An Inductive Learning Strategy

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St. John Fisher College. Strong, R., Silver, H., Perini, M., & Tuculescu, G. (2003). Boredom and its opposite. Educational Leadership, 61(1), 24–29. Group.
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ScienceDirect Procedia - Social and Behavioral Sciences 197 (2015) 1847 – 1852

7th World Conference on Educational Sciences, (WCES-2015), 05-07 February 2015, Novotel Athens Convention Center, Athens, Greece

The Development of Self-Expressive Learning Material for Algebra Learning: An Inductive Learning Strategy Nur Azlina Mohamed Mokmina*, Mona Masooda a

Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia, Malaysia

Abstract Researchers have proven that students learn best when there is a personalization in learning. Personalization may be attained by considering the individual’s learning styles. In this study, the Math Learning Style Inventory was administered to assess students’ mathematical learning styles. This inventory suggests that when learning mathematics, there are four learning styles including Mastery, Understanding, Self-Expressive and Interpersonal. This paper discusses the Self-Expressive learning material that was developed for students with the Self-Expressive learning style. Students with this preferred learning style tend to like mathematics problems that allow them to think differently by using visualization techniques to solve the problems, generating possible solutions, and exploring alternatives to the given problem. An inductive learning strategy was chosen in the development of the multimedia application in learning algebra. Thirty polytechnic students who were enrolled in an engineering program were given a set of pre- and post-test to measure the effectiveness of the learning material in improving students' understanding of the topic. Results showed that students who studied the learning material according to their preferred learning style obtained better results than the students with the randomized learning material. © 2015 2015The TheAuthors. Authors.Published Published Elsevier © byby Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under the responsibility of Academic World Education and Research Center. Peer-review under responsibility of Academic World Education and Research Center. Keywords: Learning Style, Self-Expressive, Inductive Learning, Algebra

1. Introduction Researchers in education have observed differences between individuals when the learning process occurs. These differences are referred to as learning styles. Ismail, Hussain, and Jamaluddin (2010) defined the term learning styles as the way a learner approach learning situations. In the learning style theory, individuals are expected to

* Nur Azlina Mohamed Mokmin. Tel.:+60143303404. E-mail address: [email protected]

1877-0428 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Academic World Education and Research Center. doi:10.1016/j.sbspro.2015.07.245

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prefer different ways in the process of learning. By knowing students’ learning style preferences, educators are able to apply specific techniques and learning strategies that can give the most impact to one’s educational satisfaction. When students are aware of their specific learning style, they will have the ability to know their strengths and weaknesses when it comes to learning. By knowing the students learning styles, teachers and lecturers can apply the most suitable learning strategy that can increase students achievement (Romanelli, Bird, & Ryan, 2009). Researchers such as Dorca, Lima, Fernandes, and Lopes (2013) and Tawil et al. (2012) also pointed out that a serious mismatch between the students’ learning style and learning strategies can cause students to feel bored, be uncomfortable, and have poor achievement. A survey done by Hodgen & Marks (2013) on the importance of mathematics for employment shows that there are strong needs of mathematics in various jobs especially those related to engineering and science. They recommend that it is crucially important that every student have the understanding of basic mathematics concept. By having a strong foundation in mathematics, students can avoid the difficulty of facing complex skills that require them to apply mathematical knowledge (Wood, 2011). Strong, Silver, Perini, and Tuculescu (2003) as well as Zhang and Stephens (2013) stated that teachers need to acknowledge that there exist diverse approaches to the process of learning mathematics. The utilization of technologies in helping students learn have been going on for decades. Researches such as Hrubik-Vulanovic (2013), Melis & Siekmann (2004), Walker, Rummel, & Koedinger (2013) have developed applications that intended to increase learning achievement in mathematics. Koedinger, Anderson, Hadley, and Mark, (1997) developed an Intelligent Tutoring System (ITS) that can help students learn algebra by using real world problems. Melis & Siekmann (2004) developed ACTIVEMATH that can support online mathematics learning. These developed systems have proven to increase students’ achievement score in learning mathematics. Therefore, this study will discuss the development of a multimedia application that is used for learning mathematics by utilizing the information of the students’ learning style. The learning style that is discussed for this paper is SelfExpressive. 2. Literature Review The applications of the learning style theory in the development of a multimedia application is not a new idea. Among the commonly used learning styles are Felder-Silvermen Learning Style, Myer-Briggs Type Indicator, Honey and Mumford Index of Learning Styles and Keefe Learning Style Theory. Silver et al. (2008), developed the Math Learning Style Inventory (MLSI) which classifies students into four learning styles when learning mathematics. The four learning styles are Mastery, Understanding, Self-Expressive and Interpersonal. Thomas, Brunstings, and Warrick (2010) stated that a good problem solving requires all four styles of thinking, therefore good mathematics learning is when learning styles and strategies comes together. The Mastery learning style students like questions that must be solved by certain procedures. They will face difficulty with problems that are not routine to them. The students that prefer Understanding learning style like mathematics problems that requires them to prove how the solution works. They will experience difficulty when the questions requires them to collaborate with others in order to solve the problems. The Self-Expressive learning style students like problems that needs them to think creatively. They will face difficulty when the questions focus on practices to solve the questions. The Interpersonal learning style students like questions that need them to collaborate with others. They will face difficulty with problems that lack real world applications. 2.1. Self-Expressive Learning Style Students with Self-Expressive learning style tends to visualize or create images and pursue multiple strategies (Rubio, 2007). Gaikwad (2010) suggest that for Self-Expressive students to excel in mathematics, the learning materials must be able to help students imagine the mathematical concept. The learning material must also be able to engage students to the research related to the currect topic. Students must be able to see the learning materials in art forms. For this study, the students with Self-Expressive learning style preference will be presented with SelfExpressive learning material.

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Learning materials for Self-Expressive students must consist of materials that need to use imagination and consist of routine instruction and repetition practice (Thomas et al., 2010). Thomas et al. (2010) have come out with four learning strategies that are suitable for Self-Expressive learning style students. The strategies are Metaphorical Expression, 3-D Viewer, Modeling and Experimentation, and Inductive Learning. The strategy that is best suited for this study is the Inductive Learning Strategy. 2.2. Inductive Learning Strategy Inductive learning is learning a hyphothesis from a collection of examples (Onta, 2010). Inductive learning started with the general information such as a case study, observation, experimental data, scenario or word problems (Prince & Felder, 2006). The students can either solve the problems on their own or by guidance from the teachers. This method is known to be able to increase students achievement because students are able to construct the required knowledge from pieces of information given to them. The induction process, which is moving from specific to general, is a natural thinking process. Thomas et al. (2010) stated that the human brain is designed to look for something that connects them in a thread of meaning when confronted with multiple pieces of information. Algebra has been chosen as the main topic and Simplifying Algebraic Fractions is the subtopic. There are three learning outcomes for this study. Students should be able to (i) differentiate between monomial fractions and polynomial fractions, (ii) simplify algebraic fractions with one parenthesis, and (iii) simplify algebraic fractions by using factorization technique. 3. The Application Development The graphics used for this learning material illustrated three scenes that the students need to explore; (i) The Office, (ii) My Study Table, and (iii) The Park. Figure 1 shows the scenes that are created for this application. For every scene, there are clues hiding in the items that are placed randomly. Students have to find the clues by clicking each item in the scenes. Students are then needed to construct their own understanding based on the collected information. The clues in every scene will form the required knowledge so that the specific learning outcomes can be achieved by the end of each lesson. In Scene One, students have to collect information from office supplies that located in the office. After they have completed scene one, students should be able to differentiate between monomial fractions and polynomial fractions and also able to simplify algebraic fractions with one parenthesis. In the Scene Two, students are needed to collect clues from stationeries and books on a study table. Students are expected to achieve the same learning outcomes as in scene one. The last scene, which is the park scene is where the students are expected to achieve learning outcome three. In this scene, clues on how to simplify algebraic fractions using factorizations have been placed between the items that we usually found in a park. Figure 2 shows the main interface of the Self-Expressive learning material. From the main interface, students are free to investigate any screen they want in order to find all the clues. Figure 3 shows one of the clues from the scene.

Fig. 1. The Scenes for The Learning Materials

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Fig.2. The Main Menu of the Self-Expressive Learning Material

Fig. 3.One of the Clue in the Self-Expressive Learning Material

4. Result The respondents in this study were chosen from 250 semester 1 polytechnic students who were enrolled in the engineeering programs. The students were required to complete a set of MLSI. By using the result from the MLSI, 30 students with the Self-Expressive learning style were chosen and divided equally into two groups. Both groups were first given a set of pre-trest that consist of 10 questions. Then, the first group was presented by the selfexpressive learning material and the second group was given the randomized learning material that is not mapped to their learning style. After the learning sessions were completed, these students were asked to complete a set of posttest. The learning gained from the pre-test and post-test were calculated and analyzed using SPSS. An Independent samples t-test was used for the analysis. The result shows that the group that was presented by Self-Expressive learning material that was mapped to their learning style shows a higher mean of learning gain than the group with the learning material that is randomly selected. Table 1 and 2 show results of the analysis.

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Table 1. Result of the Learning Gain.

Group

Number of Students

Mean

Std. Deviation

1

15

20

4.25

2

15

2

6.21

Table 2. Result of the Learning Gain.

t-Test for Equality of Means 95% Confidence Interval of

t Equal variances assumed Equal variances not assumed

df

Sig. (2-

Mean

Std. Error

tailed)

Difference

Difference

the Difference Lower

Upper

2.378

29

.024

18.12500

7.62215

2.53595

33.71405

2.409

26.207

.023

18.12500

7.52402

2.66511

33.58489

5. Conclusion The findings of the study show that students who were presented with the learning material that was mapped to their learning styles show a significantly higher learning gain than students who were presented with the randomized learning material that were not mapped to their learning style. The result also shows that the students’ understanding of the subtopic simplify algebraic fractions is increased when they are presented with the learning material that are mapped to their learning style. This study can hopefully give new insight to the curriculum developer and educator especially for the polytechnics in Malaysia to incorporate the learning style theory in the curriculum development process. References Dorca, F. A., Lima, L. V., Fernandes, M. A., & Lopes, C. R. (2013). Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Systems with Applications, 40(6), 2092–2101. doi:10.1016/j.eswa.2012.10.014 Gaikwad, S. (2010). Improving students’ mathematical thinking. International Forum, 13(2), 83–92. Hodgen, J., & Marks, R. (2013). The employment equation : Why our young people need more maths for today’s jobs (p. 26). Hrubik-Vulanovic, T. (2013). Effects of intelligent tutoring systems in basic algebra courses on subsequent mathematics lecture courses. (Unpublished doctoral dissertation). Kent State University. Ismail, A., Hussain, R. M. R., & Jamaluddin, S. (2010). Assessment of students’ learning styles preferences in the faculty of science, Tishreen University, Syria. Procedia - Social and Behavioral Sciences, 2(2), 4087–4091. doi:10.1016/j.sbspro.2010.03.645 Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43. Melis, E., & Siekmann, J. (2004). ActiveMath : An intelligent tutoring system for mathematics. Springer, 3070(91-101). Onta, S. (2010). Multiagent inductive learning : an argumentation-based approach. Proceeding of the 27th International Conference on Machine Learning, (2). Prince, M. J., & Felder, R. M. (2006). To state a theorem and then to show examples of it is literally to teach backwards. J. Engr. Education, 95(2), 123–138. Romanelli, F., Bird, E., & Ryan, M. (2009). Learning styles: a review of theory, application, and best practices. American Journal of Pharmaceutical Education, 73(1), 9. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2690881&tool=pmcentrez&rendertype=abstract Rubio, M. (2007). The impact of learning styles on boy ’ s motivation and its effect on reading performance the impact of learning styles on boy ’ s motivation and its effect. Education Masters. St. John Fisher College. Strong, R., Silver, H., Perini, M., & Tuculescu, G. (2003). Boredom and its opposite. Educational Leadership, 61(1), 24–29.

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Nur Azlina Mohamed Mokmin and Mona Masood / Procedia - Social and Behavioral Sciences 197 (2015) 1847 – 1852 Tawil, N. M., Zaharim, A., Asshaari, I., Nopiah, Z. M., Ismail, N. A., & Osman, H. (2012). A study on engineering undergraduate learning styles towards mathematics engineering. Procedia - Social and Behavioral Sciences, 60, 212–220. doi:10.1016/j.sbspro.2012.09.370 Thomas, E. J., Brunstings, J. R., & Warrick, P. L. (2010). Styles and strategies for teaching high school mathematics:21 techniques for differentiating instruction and assessment. (C. Hernandez, Ed.) (1st ed., p. 209). California: Corwin. Walker, E., Rummel, N., & Koedinger, K. R. (2013). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24(1), 33–61. doi:10.1007/s40593-013-0001-9 Wood, L. N. (2011). Practice and conceptions: communicating mathematics in the workplace. Educational Studies in Mathematics, 79(1), 109– 125. doi:10.1007/s10649-011-9340-3 Zhang, Q., & Stephens, M. (2013). Personalized education and the teaching and learning of mathematics: an Australian perspective, 6 (2), 48–57.