RESEARCH PAPER
Enhancing Understanding through the Use of Structured Representations
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National Taipei University, New Taipei, TAIWAN
 
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National Taiwan Normal University, Taipei, TAIWAN
 
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National Chiao Tung University, Hsinchu, TAIWAN
 
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National HsinChu Commercial & Vocational High School, HsinChu, TAIWAN
 
 
Online publication date: 2018-02-18
 
 
Publication date: 2018-02-18
 
 
EURASIA J. Math., Sci Tech. Ed 2018;14(5):1875-1886
 
KEYWORDS
ABSTRACT
Mathematical representations are an essential tool in the study of mathematics and problem solving. They are also used in word problems to facilitate the transformation from textual to symbolic information. We proposed a stepwise, blocked, structured state transition graph (STG) based on the principles of instructional message design. In this study, we adopted a posttest-only non-equivalent group design to compare the performance of students who used either STG or matrix-like tables to learn to solve word problems via transition matrices. We also took into account the student’s previous learning achievements in mathematics. The participants included four classes of senior students in a vocational high school, with two classes randomly designated as the experiment (STG) group and two designated as the control (Table) group. High-achieving students taught using STG outperformed their counterparts who were taught using matrix-like tables. The performance of low-achieving students appeared to be unaffected by the instructional method. These findings suggest that STG provides a clear representation of the relationships used in matrix calculation, which makes it easier to select and organize information. Nonetheless, alternative methods will be required to improve the performance of low-achieving students.
 
REFERENCES (58)
1.
Ahmad, A., Tarmizi, R. A., & Nawawi, M. (2010). Visual representations in mathematical word problem solving among form four students in Malacca. Procedia Social and Behavioral Sciences, 8, 356-361. https://doi.org/10.1016/j.sbsp....
 
2.
Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33, 131-152. https://doi.org/10.1016/S0360-....
 
3.
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183-198. https://doi.org/10.1016/j.lear....
 
4.
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp.89-195). New York, NY: Academic Press. https://doi.org/10.1016/S0079-....
 
5.
Ayres, P. (2006). Impact of reducing intrinsic cognitive load on learning in mathematical domain. Applied Cognitive Psychology, 20, 287-298. https://doi.org/10.1002/acp.12....
 
6.
Ayres, P. (2013). Can the isolated-elements strategy be improved by targeting points of high cognitive load for additional practice? Learning and Instruction, 23, 115-124. https://doi.org/10.1016/j.lear....
 
7.
Bernardo, A. B. T. (1999). Overcoming obstacles to understanding and solving word problems in mathematics. Educational Psychology, 19(2), 149-163. https://doi.org/10.1080/014434....
 
8.
Brenner, M. E., Mayer, R. E., Moseley, B., Brar, T., Durán, R., Reed, B. S., & Webb, D. (1997). Learning by understanding: The role of multiple representations in learning algebra. American Educational Research Journal, 34(4), 663-689. https://doi.org/10.3102/000283....
 
9.
Chen, M. J., Lee, C. Y., Lei, K. H., Tso, T. Y., & Lin S. L. (2017). Multimedia instruction presented by integrated context to enhance understanding of compass-and-straightedge construction. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3735-3752. https://doi.org/10.12973/euras....
 
10.
Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research and Development, 53(3), 15-24. https://doi.org/10.1007/BF0250....
 
11.
Common Core State Standards Initiative. (2010). Common Core State Standards for mathematics. Retrieved from http://www.corestandards.org/w....
 
12.
Crooks, S. M., Cheon, J., Inan, F., Ari, F., & Flores, R. (2012). Modality and cueing in multimedia learning: Examining cognitive and perceptual explanations for the modality effect. Computers in Human Behavior, 28, 1063-1071. https://doi.org/10.1016/j.chb.....
 
13.
Cummins, D. D., Kintsch, W., Reusser, K., & Weimer, R. (1988). The role of understanding in solving word problems. Cognitive Psychology, 20, 405-438. https://doi.org/10.1016/0010-0....
 
14.
de Bock, D., van Dooren, W., & Verschaffel, L. (2013). Students’ understanding of proportional, inverse proportional, and affine functions: Two studies of the role of external representations. International Journal of Science and Mathematics Education, 13(Suppl 1), 47-69. https://doi.org/10.1007/s10763....
 
15.
de Corte, E., Verschaffel, L., & de Win, L. (1985). Influence of rewording verbal problems on children’s problem representations and solutions. Journal of Educational Psychology, 77(4), 460-470. https://doi.org/10.1037//0022-....
 
16.
de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010). Learning by generating vs. receiving instructional explanations: Two approaches to enhance attention cueing in animations. Computers & Education, 55, 681-691. https://doi.org/10.1016/j.comp....
 
17.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Education Studies in Mathematics, 61, 103-131. https://doi.org/10.1007/s10649....
 
18.
Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 32(2), 124-158. https://doi.org/10.2307/749671.
 
19.
Goldstein, E. B. (2014). Sensation and perception (9th ed.). Belmont, CA: Wadsworth.
 
20.
Hegarty, M., & Kozhevnikov, M. (1999). Types of visual-spatial representations and mathematical problem solving. Journal of Educational Technology Psychology, 91(4), 684-689. https://doi.org/10.1037/0022-0....
 
21.
Hong, W., Thong, J. Y. L., & Tam, K. Y. (2004). Does animation attract online users’ attention? The effects of flash on information search performance and perceptions. Information Systems Research, 15(1), 60-86. https://doi.org/10.1287/isre.1....
 
22.
Jamet, E., Gavota, M., & Quaireau, C. (2008). Attention guiding in multimedia learning. Learning and Instruction, 18, 135-145. https://doi.org/10.1016/j.lear....
 
23.
Jitendra, A. K., Griffin, C. C., Haria, P., Leh, J., Adams, A., & Kaduvettoor, A. (2007). A comparison of single and multiple strategy instruction on third-grade students’ mathematical problem solving. Journal of Educational Psychology, 99(1), 115-127. https://doi.org/10.1037/0022-0....
 
24.
Jitendra, A. K., Star, J. R., Rodriguez, M., Lindell, M., & Someki, F. (2011). Improving students’ proportional thinking using schema-based instruction. Learning and Instruction, 21, 731-745. https://doi.org/10.1016/j.lear....
 
25.
Jupri, A., & Drijvers, P. (2016). Student difficulties in mathematizing word problems in algebra. Eurasia Journal of Mathematics, Science & Technology Education, 12(9), 2481-2502. https://doi.org/10.12973/euras....
 
26.
Kester, L., Kirschner, P. A., & van Merriënboer, J. J. G. (2006). Just-in-time information presentation: Improving learning a troubleshooting skill. Contemporary Educational Psychology, 31, 167-185. https://doi.org/10.1016/j.cedp....
 
27.
Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academy Press.
 
28.
Koedinger, K. R., & Nathan, M. J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. The Journal of the Learning Science, 13(2), 129-164. https://doi.org/10.1207/s15327....
 
29.
Lee, C. Y., & Chen, M. J. (2015). Effects of Polya questioning instruction for geometry reasoning in junior high school. Eurasia Journal of Mathematics, Science and Technology Education, 11(6), 1547-1561. https://doi.org/10.12973/euras....
 
30.
Lee, C. Y., & Chen, M. J. (2016). Developing a questionnaire on technology-integrated mathematics instruction: A case study of the AMA training course in Xinjiang and Taiwan. British Journal of Educational Technology, 47(6), 1287-1303. https://doi.org/10.1111/bjet.1....
 
31.
Lesh, R., Post, T., & Behr, M. (1987). Representations and translations among representations in mathematics learning and problem solving. In C. Janvier (Ed.), Problems of representations in the teaching and learning of mathematics (pp. 33-40). Hillsdale, NJ: Lawrence Erlbaum.
 
32.
Lewis, A. B. (1989). Training students to represent arithmetic word problems. Journal of Educational Psychology, 81(4), 521-531. https://doi.org/10.1037/0022-0....
 
33.
Litvinova, E. (2014). Adding another row to the time-distance-speed diagram. Mathematics Teaching, 240, 47-50.
 
34.
Llinares, S., & Roig, A. I. (2006). Secondary school students’ construction and use of mathematical models in solving word problems. International Journal of Science and Mathematics Education, 6(3), 505-532. https://doi.org/10.1007/s10763....
 
35.
Luzón, J. M., & Letón, E. (2015). Use of animated text to improve the learning of basic mathematics. Computers & Education, 88, 119-128.
 
36.
Marcus, N., Cooper, M., & Sweller, J. (1996). Understanding instructions. Journal of Educational Psychology, 88(1), 49-63. https://doi.org/10.1037//0022-....
 
37.
Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York, NY: Cambridge University Press. https://doi.org/10.1017/CBO978....
 
38.
Müller, H. J., & Krummenacher, J. (2006). Visual search and selective attention. Visual Cognition, 14, 389-410. https://doi.org/10.1080/135062....
 
39.
Mullis, I. V. S., & Martin, M. O. (Eds.) (2013). TIMSS 2015 Assessment Frameworks. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College and International Association for the Evaluation of Educational Achievement.
 
40.
OECD. (2016). PISA 2015 assessment and analytical framework: Science, reading, mathematics, and financial literacy. Paris, France: Author. https://doi.org/10.1787/978926....
 
41.
Pashler, H. (1988). Cross-dimensional interaction and texture segregation. Perception & Psychophysics, 43(4), 307-318. https://doi.org/10.3758/BF0320....
 
42.
Pavlin-Bernadrić, N., Vlahović-Štetić, V., & Arambašić, L. (2008). Children’s solving of mathematical word problems: The contribution of working memory. Review of Psychology, 15(1-2), 35-43.
 
43.
Plass, J. L., Homer, B. D., & Hayward, E. O, (2009). Design factors for educationally effective animations and simulations. Journal of Computing in Higher Education, 21(1), 31-61. https://doi.org/10.1007/s12528....
 
44.
Pólya, G. (1945). How to solve it. Princeton, NJ: Princeton University Press.
 
45.
Presmeg, N. C. (1986). Visualisation in high school mathematics. For the Learning of Mathematics, 6(3), 42-46.
 
46.
Pretorius, A. J. (2008). Visualization of state transition graphs. Eindhoven, The Netherlands: Technische Universiteit Eindhoven.
 
47.
Riley, M. S, Greeno, J. G., & Heller, J. I. (1983). Development of children’s problem-solving ability in arithmetic. In H. P. Ginsburg (Ed.), The development of mathematical thinking (pp. 153-196). New York, NY: Academic Press.
 
48.
Sepeng, P., & Sigola, S. (2013). Making sense of errors made by learners in mathematical word problem solving. Mediterranean Journal of Social Sciences, 4(13), 325-333. https://doi.org/10.5901/mjss.2....
 
49.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin Company.
 
50.
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295-312. https://doi.org/10.1016/0959-4....
 
51.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer. https://doi.org/10.1007/978-1-....
 
52.
Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology: General, 119(2), 176-192. https://doi.org/10.1037/0096-3....
 
53.
Treisman, A. (1986). Features and objects in visual processing. Scientific American, 255(5), 114-125. https://doi.org/10.1038/scient....
 
54.
Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 136. https://doi.org/10.1016/0010-0....
 
55.
Underwood, J., & Underwood, G. (1987). Data organisation and retrieval by children. British Journal of Educational Psychology, 57(3), 313-329. https://doi.org/10.1111/j.2044....
 
56.
Ware, C. (2013). Information visualization: Perception for design (3rd ed.). Waltham, MA: Elsevier. https://doi.org/10.1016/B978-0....
 
57.
Wolfe, J. M. (1998). Visual search: A review. In H. Pashler (Ed.), Attention (pp. 13-77). London, UK: University College London Press.
 
58.
Yantis, S., & Jonides, J. (1990). Abrupt visual onsets and selective attention: Voluntary versus automatic allocation. Journal of Experimental Psychology: Human Perception and Performance, 16(1), 121-134. https://doi.org/10.1037/0096-1....
 
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