010), and that people’s experiences are not isolated, but social and connected. It was Rovai (2002) that challenged the belief that the sense of building a community was not limited to the traditional face-to-face environment, but that “spirit, trust interaction and commonality promote community” in the virtual environment (p. 12). The constructs of Web 2.0 learning environments are discussed by Tu, Blocher, and Roberts (2008), in four dimensions, as a theatrical metaphor - cognitive/scripts, social/ actors, networking/stages, and integration/acting dimensions. The idea that online activity involves the creation of personas and that the Web is a kind of platform for performance and scripting is framed. Tu and colleagues (2008) assert that Web 2.0 environments afford learners an opportunity to learn by acting in the learning
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environment. This “acting” includes accomplishing learning goals, and that the virtual world takes the teacher off of “center stage” and gives the learner a more active role in learning. This metaphor re-images the sage on the stage construct that often remains in the face-to-face learning environment. Such imagery resonates with the active role of the online participatory learner. The Web changed from a medium to a platform for action, in line with educational theories such as constructivism and connectionism. Learning takes place in the context and in collaboration and provides opportunities to solve realistic and meaningful problems. Ullrich et al. (2008) analyzed the technological principles of Web 2.0 and described their pedagogical implications on learning. Web 2.0 enables and facilitates the active participation of each user. They cite applications and services that “allow publishing and storing of textual information, by individuals (blogs) and collectively (wikis), o f audio recordings (podcasts), of video material (vidcasts), of pictures, etc.” as indicative o f opportunities for participatory learning. Ulrich et al. (2008) also notes the changes in Web 2.0 interface to behave more like desktop applications, now enabling more creative content. The research of Wu, Bieber, and Hiltz (2008) analyzed the learning of students through an online participatory exam in which they were required to construct, evaluate and engage in learning through creating the exam. The participatory examination in this study changed the role of teachers and students by shifting the students from passive testtakers to active exam designers and evaluators. Such active participation is necessary to engage students in deep and meaningful learning. Greenhow, Robelia, and Hughes (2009) found learner participation, creativity, and online identity formation, emerged
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from the analysis of research on the Web. They speculate that cloud computing and social operating systems will likely influence both education and research in the next decade. If this is so, the necessity of understanding the participatory learning will increase. Malhiwsky (2010) used a mixed method to determine the effect of Web 2.0 technologies on student achievement. The quantitative portion collected data from a pre and post-test and examined students’ self-reported levels of classroom community, connectedness and learning The qualitative portion investigated the ways students used Web 2.0 technologies in their language learning and their perceptions. The classroom community survey indicated a higher level of student reported classroom community in the Web 2.0 enhanced courses than in the non-Web 2.0 courses. There was also a higher level o f connectedness in the Web 2.0 courses reported. Five overall themes were identified in the coding of the interviews: networking, convenience, enhancement, pleasure and ease of use (Malhiwsky, 2010). Lewis, Koston, Quartley, and Adsit (2010) presents a definition for a community of practice in that it represents a collaborative process in “which resources and knowledge can be more freely exchanged across school, and program settings” (p. 157), but not without the barriers present in traditional face-to-face settings. However, Web 2.0 tools have the capability of fostering the development of communities of practice and allow for “broad-based participation and information exchange” (p. 157). Lewis et al. (2010) describes a virtual community of practice developed through a partnership between University of Buffalo School of Social Work and University of Buffalo Teaching and Learning Center. A Web 2.0 social networking site tool was utilized to
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promote the free exchange of information indicating that educators consider such technology when trying to improve accessibility (Lewis et al., 2010). Given the dominance o f constructivist and participatory approaches to learning, keeping students out of the design of new educational environments creates a paradox. Palaigeorgiou, Triantafyllakos, and Tsinakos (2011) conducted a study of 117 undergraduate students from two Greek Informatics Departments. The subjects had extensive computer experience, engaged in social media and were familiar with Web 2.0 technologies. For the study, they participated in 25 participatory design sessions and produced 773 distinct needs including broad categories of participation, networking and communication. The majority of students tried to design a system around advanced digital literacy skills or personal learning environments (Palaigeorgiou et al., 2010). Lin and Tsa (2012) conducted a study that investigated the relationships between college students’ behavioral and cognitive engagements while performing collective, online information searching (CIS) activity. The comments, actions and annotations of 101 college students in Taiwan were logged and analyzed with four categories of behavioral engagement were identified, namely “Hitchhiker,” “Individualist,” “Active,” and “Commentator.” Also, a refined coding framework revealed that the students’ cognitive engagement levels could be identified as “Deep” and “Surface.” When a comparison of behavioral and cognitive engagements it revealed those with active behavior exhibited deep cognitive engagement. The findings suggested that behavioral and cognitive engagements are critical to participatory learning (Lin & Tsa, 2012). The new literacies almost all involve social skills developed through collaboration and networking (Jenkins, 2006), or “collective intelligence...In such a world, everyone
41
knows something, nobody knows everything, and what any one person knows can be tapped by the group as a whole” (p. 40). Loertscher and Koechlin (2013) warned about the predictability of many virtual courses and that a lack in community and participatory opportunities for students is cause for dropout rate and low achievement. The format of “read this, listen to that, do that, take a test and repeat” makes for a boring and predictable course experience. In contrast, a Virtual Learning Commons was proposed in which virtual rooms such as the Knowledge Building Center, Information Center and School Culture were developed in order to encourage participatory online learning opportunities. Much of this development was through the use of Web 2.0 apps as made available free by Google Apps in Education (Loertscher & Koechlin, 2013). They found that while students applied Web 2.0 tools, they collaborated with others to create new ideas from separate, individual ones. This is a key CCR skill practiced in a virtual environment. John (2013) explored the connection between collaborative consumption and technology, posing the belief that technology drives and enables participatory activity. He frames participatory activity in that technology environments promote a ‘sharing economy’ in which there are consumption and production of information. Sharing economies is the swapping, sharing, bartering, trading and renting being reinvented through the latest technologies. This is essential in the 21st century and a necessary skill. In his review of 63 news articles of collaborative consumption, John (2013) found sharing o f information and communication in a technology environment often leads to transference into face-to-face broadening the scope of the share.
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Reilly (2011) described new media literacies (NMLs) are a set of social skills and cultural competencies that students and teachers need to acquire in order to fully participate in the new media environment. The main shift is from a focus on individual expression to community involvement in which the NML require reflection and acquisition of important skills: teamwork, leadership, problem solving, collaboration, and brainstorming, resounding the CCR skills for success. College and Career Readiness According to Conley (2011), college and career readiness is a complex and multi dimensional concept that requires more than a single math or English test score to show acceptable levels of acquisition. Conley (2011) denotes readiness as an alignment of student skills, interests and objectives and current CCR measures are insufficient. Conley draws a defining line of difference between completing college prerequisite requirements such as prep and admissions tests and actual readiness. Success then is being capable of completing an entry-level course at a level of proficiency that makes it possible to take the next course in a sequence of courses. Measurement then will need to be closer to “profiles” of readiness in relation to goals and then recommendations on how to improve (Conley, 2011). Information, media and technology skills are listed tandem with core content acquisition and learning innovation and was endorsed by the Partnership for 21st Century Skills (P21) when it was formed in 2002. It underscores the seriousness of students being credentialed rather than ready for college. Since then, in 2011, the Readiness Act was introduced to Congress and a formal definition of readiness was determined upon agreement between colleges and employees on what readiness actually is. Essentially, it
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is students who learn to fuse subject knowledge and the 4C-skills (critical thinking communication, collaboration, and creativity) in school are better prepared to enter the workforce. Learning, adapting and applying these skills in all subjects areas equates to college and career readiness as included in The 21st Century Readiness Act (S. 1175 & H.R. 2536). An Act of this nature comes with funding incentives hinged to it. Fear of Failure The Academic Goal Questionnaire, locus of control survey, and fear of failure survey were administered online to students in Introductory and Intermediate Microeconomics courses midway through the spring 2009 term in a study by (Hadsell, 2010). These were administered in stages to measure: mastery and performance goals, locus of control, and fear of failure of students as it relates those to academic outcomes such as effort (attendance, completion of reading assignments, and completion of homework), learning outcomes (exam scores), and affective measures such as interest in and enjoyment of economics class. Results indicated that introductory students performed better on the exams. Fladsell (2010) also concluded that a stronger desire to be no worse than other students led some students to perform better on the exams. Fear of Failure was only seen to have a notable negative association with attendance than with any o f the other variables. A study by Kahraman and Sungur (2012) of 977 (495 girls, and 482 boys) 7th grade Turkish middle school students explored the antecedents and consequences of achievement goals by proposing a path model. The Performance Failure Appraisal Inventory was used to assess students' fear of failure delineated into descriptors: The Fear of Shame and Embarrassment, The Fear of Devaluing One's Self Estimate, The Fear of
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Having Uncertain Future, The fear of Losing Social Influence, The Fear o f Upsetting Important Others. Positive relationships were expected to be found between students' fear o f failure and their approach and avoidance performance goals, and mastery avoidance goals. Kahraman and Sungur (2012) found Fear of Failure to be significantly linked to achievement goals acting as precursors of the achievement goals. Among the findings was that 30.2 percent of Fear of Devaluing One's Self Estimate effected SelfEfficacy. A positive relationship was found between fear of failure and task value. In an earlier study Elliot, Chirkov, Kim and Sheldon (2001) noted that in some cultures, fear of failure is not linked to negative outcomes rather a stimulant to strive for better achievement outcomes and that higher levels of fear of failure result in higher levels of intrinsic interest. A study of achievement goals and sports was explored by Conroy and Elliot (2004) in which a hierarchal model of achievement motivation was developed. It described that in addition to the need achievement, Fear of Failure actually energized achievement behavior. Their findings suggested that Fear of Failure had a causal influence over achievement goals and that continued study of the effects was warranted. The purpose of Bartels and Ryan’s (2013) study was to further examine the relationship between the intrapersonal/interpersonal dimensions of Feat of Failure and achievement goals. The aim was to assess the relationship between the Fear of Fear dimensions and mastery and performance approach avoidance goals. Bartels and Ryan (2013) also used the Performance Failure Appraisal Inventory to measure 308 undergraduate students from a midsize, mid-western University (USA). Results indicated
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that fear of failure was significantly correlated with achievement however it was noted that the investigation into diverse sample such as gender should be further explored. Gender was researched by Turliuc and Danila (2012), with respect to fear of failure, need for achievement, and need for affiliation as energizing agents of motivation to achieve. The study involved the use of a Fear of Failure scale that measured 100 Romanian college students in (50 females and 50 males) level of fear in the work and educational settings. A /-test analysis between genders showed that there was no significant difference between genders when it came to Fear of Failure. When comparing grade averages and Fear of Fear, female students with lower grade averages had higher levels of Fear of Fear and significantly higher scores for Need for Achievement than males (Turliuc & Danila, 2012). The Need Achievement perspective was examined by Martin and Marsh (2003), and determined that Fear of Failure was divided into two main areas: over-striving and self-protection. It is suggested that for some students it promotes achievement and persistence toward a goal and for others it may produce anxiety and reduced resilience resulting in underachievement. One conclusion indicated a need for analysis of learning climate with respect to Fear of Failure and the implications of climate on motivation (Martin & Marsh, 2003). Another study investigated the effect of one’s preoccupation with failure combined with perceived academic control as a motivation for success and the interaction will predict academic achievement (Brunborg, Pallesen, Diseth, & Larsen, 2010). A questionnaire about study habits, preoccupation with failure and perceived academic control was administered to 442 first year psychology students in Scandinavia. The
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findings concluded that although perceived academic control had an effect on achievement, none was found from preoccupation with failure. Brunborg et al. (2010) also examined these variables as they predict work hours and found that those with low perceived academic control but high preoccupation with failure had longer work hours. A structural equation analysis and multiple-group comparisons were used by Wach, Spengler, Gottschling, and Spinath (2015) to determine whether gender influenced self-reported motivational variables (self-perceived abilities, fear of failure) on mid-term school grades of elementary students (140 boys and 185 girls) in Germany. Their results indicated that a gender gap exists partly due to theses motivational variables. It was seen that girls had higher levels self-perceived abilities, which led to higher grades. Higher levels of Fear o f Failure led to lower math grades but only for girls (Wach et al., 2015). Fear of failure and success orientations as they relate to achievement and motivation was investigated using the perspective of a self-worth theory (De Castella, Byrne, & Covington, 2013). The study examined how approach and avoidance orientations related to self-handicapping, defensive pessimism, and helplessness in Eastern and Western settings. Study I, in Japan examined 1,423 High School students and found that helplessness and self-handicapping were highest when students were low in success orientation and high in fear of failure. Study II was replicated in Australia with 643 students. Success orientation largely moderated the relationship between fear of failure and academic engagement in both cultures with implications that fear of failure is associated with many self-protective strategies (De Castella et al., 2013).
Technology Beliefs of Millennials
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The current High School students arethe newest of the millennial cohort and termed Generation Z. McMahon and Pospisil (2005)developed demonstrated “millennial” traits for this subgroup, summarized in the following: •
learning preferences that tend toward teamwork, experiential activities, and the use of technology
•
being responsible and focused on achievement
•
a need to stay connected
•
zero tolerance for delays
•
having strengths in multitasking, goal orientation, and collaboration. While educational researchers use the over-arching term 21st century learning to
allude to the technologies used in learning, it is the use mobile devices that separate Generation Z from the umbrella millennial group (McMahon & Pospisil, 2005). In a qualitative study by Oblinger and Oblinger (2005) high school students were asked why technology is essential to their education, responses included, “It’s part of our world” and “Technology is so embedded in our society, it’d be hard not to know how to use it” (p. 2.3) and consider access to the interne important to daily life. It was also found that instant messaging (IM) is the most accessed form of communication, but, moreover, it can support multiple, simultaneous conversations (Oblinger & Oblinger, 2005). Jones, Jun, and Martin (2007) noted in their research that millennials are also known as the Net-Generation or {Generation, because they were surrounded by digital technologies from a young age. They denote computers, the Internet, online games and mobile phones as normal to them as television is to previous generations. They
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summate the Generation Z students as those who would “rather be at home using their own high-tech computer or game machines” than using it in school (Jones, Jun, & Martin, 2007). Summary Though reforms in education are currently ongoing, unprecedented funding reductions, virtual learning, online learning and blended learning innovations continue to have allocations that fall under the Obama Administration’s Race to the Top rewards termed “reform-oriented competitive initiatives.” College and Career readiness skills, though not new to K-12 instructional programs and educational goals, it has recently become a focus in delivering of Common Core standards. In a summary of research on virtual schools, studies of virtual learning included: student achievement, retention rates, student satisfaction with online courses, and cost effectiveness (Blazer, 2009) as well as strategies for increasing effectiveness. There appears to be a dearth of studies on the virtual learning settings’ impact on college and career ready skill acquisition. Students enrolled in face-to-face and virtual AP course settings are assumed to be more academically motivated in general and should prove suitable subjects for this study.
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CHAPTER III RESEARCH DESIGN AND METHODOLOGY Introduction Virtual learning opportunities in New York State K-12 institutions are on the increase through inducements of grant funding, including the VAP (Virtual Advanced Placement) program awarded to districts across the state. In 2011, member states of the Innovation Lab Network (ILN), including New York, together with Council of Chief State School Officers (CCSSO), shared the belief that their states’ education systems needed transformation in preparing all students for postsecondary learning, work, and citizenship. The ILN developed a definition of college and career readiness, which includes self-regulation, cognitive presence, social presence, participatory learning behaviors and technology use as well as rigorous content. The Advanced Placement program is considered a long-standing rigorous course offering. The purpose of this study is to examine High School students’ college and career readiness dimensions of self-regulation, cognitive presence, social presence, participatory learning behaviors and technology beliefs in the face-to-face and virtual AP course settings. Original Research Questions The characteristics (gender, grade level, prior grade point average [GPA], course grade earned) o f HS students in face-to-face, and virtual AP course settings will be gathered.
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Research Question One How do High School students describe their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings? Research Question Two How do High School students differ in their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings according to gender, and prior grade point average [GPA]? Research Question Three What is the relationship between the CCR dimensions of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs, gender, and prior grade point average [GPA] in traditional face-to-face and virtual AP course settings? Research Question Four How do the CCR dimensions Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs gender, and prior grade point average [GPA] predict High School students taking traditional face-to-face and virtual AP course settings? Research Question Five How do High School students in traditional face-to-face and virtual AP course settings describe their course experience?
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Research Question Six How do High School students’ characteristics of gender and prior grade point average [GPA] compare in traditional face-to-face and virtual AP course settings? Selection of Setting and Subjects Students in a large suburban high school within a school district located in New York State, who completed an Advanced Placement course, were invited to participate in this study. This district is considered a moderate to low income community with enough of a high needs population to receive RTTT funding through the New York State virtual course grant. The selection of subjects was based on students enrolled in the VAP program and those enrolled in face-to-face AP courses. The students who opted for the virtual course were a combination of self-selecting and invited. Students ranged from the first time AP enrollees and first time virtual course attendees as well as repeated in both settings. There were approximately nine hundred eligible AP participants of which approximately one hundred forty were part of the VAP offering. There was a total of one hundred thirty four usable response surveys, one hundred four face-to-face participants, twenty virtual AP students, and five who did not define setting. Design and Methodology A survey instrument was developed to measure High School students’ selfreported levels of CCR dimensions of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings. All participants completed a survey that used a 5-point Likert scale that measured the CCR dimensions and demographic information, in which “ 1" indicated Strongly Disagree and "5" indicated Strongly Agree with “3” equating to
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Neutral. The survey items for Cognitive and Social Presence were based on the work of Arbaugh et al., (2008) and the Community of Inquiry (Col) framework. The survey items for the variable Self-Regulation were taken from The Self-Regulation Questionnaire (SRQ; Brown, Miller, & Lawendowski, 1999). The SRQ measures beliefs about one’s ability to “ develop, implement, and flexibly maintain planned behavior” (Brown, Miller, & Lawendowski, 1999, p. 281). Participatory survey items were based on the work of Jenkins (2006) and Rovai (2002). Technology questions were basic in reference to beliefs about computer-based learning. Survey Instrumentation The survey examined of CCR dimensions of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs. Table 3.1 reports original survey dimensions, items, and score range. Table 3.1 Original Survey Dimensions, Items and Score Range Dimensions Demographics Self-Regulation
Items
Number o f Items
Raw Score Ranee
Researcher(s)
1-6 7-15
6 9
945
Cognitive Presence
16-26
11
11-55
Arbaugh,, Cleveland-Innes,, D ia z,, Garrison,., I c e ,, Richardson,, et al. (2008)
Social Presence
27-35
9
9-45
Arbaugh,, Cleveland-Innes,, D ia z ,., Garrison,, Ic e ,, Richardson,, et al. (2008)
Participatory Behaviors
36-43
8
8-40
Jenkins (2006); Rovai et.al, (2005)
Technology Beliefs
44-58
15
15-75
Dependent Variable
Virtual Face-to- Face
Brown, Miller, & Lawendowski, 1999
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Validity Content Survey items measuring college and career dimensions were extracted from previously juried survey instruments. The items for the variable Self-Regulation were taken from The Self-Regulation Questionnaire (SRQ; Brown, Miller, & Lawendowski, 1999). The survey items for Cognitive and Social Presence were based on the work of Arbaugh, et al., (2008), the Community of Inquiry (Col) framework. Participatory Behaviors survey items were based on the work of Jenkins (2006) and Rovai (2002). Construct For the purpose of this study, a factor analysis of the responses from participants was utilized to verify that the items measure what they purport to measure. After identifying 134 useable surveys based on the sufficient completion of the instrument by respondents, a factor analysis was performed to gain clarity among the five proposed variables that affect high school students’ college and career readiness (Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs). The 58 survey items pertaining to college and career readiness were analyzed using principal component analysis as the extraction method with the Kaiser Normalization rotation method yielding five interpretable variables. The variables Self-Regulation, Cognitive Presence and Technology Beliefs remained, while Social Presence and Participatory Behaviors merged into one variable and a new variable indicating academic fear emerged. The revised variables: Self-Regulation, Cognitive Presence, Socially Active Learning, Technology Beliefs and Fear of Failure were included in the research questions from this point forward. Table 3.2 identified seven items that were removed
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due to inconsistencies in responses and improved reliability as determined through scale item deleted factor reliability tests. Table 3.2 Deleted Items from Survey Instrument
Variables SelfRegulation Cognitive Presence Social Presence Technology
Item Number 15
Item I desire to completely master the material presented in this class.
24
Reflection on course content and discussions helped me understand fundamental concepts in this class better
25
I can describe ways to test and apply the knowledge created in this course.
27 45 49 57
Getting to know other course participants gave me a sense o f belonging in the course Many o f my classmates know more about computers than I do I used multiple devices to complete course work I would be equally prepared to enter college without technology
Table 3.3 presents the item loading for the factor Self-Regulation of five items with an Eigenvalue is 3.47, and accounts for 6.66 percent of the variance in the total responses within the factor analysis. The Crobach’s Alpha coefficient for this is .826. Table 3.3 Factor Loading - Self Regulation N -134 Item Number
Item 7 My goal in this class is to get a better grade than most of the other students
8 9 10 11
It is important for me to do well compared to others in this class It is important for me to do better than other students. I just want to avoid doing poorly in this class. My goal in this class is to avoid performing poorly
h2 .765 .785 .838 .584 .475
Table 3.4 presents the item loading for the factor Cognitive Presence with thirteen items with an Eigenvalue is 8.13, and accounts for 15.64 percent of the variance in the total responses within the factor analysis. The Crobach’s Alpha coefficient for this dimension is .840.
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Table 3.4 Factor Loading - Cognitive Presence N=134 Item Number
Item
16
I think problems posed increased my interest in course issues
.559
17
I think course activities increased my curiosity
.573
18
I was motivated to explore content related questions
19 20
I utilized a variety o f information sources to explore problems I found brainstorming and finding relevant information helped me to resolve content related questions
.624
21
I think online discussions were valuable in helping me appreciate different perspectives
.529
22
I think combining new information helped me answer questions raised in course activities
23
h2
.736
.705
.610 .614
26
I think learning activities helped me construct explanations/solutions I can apply the knowledge created in this course to my work or other non-related class activities
43
I value others when they contribute online
.639
51
Compared to other students I really enjoy being in class
.505
54
I needed to learn new technology skills to do well in this class
55
I learned new course content and new technology skills in this class
.363
.484 .457
Table 3.5 presents the item loading for the factor Socially Active Learning (Social
Presence and Participatory Behaviors) with eleven items with an Eigenvalue is 6.54, and accounts for 12.57 percent of the variance in the total responses within the factor analysis. The Crobach’s Alpha coefficient for this dimension is .829. Table 3.5 Factor Loading - Socially Active Learning Behaviors N=134 Item Number
Item 28
I was able to form distinct impressions o f some course participants
31
I am comfortable participating in the course discussions
32 33 34 36 37 38 39 40
I am comfortable interacting with other course participants I am comfortable disagreeing with other course participants while still maintaining a sense o f trust. I think that my point o f view was acknowledged by other course participants I sought to work with others in order to learn more I offer information and ideas freely to others When I need help I reach out to other students for help I value the creativity o f others I feel that my creativity is valued by others
41
I find it important to collaborate with others
h2 .469 .616 .789 .625 .602 .633 .599 .385 .559 .525 .443
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Table 3.6 presents the item loading for the factor Technology with 11 items with an Eigenvalue is 5.88, and accounts for 11.31 percent of the variance in the total responses within the factor analysis. The Crobach’s Alpha coefficient is .858. Table 3.6 Factor Loading - Technology Beliefs N=134 Item Number Item 29 I think online or web based communication is an excellent medium for social interactions 30 I am comfortable conversing through the online medium 35 I think online discussions help me to develop a sense of collaboration 42 I felt that when I participated online others valued my contribution 44 I think computers make schoolwork more fun/interesting 46 Computers help me to improve the quality of my schoolwork 47 I have learned new technology skills during this course 48 Computers help me understand my classes better 50 I generally enjoy schoolwork better with technology 52 I prefer to use computers to do schoolwork instead of using pencil and paper 53 I consider my technology knowledge strong
h2 .672 .675 .434 .451 .837 .711 .434 .737 .553 .637 .446
Table 3.7 presents the item loading for the factor Fear of Failure with 5 items with an Eigenvalue is 3.88 and accounts for 7.47 percent of the variance in the total responses within the factor analysis. The Crobach’s Alpha coefficient for this dimension is .801. Table 3.7 Factor Loading - Fear o f Failure N=134 Item Number
Item
12 Sometimes I am afraid that I may not understand the content of this class as thoroughly as I would like 13 I worry that I may not learn all that I possibly could in this course 14 I am often concerned that I may not learn all that there is to learn in this class 58 I feel that my technology knowledge did change during this course
h2 .769 .837 .776 .437
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Reliability After factor analysis, the dimensions of the study were subjected to reliability tests. A Cronbach alpha analysis was used to calculate the reliability of each of the survey items for each dimension. Cronbach’s Alpha coefficient of internal consistency was computed for each dimension and ranged from .801 to .856 as shown in Table 3.8. As a result of factor analysis using principal component analysis and a rotational method of Varimax with Kaiser Normalization, two variables merged and a new variable emerged. Social Presence and Participatory Learning combined to become Socially Active Learning and the new variable Fear of Academic Failure was identified. Table 3.8 contains the revised raw score ranges and alpha coefficients. Table 3.8 Raw Score Range/Scale Reliability after Factor Analysis Dimensions Demographics Self-Regulation Cognitive Presence
Items
Number o f Items
Raw Score Range
Alpha Coefficient a
1-6
6
7-11
5
5-25
.826
13
11-55
.840
16-23,26,43,51,54,55
Socially Active Learning
28,31-34,36-41
11
11-55
.829
Fear o f Failure
12,13,14,58
4 11
4-20 13-65
.801 .856
Technology Beliefs Dependent Variable
29,30,35,42,44,4648,50,52,53 Virtual Face-to- Face
Data Gathering Procedure After obtaining the Institutional Review Board approval, a letter o f assent was sent to parents of students in one Suffolk County high School with virtual AP course offerings requesting participation in the study. Included in the letter was the link to the
58
online Google Forms survey. Participants were offered a copy of results upon request. The survey incentive was the raffling of three individual iTunes gift cards worth $20 each to be completed in January 2015. The winners were randomly selected and a department chair at the school facilitated distribution to the winners. Data Analysis Refined Research Questions The Statistical Package for the Social Sciences (SPSS) software version 21 was used to analyze the following questions that guided this study. Research Question One How do High School students describe their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings? Descriptive data including mean, standard deviation and item frequency were analyzed to answer question one. Research Question Two How do High School students differ in their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face and virtual AP course settings according to gender, and prior grade point average [GPA]? An Independent Samples t test was performed to measure the difference in CCR skills in both settings.
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Research Question Three What is the relationship between the CCR dimensions of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs, gender, and prior grade point average [GPA] in traditional face-to-face and virtual AP course settings? Two Correlation tests was performed, one for face-to-face and one for virtual to compare the relationships of CCR dimensions, gender and prior grade point average [GPA] in both settings. Research Question Four How do the CCR dimensions Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs gender, and prior grade point average [GPA] predict High School students taking traditional face-to-face and virtual AP course settings? Logistic Regression Analysis was performed to analyze the degree to which CCR dimensions and student demographic factors predict students taking traditional face-toface and virtual AP course settings. Research Question Five How do High School students in traditional face-to-face and virtual AP course settings describe their course experience? Answers to open-Ended question were analyzed for emergent themes. Research Question Six How do High School students’ characteristics of gender and prior grade point average [GPA] compare in traditional face-to-face and virtual AP course settings?
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Two - Two way ANOVA were conducted and the factors will be Gender and Course Setting, dependent variable 1) Prior GPA and 2) Grade Earned. Limitations A limitation in the study is that responses were from AP students in one long island high school based on the district’s eligibility for the RTTT funds available through the VAP grant. Another limitation is that prior GPA and Course Grades Earned were collected using self-report. Students could have answered untruthfully, but there was no obvious benefit in doing so. This could have been avoided by requesting an authorized copy o f high school academic records; however previous research has shown a correlation of .85 between self-reported and transcript-based GPAs (Schuman et al., 1985), expecting high reliability o f self-reported prior GPA and Course Grade Earned. Also, the instrument used was a web survey, which might have biased the sample toward students engaged in the virtual AP course.
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CHAPTER IV DATA ANALYSIS AND FINDINGS Introduction The purpose of this quantitative research was to investigate college and career readiness skills of High School students in traditional face-to-face and virtual Advanced Placement course settings. College and career readiness was measured utilizing a survey instrument developed to measure self-regulation, cognitive presence, socially active learning behaviors, technology beliefs and fear of academic failure in traditional face-toface and virtual course environments. The difference in HS students was examined on variables including gender; grade level; grade point average with face-to-face and virtual AP courses. The face-to-face students’ survey responses were compared to the virtual students’ survey responses. Investigation into whether a relationship exists between variables for HS students in faceto-face and virtual AP courses. Also examined was whether High School students’ college and career readiness dimensions might predict students who take face-to-face or virtual AP courses. Finally a comparison of High School students’ characteristics of gender, prior GPA, and grade level was examined in face-to-face and virtual AP courses. The study was planned for a Long Island High School that offered both face-toface and virtual Advanced Placement courses. Nine hundred requests were distributed to parents of HS students taking AP courses in either face-to-face or virtual inviting them to
62
complete a 58 item online survey including one open-ended response. There were 134 usable surveys completed for a return rate of 6.7 percent. Those who wished to participate in the chance to win one of three $20 iTunes cards were invited to do so through an additional submission. The winners were selected and prizes distributed through a third party school chairperson. Demographic Analysis Students in one Long Island High School who were taking Advanced Placement courses in both face-to-face and virtual settings were invited to participate in the study. They were enrolled in a variety of subjects. From the 134 useable surveys, Grade level distribution, gender and number of students in each course setting was examined. Table 4.1 Distribution by Participant’s Grade Levels Grade
Valid
Missing Total
Percent 42.5
Valid Percent
10
Frequency 57
43.2
Cumulative Percent 43.2
11 12
56 19
41.8 14.2
42.4 14.4
85.6 100.0
132 2 134
98.5 1.5 100.0
100.0
Total System
The students reported being in grades 10th, 11th’ and 12th grades with two students who did not identify their grade level. Of the total 134 student respondents, 113 (84.3%) were underclassmen and 19 (14.2%) were seniors (Table 4.1). Participant gender was also examined as summarized in Table 4.2, with 81 (60.4%) females and 53 (39.6%) males.
63
Table 4.2 Distribution by Participant’s Gender
Valid
Frequency
Percent
Female
81
Male Total
Cumulative Percent
60.4
Valid Percent 60.4
53
39.6
39.6
100.0
134
100.0
100.0
60.4
There were 104 students who responded as taking the course in a face-to-face (F2F) setting, 25 in a virtual setting (VAP) and 5 did not answer the question (Table 4.3). O f the 134 respondents, four times as many students took their AP courses in face-to-face over virtual course settings. There were 104 (77.6 percent) students in face-to-face AP courses and 25 (18.7 percent) in a virtual AP course. Table 4.3 Advanced Placement Course Setting ____________________Frequency F2F 104 Valid Missing Total
Percent 77.6
Valid Percent 80.6
Cumulative Percent 80.6
VAP 25 18.7 19.4 100.0 Total 129 96.3 100.0 System____________5________ 3/7__________________________________ 134 100.0
The participants were also asked to self-report their current Grade Point Average, summarized in Table 4.4. Seventy-two students (53.7%) reported having a prior GPA of 95 or over, 51 (38.1%) reported a prior GPA of 85-95 and 11 (8.2%) reported 75-85. No students reported a 65-75 prior GPA or a 65 and below.
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Table 4.4 Grade Point Average Before AP Course
75-85 Valid
8^ '95 95+ Total
Frequency
Percent
Valid Percent
Cumulative Percent
11
8.2
8.2
8.2
51
38.1
38.1
46.3
72 134
53.7 100.0
53.7
100.0
100.0
Research Questions Research Question One Research question one asked, How do High School students describe their CCR skills of Self-Regulation, Cognitive Presence, Social Presence, Participatory Behavior and Technology Beliefs in traditional face-to-face, and virtual AP course settings? Research question one was answered using a descriptive statistical analysis of means, standard deviation and frequencies conducted to analyze High School students’ survey responses on college and career readiness skills. The variable, Self-Regulation, consisted of 5 items with a possible range from 5 to 25. The mean score was 20.47 with a standard deviation of 3.96, which indicated that HS students agreed with self-regulatory description of themselves. The variable, Cognitive Presence, consisted of 13 items with a possible range from 13 to 75. The mean score was 39.93 with a standard deviation of 8.63, which indicated that HS students were neutral with having behaviors of cognitive presence in their learning setting. The variable, Socially Active Learning, consisted of 11 items with a possible range from 11 to 55. The mean score was 42.95 with a standard deviation of 6.94, which
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indicated that HS students agreed with exhibiting socially active learning behaviors in their learning setting. The variable, Technology Beliefs, consisted of 11 items with a possible range 11 to 55. The mean score was 33.77 with a standard deviation of 9.11, which indicated that HS students were neutral with respect to their technology beliefs. The variable, Fear of Academic Failure, consisted of 4 items with a possible range from 4 to 20. The mean score was 13.31 with a standard deviation of 4.27, which indicated that HS students slightly less agreement with exhibiting fear of academic failure in their learning setting. Table 4.5 reports the descriptive statistics for High School students in each of the five variables. Table 4.5 Descriptive Statistics fo r HS Students in Each o f the Five College Career Readiness Variables
Self-Regulation Cognitive Presence Socially Active Learn Technology Beliefs Fear of Failure Valid N (listwise)
N 134 134 134 134 134 134
Number o f Items 5 13 11 11 4
Range 5-25 13-75 11-55 11-55 4-20
M
SD
20.47 39.93 42.95 33.77 13.31
3.96 8.63 6.94 9.11 4.27
Self-Regulation The variable of the college career readiness skill, Self-Regulation, consisted of five items. As shown in Table 4.6, more HS students Agree/Strongly Agree with all of the statements regarding Self-Regulation. The item analysis indicated 91.8 percent of
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High Schools students Agree/Strongly Agree with items 10 and 11, that is, the students’ primary goal was to do well in the class. Table 4.6 Items Analysis fo r Self-Regulation N=134 Item Number
Self-Regulation
7 My goal in this class is to get a better grade than most o f the other students 8 It is important for me to do well compared to others in this class 9 It is important for me to do better than other students. 10 I just want to avoid doing poorly in this class. 11
My goal in this class is to avoid performing poorly
SD+D
Neutral
A+SA
20.1
26.9
53.0
9.7
26.9
63.4
20.1
20.1
59.7
0.0
8.2
91.8
0.0
8.2
91.8
Cognitive Presence The variable of the college career readiness skill, Cognitive Presence, consisted of 13 items. Table 4.7 is an item analysis of the Cognitive Presence survey questions. A content analysis of the items reveals that 50 percent or more High School students responded Agree/Strongly Agree for items 17, 20, 23, 26. Item 54 revealed 76.9 percent o f High School students Strongly Disagreed/Disagreed with having to learn new technology skills to do well in the AP course. Analysis for items 16 and 18 revealed that High School students were mainly neutral about seeing problems posed increased interest and that they were motivated to explore content questions.
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Table 4.7 Items Analysis fo r Cognitive Presence N=134 Item Number Cognitive Presence SD+D Neutral
A+SA
16 I think problems posed increased my interest in course issues
19.4
47.8
32.8
17 I think course activities increased my curiosity
20.9
29.1
50.0
18 I was motivated to explore content related questions
19.4
47.0
33.6
19 I utilized a variety of information sources to explore problems
22.4
36.6
41.0
20
I found brainstorming and finding relevant information helped me to resolve content related questions
19.4
29.1
51.5
21
I think online discussions were valuable in helping me appreciate different perspectives
47.8
31.3
20.9
22
I think combining new information helped me answer questions raised in course activities
15.7
35.8
48.5
23
I think learning activities helped me construct explanations/solutions
11.2
30.6
58.2
26
I can apply the knowledge created in this course to my work or other non-related class activities
21.6
25.4
53.0
43
I value others when they contribute online
20.9
34.3
44.8
51
Compared to other students I really enjoy being in class
23.1
35.1
41.8
54 I needed to learn new technology skills to do well in this class
76.9
7.5
15.7
55 I learned new course content and new technology skills in this class
46.3
26.1
27.6
Socially Active Learning The variable of the college career readiness skill, Socially Active Learning, consisted of 11 items. Table 4.8 is a content analysis the items related to socially active learning. Every item indicated more than 51.5 percent of High School students
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Agree/Strongly Agree with them all and items 32, 37 and 41 were over 80 percent. Item 32 revealed no student felt uncomfortable interacting with other course participants. Table 4.8 Items Analysis fo r Socially Active Learning Behaviors N=134 Item Number
Socially Active Learning SD+D
Neutral
A+SA
28
I was able to form distinct impressions of some course participants
10.4
26.9
62.7
31
I am comfortable participating in the course discussions
10.4
17.9
71.6
32
I am comfortable interacting with other course participants I am comfortable disagreeing with other course participants while still maintaining a sense of trust.
0.0
18.7
81.3
18.7
15.7
65.7
33 34
I think that my point of view was acknowledged by other course participants
11.9
28.4
59.7
36
I sought to work with others in order to learn more
10.4
16.4
73.1
37
I offer information and ideas freely to others
5.2
14.2
80.6
38
When I need help I reach out to other students for help
11.9
24.6
63.4
39
I value the creativity of others
8.2
13.4
78.4
40
I feel that my creativity is valued by others
9.0
39.6
51.5
41
I find it important to collaborate with others
9.0
8.2
82.8
Technology Beliefs The variable o f the college career readiness skill, Technology Beliefs, consisted of 11 items. Table 4.9 is a content analysis of the items related to technology beliefs. Item 42 indicated that 51.5 percent were neutral about whether other course participants valued their online contributions and item 47 indicates that 61.9 percent of students do not believe they have learned new technology during this course. Items 50 and 52 indicate that although 50 percent o f students generally enjoy schoolwork better with
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technology, they do not prefer using technology to pen/pencils (53% Strongly Disagreed/Disagreed). Table 4.9 Items Analysis fo r Technology Beliefs N -1S4 Item Number
Technology SD+D
Neutral
A+SA
29 I think online or web based communication is an excellent medium for social interactions
34.3
20.1
45.5
30 I am comfortable conversing through the online medium 35 I think online discussions help me to develop a sense of collaboration
23.1
32.8
44.0
41.0
35.1
23.9
32.8
51.5
15.7
44 I think computers make schoolwork more fun/interesting
14.2
28.4
57.5
46
Computers help me to improve the quality of my schoolwork
28.4
23.1
48.5
47
I have learned new technology skills during this course
61.9
10.4
27.6
48
Computers help me understand my classes better
46.3
25.4
28.4
50 I generally enjoy schoolwork better with technology
29.1
20.9
50.0
52 I prefer to use computers to do schoolwork instead of using pencil and paper
53.0
10.4
36.6
53
10.4
8.2
81.3
42
I felt that when I participated online others valued my contribution
I consider my technology knowledge strong
Fear o f Academic Failure The variable, Fear of Academic Failure consisted of 4 items. Table 4.10 is a content analysis of the items. Most students (67.9%) Agree/Strongly Agree with item 12 revealing a fear of not fully understanding course content. Most students, 44.8 percent and 47.8 percent Strongly Disagreed/Disagreed with worrying about not learning all that
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they should in the class. Most students, 56.7 percent Agree/Strongly Agree with feeling that their technology knowledge did not change during the course. Table 4.10 Items Analysis fo r Fear o f Academic Failure N=134 Item Number Fear 12 Sometimes I am afraid that I may not understand the content of this class as thoroughly as I would like
SD+D 17.2
Neutral 14.9
A+SA 67.9
13 I worry that I may not learn all that I possibly could in this course
44.8
18.7
36.6
14 I am often concerned that I may not learn all that there is to learn in this class
47.8
17.2
35.1
58
25.4
17.9
56.7
I feel that my technology knowledge did not change during this course
Research Question Two Research question two asked, how do High School students differ in their CCR skills of Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors, Technology Beliefs and Fear of Failure in face-to-face and virtual AP course settings according to gender, and prior grade point average [GPA]? Descriptive statistics were utilized to determine the mean, range and standard deviation of the samples in face-toface and virtual AP course settings. Independent samples t tests were performed to compare CCR skills Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors, Technology Beliefs and Fear of Failure both AP course settings as well as prior GPA and gender. Table 4.11 shows the results for means; ranges and significance of HS students in face-to-face compared with the virtual AP course settings. A random sample of 35 was selected from the 102 face-to-face AP course sample set.
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Table 4.11
Independent Samples t test Comparing College Career Readiness Skills in the Face-to-Face (F2F) and Virtual (VAP) AP Course Settings Setting
N
M
SD
t
SelfRegulation
F2F
35
21.49
2.97
1.75
P .088
VAP
25
19.88
Cognitive Presence
F2F VAP
35 25
39.51 42.24
3.85 7.81 6.44
-1.48
.158
F2F VAP F2F VAP F2F VAP F2F VAP
35 25 35 25
33.66 34.68 12.97 15.56
7.28 9.64 3.49 4.90
-.47
.641
-2.26
.029
35 25
44.29 43.28 1.40 1.56
6.73 7.80 .50 .51 .49 .89
.52
.605
-1.22
.230
3.41
.002
Technology Fear of Failure Social Learning Gender Prior GPA
F2F VAP
35 25 35 25
4.63 3.96
Table 4.12 shows the results of the independent samples t tests. The mean for Virtual students in Fear o f Failure was higher than students in F2F. It was a significant difference in the two course settings for the variable Fear of Failure as seen in a p-value o f .029. The mean for the variable prior GPA was higher in the F2F setting, a significant difference as seen in a p-value of .002. Self-Regulation was approaching significance with p-value of .088 and the mean was higher in the F2F setting. The remaining variables, Cognitive Presence, Technology Beliefs, Socially Active Learning and Gender did not show significant differences between groups. Research Question Three Research question three asked, what is the relationship between the CCR dimensions o f Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors,
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Technology Beliefs and Fear of Failure, gender, and prior grade point average [GPA] in traditional face-to-face and virtual AP course settings? Two Pearson Correlation tests were performed, one for face-to-face and one for virtual course setting. Table 4.12 shows the results for each CCR variable, gender and prior GPA in the face-to-face AP course setting. The results indicated that Technology Beliefs and SelfRegulation had a moderately strong positive relationship (r = .304 with 9.24% of the variance) in the face-to-face AP course setting. There is a weak positive relationship between gender and Cognitive Presence (r= .204 with 4.1% of variance), gender and Technology Beliefs (r= .204 with 4.1% of variance), and gender and Fear of Failure (r= .221 with 4.9% of variance). The variable prior GPA had a weak positive relationship with variable SelfRegulation (r = .255 with 6.5% of the variance) and a moderate negative relationship with variable Fear of Failure (r = -.390 with 15.2% of the variance). The F2F correlation results between variables also indicated a moderately strong positive relationship between variables Socially Active Learning Behaviors and Cognitive Presence (r = .392 with 15.37% o f the variance). A weak positive relationship was seen between Socially Active Learning and gender (r = .221 with 4.9% of the variance) and a weak negative relationship with prior GPA (r = -.210 with 4.4% of the variance). The relationships are presented in the correlation results Table 4.12 for F2F setting AP course setting. A majority of the variables in the face-to-face AP course setting did not have significant relationships also noted in the correlation results of Table 4.12 for F2F AP course setting.
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Table 4.12
Correlation Among Variables in Face-to-Face AP Course Setting (N-104)
r r2 (%)
P N Technology r Beliefs r2 (%) P N r r2 (%)
Fear of Failure
P N r r2 (%)
Gender
Prior GPA
Socially Active Learning
P N r r2 (%) P N r r2 (%) P N
Prior GPA
i o
Cognitive Presence
SelfCognitive Technology Fear o f Gender Beliefs Failure Regulation Presence -.022 0.05% .821 104 .304” .105 9.24% 1.09% .002 .291 104 104 .027 -.136 1.85% 0.56% 0.07% .787 .168 .450 104 104 104 .204* .204* -.221* .033 4.1% 4.1% 4.9% .10% .024 .737 .038 .038 104 104 104 104 -390** -.004 .255** -.190 -.085 15.2% 3.6% .72% 6.5% 0% .009 .970 .055 .000 .394 103 103 103 103 103 _ .392 -.017 .099 .035 .221* 0.98% 0.12% 0.03% 15.37% 4.9% .000 .318 .726 .866 .024 104 104 104 104 104 **
-.210* 4.4% .034 103
**. C orrelation is sign ifican t a t th e 0 .0 1 lev el (2-tailed) a. S e ttin g = F2F
The correlation analyses of CCR skills of Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors, Technology Beliefs, Fear of Failure, and gender and prior GPA in the virtual AP course setting is shown in Table 4.13. Technology Beliefs and Self-Regulation had a moderately strong positive relationship (r = .652 with 41.51% of the variance) in the virtual AP course setting. Technology Beliefs had a strong positive relationship with Cognitive Presence (r = .829
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with 68.72% of the variance). Fear of Failure had strong negative relationship with Cognitive Presence (r = -.792 with 62.73% of the variance) and a moderate negative relationship with Technology Beliefs (r = -.626 with 39.19% of the variance). Gender had a very strong positive relationship with Self-Regulation (r = .932 with 6.86% of the variance), a moderate positive relationship with Technology Beliefs (r = .686 with 47.05% of the variance) and a moderate positive relationship with Fear of Failure (r = -.416 with 17.31% of the variance). Prior GPA had a negative moderate relationship with Technology Beliefs (r = -.420 with 17.64% of the variance). Socially Active Learning behaviors had moderately strong positive relationships with Self-Regulation (r = .468 with 31.82% of the variance). Socially Active Learning Behaviors also had a negative moderate relationship with Cognitive Presence (r= -.400 and 16% o f variance) and a positive moderate relationship with Fear of Failure (r = .546 with 30.58% of the variance) in the virtual AP course setting. The correlation results showed a majority of the variables Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors, Technology Beliefs and Fear of Failure as well as gender, and prior grade point average [GPA] had significant relationships. The correlation results between variables for the virtual AP course setting (VAP) are presented in Table 4.13.
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Table 4.13
Correlation Among Variables in Virtual AP Course Setting (N-25)
Cognitive Presence
r r2 (96)
P N Technology r Beliefs r2 (96) P N r rM%)
Fear of Failure
Gender
Prior GPA
Socially Active Learning
P N r r2 (96) P N r r2 (96) P N r r2 (%) P N
Self- Cognitive Technology Regulation Presence Beliefs .206 4.25 .323 25 .652** .829** 42.51 68.72 .000 .000 25 25 -.241 -.792” -.626” 39.19 5.80 62.73 .246 .000 .001 25 25 25 .932** .302 .686** 86.86% 9.1% 47.05% .000 .000 .142 25 25 25 -.330 -173 -420* 10.9% 12.6% 17.64% .107 .408 .037 25 25 25 .468* -.400* -.006 31.82 16.00 0.00 .977 .018 .048 25 25 25
Fear o f Failure
Gender
Prior GPA
-.416* 17.31% .038 25 -186 16.0% .374 25 .546*’ 30.58 .005 25
-.318 10.1% .121 25 .170 2.9% .418 25
-.143 2.0% .496 25
C orrelation is sign ifican t at th e 0 .01 lev el (2-tailed). *. C orrelation is sign ifican t at th e 0 .0 5 lev el (2-tailed).
**.
a. Setting = VAP
Research Question Four How do the CCR dimensions Self-Regulation, Cognitive Presence, Socially Active Learning Behaviors, Technology Beliefs and Fear of Failure gender, prior grade point average [GPA] and course grade earned predict High School students taking traditional face-to-face and virtual AP course settings?
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A forward stepwise (Wald) logistic regression analysis procedure was performed to determine whether High School students’ description of CCR dimensions and student demographic factors (gender and prior GPA) predict students taking traditional face-toface and virtual AP course settings. The forward stepwise regression model was employed, so that the predictive value of each variable could be determined. The logistic regression model was statistically significant, x2 (1) = 11-80, p < .003, as shown in Table 4.14 Table 4.14 Omnibus Tests o f Model Coefficients X2 Step 2
Step
6.280
df 1
Block Model
11.80 11.80
2 2
P .012 .003 .003
Table 4.15 showed the fit of the model and the percent of the variance accounted for by the model. The model explained 24 percent (Nagelkerke R2) of the variance in placement in face-to-face and virtual AP course settings. Table 4.15 Model Summary Step 2
-2 Log likelihood
Cox & Snell R2
Nagelkerke R2
69.705a
.179
.240
a. Estimation terminated at iteration number 5 because param eter estimates changed by less than .001.
Table 4.16 showed the ability of the logistic regression models to appropriately predict the placement of High School students in face-to-face and virtual AP course settings. The model correctly predicted placement in face-to-face AP course settings 74.3 percent o f the time. However, it predicted placement in virtual AP course settings 80
77
percent o f the time. As compared to Block 0, which is 58.3 percent, this model correctly predicted 76.7 percent of the placement of High School students in face-to-face and virtual AP course settings, which meant the regression predicted 18.4 percent better than the constant model. Table 4.16 Classification Tablea
Setting
Step 2
Setting F2F VAP 26 9 20 5
F2F VAP
Percentage Correct 74.3 80.0
Overall Percentage
76.7
a. The cut value is .500
Table 4.17 Variables in the Equation
Cognitive Presence Step 2b
Fear of Failure Constant
B
S.E.
Wald
.116
.051
.230 8.395
.081 2.949
p
Exp(B)
5.047
df 1
.025
1.123
8.066 8.105
1 1
.005 .004
1.258 .000
a. V aria b le s) entered on step 1: Fear o f Failure. b. Variable(s) entered on step 2: Cognitive Presence.
Table 4.17 displayed the statistics for the Step 2 regression model that predicted High School students in face-to-face and virtual AP course settings. Cognitive Presence and Fear of Failure are constant. Cognitive Presence and Fear of Failure were the sole predictors of placement in face-to-face and virtual AP course settings. The Exp(B) value of item Fear of Failure (.230) indicated that for each one-point increase on that item for a High School student, the probability of being placed in a face-to-face AP course setting increased by 23 percent. Fear of Failure was the strongest predictor for placement in a
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face-to-face AP course setting. That is, Fear of Failure, those students who considered worrying about not learning all the content they were supposed to learn was a determining factor in whether they took the AP course in the face-to-face or virtual setting. The logistical regression analysis found that because of the level of significance for Cognitive Presence and Fear of Failure, the CCR variables Self-Regulation, Socially Active Learning, and Technology Beliefs were not added to the prediction model because they were not significant. Research Question Five How do High School students in traditional face-to-face and virtual AP course settings describe their course experience? Table 4.18 included the number o f responses included and omitted according to course setting. Themes and word frequencies were then analyzed. Table 4.18 Number o f Participants ’ Completion o f Open-Ended Question by Course Setting Item Response Omitted Item Response Included N
F2F 24 80 104
VAP 5 20 25
Missing 0 5 5
High School students taking the AP course in the F2F and VAP settings responded to an open-ended question with a variety of key words. O f the 104 F2F students, 80 responded to the open-ended question ranging between a one-sentence to a paragraph with several sentences. Of the 25 students taking the AP course in the virtual setting, 20 responded to the open-ended question. These responses were regarding a description of the course experience, though other comments were included as well.
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A chart summarizing key word frequency from both settings is outlined in Table 4.19. Table 4.19 Key Word/Theme Responses for F2F and VAP course settings
Harder Content Stimulating/Good Importance of teacher effect Discussion Improved Organization More Work Technology Benefits Fast Pace First Time Total Open Response
VAP (n=20) 0 3 0 1 1 5 5 0 5 20
F2F (n=80) 11 g 18 3 0 14 14 6 6 80
High School students in both F2F and Virtual AP course settings stated that there was a greater workload, that the courses were stimulating and had some level of technology benefit. Students in the F2F setting noted discussions as important and valuable while one student in the Virtual setting said, “I have taken two other courses but this is the first online. I miss the class discussion sometimes but I mostly just want to get the assignments done.” Only F2F students sighted teachers as an important aspect of the AP course experience, with sixteen noting that it “depended” on the teacher as to a positive experience. One student in the Virtual said the experience increased organization skills. Research Question Six How do High School students’ characteristics of gender, grade level, prior grade point average [GPA] and course grade earned compare in traditional face-to-face and virtual AP course settings?
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Two - Two way ANOVA were conducted and the factors will be Gender and Course setting, dependent variable 1) prior GPA and 2) Grade Earned. A 2 X 2 ANOVA was utilized to compare HS student prior GPA by gender and AP course settings. The results of the ANOVA examining gender, setting and the interaction of gender and setting with prior GPA are presented in Table 4.20. The AP course setting was significant in terms of prior GPA with a p-value of .001 and gender was approaching significance with a p-value of .071. Table 4.20 Two by Two ANOVA Between Groups Comparing prior GPA fo r HS AP Students by Gender and AP Course Setting
Source Corrected Model Intercept Gender Setting gender * Setting Error Total Corrected Total
Type III Sum o f Squares
df
Mean Square
F
P
8.516a
3
2.839
6.325
.001
1054.442 1.519 5.563 .763 25.134 1169.000 33.650
1 1 1 1 56 60 59
1054.442 1.519 5.563 .763 .449
2349.338 3.383 12.393 1.699
.000 .071 .001 .198
a. R Squared = .253 (Adjusted R Squared = .213)
The estimated marginal means for the variable prior GPA are presented in Table 4.21. It should be noted that females had higher means than males. This indicates that females had higher GPAs going into both AP course settings. In the virtual AP course setting there was a greater difference in the prior GPA between males and females than in the face-to-face AP course setting. Table 4.21 Estimated Marginal Means fo r prior GPA
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95% Confidence Interval Setting
Gender
Mean_______ SEM_______ Lower Bound
Upper Bound
Female
4.667
.146
4.374
4.960
Male Female
4.571 4.273
.179 .202
4.213 3.868
4.930 4.677
Male
3.714
.179
3.356
4.073
A 2 X 2 ANOVA was utilized to compare HS student AP Course Grade by gender and AP course settings. The results of the ANOVA examining gender, setting and the interaction of gender and setting with AP Course Grade are presented in Table 4.22. The results reveal significance for Gender (p=.013) setting (p=026) and the greatest significance seen in the interaction of Gender and AP course setting (p=.008). Table 4.22 Two by Two ANOVA Between Groups Comparing AP Course Grade fo r HS AP Students
by Gender and AP Course Setting Type III Sum
Source Corrected Model Intercept Gender Setting Gender * Setting Error Total Corrected Total
o f Squares 4.156a 542.325 2.306 1.833 2.608 15.864 765.000 20.020
