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Effects of Prior Knowledge on Mathematics Different Order Thinking Skills in Mobile Multimedia Environments

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Mobile Learning Design

Part of the book series: Lecture Notes in Educational Technology ((LNET))

Abstract

This chapter presents a study that examined the effects of prior knowledge and multimedia design on developing mathematical conceptual understanding in a mobile learning environment. Two different approaches—instructional and noninstructional—were used in the design of the multimedia representation to facilitate students learning for a more complete understanding. Seventy students with different levels of prior knowledge in a secondary school participated in the experiment. Participants were assigned to the 2 (high vs. low prior knowledge group) × 2 (instructional vs. noninstructional) factorial groups to receive the 100-min treatment. The results revealed that the low prior knowledge group outperformed the high prior knowledge group in conceptual knowledge of low order thinking; the instructional group outperformed than the noninstructional group in conceptual knowledge of high order thinking and procedural knowledge; and there was no interaction of prior knowledge and design approach. These findings suggest that mobile multimedia environment enhancing viewing is sufficient for the low order thinking skill development, but not for the high order in mathematics concept learning and procedural skill. Finally, recommendations for future research were suggested.

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References

  • Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33, 131–152.

    Article  Google Scholar 

  • Albers, M., & Kim, L. (2001). Information design for the small-screen interface: An overview of web design issues for personal digital assistants. Technical Communications, 49(1), 45–60.

    Google Scholar 

  • Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14(3), 325–341.

    Article  Google Scholar 

  • Boticki, I., Baksa, J., Seow, P., & Looi, C. K. (2015). Usage of a mobile social learning platform with virtual badges in a primary school. Computers & Education, 86, 120–136.

    Article  Google Scholar 

  • Chen, N. S., Hsieh, S. W., & Kinshuk, K. (2008). Effects of short-term memory and content representation type on mobile language learning. Language Learning & Technology, 12(3), 93–113.

    Google Scholar 

  • Chiu, T. K. F., & Churchill, D. (2015a). Design of learning objects for concept learning: Effects of multimedia learning principles and an instructional approach. Interactive Learning Environment, 1, 1–16. doi:10.1080/10494820.2015.1006237.

    Google Scholar 

  • Chiu, T. K. F., & Churchill, D. (2015b). Exploring the characteristics of an optimal design of digital materials for concept learning in mathematics: Multimedia learning and variation theory. Computers & Education, 82, 280–291. doi:10.1016/j.compedu.2014.12.

    Article  Google Scholar 

  • Churchill, D. (2007). Towards a useful classification of learning objects. Education Technology Research and Development, 55, 479–497.

    Article  Google Scholar 

  • Churchill, D. (2011). Conceptual model learning objects and design recommendations for small screens. Educational Technology & Society, 14, 203–216.

    Google Scholar 

  • Churchill, D. (2013). Conceptual model design and learning uses. Interactive Learning Environments, 21, 54–67.

    Article  Google Scholar 

  • Churchill, D., & Hedberg, G. (2008). Learning object design considerations for small-screen handheld devices. Computers & Education, 50(3), 881–893.

    Article  Google Scholar 

  • Clark, R. E. (1994). Media will never influence learning. Education Technology Research and Development, 42, 21–29.

    Article  Google Scholar 

  • Curriculum Development Council, & Hong Kong Examinations and Assessment Authority. (2007). The new senior secondary mathematics curriculum and assessment guide (secondary 4–6). Hong Kong, China: The Government Printer.

    Google Scholar 

  • Doolittle, P. E. (2002, August). Multimedia learning: Empirical results and practical applications. In The proceedings of the Irish Educational Technology Users’ Conference.

    Google Scholar 

  • Fletcher, J. D., & Tobias, S. (2005). The multimedia principle. The Cambridge Handbook of Multimedia Learning, 117, 133.

    Google Scholar 

  • Gay, G., Stefanone, M., Grace-Martin, M., & Hembrooke, H. (2001). The effects of wireless computing in collaborative learning environments. International Journal of Human-Computer Interaction, 13(2), 257–276.

    Article  Google Scholar 

  • Gedik, N., Hanci-Karademirci, A., Kursun, E., & Cagiltay, K. (2012). Key instructional design issues in a cellular phone-based mobile learning project. Computers & Education, 58(4), 1149–1159.

    Article  Google Scholar 

  • Gu, L., Huang, R., & Marton, F. (2004). Teaching with variation: A Chinese way of promoting effective mathematics learning. In L. Fan, N. Y. Wong, J. Cai, & S. Li (Eds.), How Chinese learn mathematics: Perspectives from insiders (pp. 309–347). Singapore: World Scientific.

    Chapter  Google Scholar 

  • Guo, J. P., Yang, L. Y. & Ding, Y. (2013). Effects of example variability and prior knowledge in how students learn to solve equations. European Journal of Psychology of Education, 29, 1–22.

    Google Scholar 

  • International Telecommunication Union. (2012). Measuring the Information Society. Retrieved May 15, 2014, from http://www.itu.int/en/ITU-/Statistics/Documents/publications/mis2012/MIS2012_without_Annex_4.pdf.

  • Kalyuga, S. (2014). The expertise reversal principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 576–597). New York: Cambridge University Press.

    Chapter  Google Scholar 

  • Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92(1), 126.

    Article  Google Scholar 

  • Kastberg, S. (2003). Using Bloom’s taxonomy as a framework for classroom assessment. Mathematics Teacher, 96, 402–405.

    Google Scholar 

  • Lee, C. Y., & Chen, M. P. (2009). A computer game as a context for non-routine mathematical problem solving: The effects of type of question prompt and level of prior knowledge. Computers & Education, 52(3), 530–542.

    Article  Google Scholar 

  • Lee, K., & Bahn, H. (2005). A compressed display technique for 240 x 320 resolution personal digital assistants (PDAs). IEEE Transactions on Consumer Electronics, 51(4), 1268–1272.

    Article  Google Scholar 

  • Leslie, K. C., Low, R., Jin, P., & Sweller, J. (2012). Redundancy and expertise reversal effects when using educational technology to learn primary school science. Educational Technology Research and Development, 60(1), 1–13.

    Article  Google Scholar 

  • Levin, J. R., Anglin, G. J., & Carney, R. N. (1987). On empirically validating functions of pictures in prose. The Psychology of Illustration, 1, 51–85.

    Article  Google Scholar 

  • Liu, T. C., Lin, Y. C., & Paas, F. (2013). Effects of prior knowledge on learning from different compositions of representations in a mobile learning environment. Computers & Education, 72, 328–338

    Google Scholar 

  • Low, R., & Sweller, J. (2005). The modality principle in multimedia learning. The Cambridge handbook of multimedia learning, 147, 158.

    Google Scholar 

  • Lusk, D. L., Evans, A. D., Jeffrey, T. R., Palmer, K. R., Wikstrom, C. S., & Doolittle, P. E. (2009). Multimedia learning and individual differences: Mediating the effects of working memory capacity with segmentation. British Journal of Educational Technology, 40(4), 636–651.

    Article  Google Scholar 

  • Mampadi, F., Chen, S., & Ghinea, G. (2009). The effects of prior knowledge on the use of adaptive hypermedia learning systems. In Human-computer interaction. Interacting in various application domains (pp. 156–165). Springer, Berlin

    Google Scholar 

  • Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational psychologist, 32(1), 1–19.

    Article  Google Scholar 

  • Mayer, R. E. (2009). Multimedia learning. New York: Cambridge Press.

    Book  Google Scholar 

  • Mok, I. A. C. (2009). Learning of algebra inspiration from students’ understanding of the distributive law. Hong Kong, China: Hong Kong Association for Mathematics Education.

    Google Scholar 

  • Mok, I. A. C., & Lopez-Real, F. (2006). A tale of two cities: A comparison of six teachers in Hong Kong and Shanghai. In D. Clarke, C. Keitel, & Y. Shimizu (Eds.), Mathematics classrooms in 12 countries: The insiders’ perspective (pp. 237–246). Rotterdam, Netherlands: Sense Publishers B.V.

    Google Scholar 

  • National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author.

    Google Scholar 

  • Potelle, H., & Rouet, J. F. (2003). Effects of content representation and readers’ prior knowledge on the comprehension of hypertext. International Journal of Human-Computer Studies, 58, 327–345.

    Article  Google Scholar 

  • Rey, G. D., & Fischer, A. (2013). The expertise reversal effect concerning instructional explanations. Instructional Science, 41(2), 407–429.

    Article  Google Scholar 

  • Riffell, S., & Sibley, D. (2005). Using web-based instruction to improve large undergraduate biology courses: An evaluation of a hybrid course format. Computers & Education, 44(3), 217–235.

    Article  Google Scholar 

  • Rittle-Johnson, B., & Star, J. R. (2007). Does comparing solution methods facilitate conceptual and procedural knowledge? An experimental study on learning to solve equations. Journal of Educational Psychology, 99(3), 561–574.

    Article  Google Scholar 

  • Rittle-Johnson, B., & Star, J. R. (2009). Compared with what? The effects of different comparisons on conceptual knowledge and procedural flexibility for equation solving. Journal of Educational Psychology, 101(3), 529–544.

    Article  Google Scholar 

  • Sankey, M., Birch, D., & Gardiner, M. (2012). The impact of multiple representations of content using multimedia on learning outcomes across learning styles and modal preferences. International Journal of Education and Development using ICT, 7(3), 18–35.

    Google Scholar 

  • Schneider, M., & Stern, E. (2005). Conceptual and procedural knowledge of a mathematics problem: Their measurement and their causal interrelations. In Proceedings of the 27th Annual Conference of the Cognitive Science Society.

    Google Scholar 

  • Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156.

    Article  Google Scholar 

  • Schnotz, W., & Lowe, R. (2003). External and internal representations in multimedia learning. Learning and Instruction, 13(2), 117–123.

    Article  Google Scholar 

  • Spanjers, I. A. E., Wouters, P., Van Gog, T., & Van Merrienboer, J. J. G. (2011). An expertise reversal effect of segmentation in learning from animated worked-out examples. Computers in Human Behavior, 27(1), 46–52.

    Article  Google Scholar 

  • Stokes, S. (2002). Visual literacy in teaching and learning: A literature perspective. Electronic Journal for the Integration of technology in Education, 1(1), 10–19.

    Google Scholar 

  • Thompson, T. (2008). Mathematics teachers’ interpretation of higher-order thinking in Bloom’s taxonomy. International Electronic Journal of Mathematics Education, 3, 96–109.

    Google Scholar 

  • Usiskin, Z. (1999). Conceptions of school algebra and uses of variables. In B. Moses (Ed.), Algebraic thinking, grades k–12: Readings from NTCM’s school-based journals and other publications (pp. 7–13). Reston, VA: National Council of Teachers of Mathematics.

    Google Scholar 

  • Wagner, E. D. (2002). The new frontier of learning object design. The E-Learning Developers’ Journal.

    Google Scholar 

  • Wiredu, G. (2005, August). The reconstruction of portable computers: On the flexibility of mobile computing in mobile activities. Paper presented at IFIP 8.2 Conference on Designing Ubiquitous Information Environments, Cleveland, OH.

    Google Scholar 

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Correspondence to Thomas K. F. Chiu .

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Chiu, T.K.F. (2016). Effects of Prior Knowledge on Mathematics Different Order Thinking Skills in Mobile Multimedia Environments. In: Churchill, D., Lu, J., Chiu, T., Fox, B. (eds) Mobile Learning Design. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-0027-0_22

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  • DOI: https://doi.org/10.1007/978-981-10-0027-0_22

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