UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
Studying worked examples has been found to be effective for learning problem solving, especially among students. However, students need to actively process example content to benefit from it and content must be structured in a manner that facilities knowledge construction. This study investigated the use of worked examples for teaching and learning programming. Programming involves problem analysis and solution generation. But students tend to jump to solution generation without adequately analysing the problem. Consequently, the current study designed and implemented a new worked example design that emphasised problem analysis and utilised highlighting through web technology to encourage active processing of example content. This study also evaluated the new design in a quasi‐experiment in a university course in Malaysia, compared to subgoal labelled worked examples, and conducted over three sessions. Posttest performance was analysed using independent samples t‐test and frequency distributions. The results suggested that worked examples based on the new design were more effective than subgoal labelled worked examples, with statistically significant difference in performance, and medium effect size for the first session. For the second and third sessions, performance was marginally better, with learning in both groups possibly limited by the complexity of the worked examples and assessments |
References |
1. Altintas, T., Gunes, A., & Sayan, H. (2016). A peer‐assisted learning experience in computer programming language learning and developing computer programming skills. Innovations in Education and Teaching International, 53(3), 329‐337. doi: 10.1080/14703297.2014.993418. 2. Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: instructional principles from the worked examples research. Review of Educational Research, 70(2), 181. doi:10.2307/1170661. 3. Atkinson, R. K., Catrambone, R., & Merrill, M. M. (2003). Aiding transfer in statistics: examining the use of conceptually oriented equations and elaborations during subgoal learning. Journal of Educational Psychology, 95(4), 762‐773. doi: 10.1037/0022‐0663.95.4.762. 4. Bester, G., & Brand, L. (2013). The effect of technology on learner attention and achievement in the classroom. South African Journal of Education, 33(2), pp. 1‐15. 5. Cardellini, L. (2014). Problem solving: how can we help students overcome cognitive difficulties. Journal of Technology and Science Education, 4(4), 237‐249. doi: 10.3926/jotse.121. 6. Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355‐376. doi:10.1037/0096‐3445.127.4.355. 7. Dos Santos, M. T., Vianna Jr, A. S., & Le Roux, G. A. (2018). Programming skills in the industry 4.0: are chemical engineering students able to face new problems?. Education for Chemical Engineers, 22, 69‐76. 8. Driscoll, M. P. (2005). Psychology of learning for instruction, (3rd ed.) Boston: Pearson. 9. Dunn, T. J., & Kennedy, M. (2019). Technology enhanced learning in higher education; motivations, engagement and academic achievement. Computers & Education, 137, 104‐113. 10. Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1‐4. doi: 10.11648/j.ajtas.20160501.11 11. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technologymediated learning: A review. Computers & Education, 90, 36-53. 12. Jamet, E., & Fernandez, J. (2016). Enhancing interactive tutorial effectiveness through visual cueing. Educational Technology Research and Development, 64(4), 631‐641. doi:10.1007/s11423‐016‐9437‐6. 13. Lin, L., Atkinson, R. K., Savenye, W. C., & Nelson, B. C. (2014). Effects of visual cues and self‐explanation prompts: empirical evidence in a multimedia environment. Interactive Learning Environments, 24(4), 799‐813. doi:10.1080/10494820.2014.924531. 14. Loksa, D., & Ko, A. J. (2016, August). The role of self‐regulation in programming problem solving process and success. Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 83‐91). ACM. 15. Malhotra, V. M., & Anand, A. (2019). Teaching a university‐wide programming laboratory: managing a C programming laboratory for a large class with diverse interests. Proceedings of the Twenty‐First Australasian Computing Education Conference on ‐ ACE '19. doi:10.1145/3286960.3286961. 16. Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42, 58‐71. doi:10.1016/j.learninstruc.2015.12.002. 17. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.‐D., Laxer, C., Thomas, L., Utting, I., Wilusz, T. (2001). A multi‐national, multi‐institutional study of assessment of programming skills of first‐year CS students. Working group reports from ITiCSE on Innovation and Technology in Computer Science Education ‐ ITiCSE‐WGR '01. doi:10.1145/572134.572137. 18. Moreno, R. (2006). When worked examples don't work: Is cognitive load theory at an Impasse? Learning and Instruction, 16(2), 170‐181. doi:10.1016/j.learninstruc.2006.02.006. 19. Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. Proceedings of the eleventh annual International Conference on International Computing Education Research ‐ ICER '15. doi:10.1145/2787622.2787733. 20. Mulder, Y. G., Lazonder, A. W., & De Jong, T. (2014). Using heuristic worked examples to promote inquiry‐based learning. Learning and Instruction, 29, 56‐64. doi:10.1016/j.learninstruc.2013.08.001. 21. Patitsas, E., Craig, M. & Easterbrook, S. (2013). Comparing and contrasting different algorithms leads to increased student learning. Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research , San Diego, 145‐152. doi: 10.1145/2493394.2493409. 22. Renkl, A. (2014). Toward an instructionally oriented theory of example‐based learning. Cognitive Science, 38(1), 1‐37. doi:10.1111/cogs.12086. 23. Renkl, A. (2017). Learning from worked‐examples in mathematics: students relate procedures to principles. ZDM, 49(4), 571‐584. doi:10.1007/s11858‐017‐0859‐3. 24. Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer‐based technology and student engagement: a critical review of the literature. International Journal of Educational Technology in Higher Education, 14(1), 25. 25. Schoenfeld, A. H. (1992). Learning to think mathematically: problem solving, metacognition, and sense making in mathematics. D. Grouws (Ed.) Handbook for Research on Mathematics Teaching and Learning. NY: Macmillan, 334‐370. 26. Shi, J., Sha, A., Hedman, G., & Rourke, E. O. (2019). Pyrus: designing a collaborative programming game to support problem‐solving behaviors. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Glasgow, Paper No. 656. doi: 10.1145/3290605.3300886. 27. Spector, J. M. (2013). Trends and Research Issues in Educational Technology. Malaysian Online Journal of Educational Technology, 1(3), 1-9. 28. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295‐312. doi:10.1016/0959‐4752(94)90003‐5. 29. Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59‐89. doi:10.1207/s1532690xci0201_3. 30. Van Gog, T., Kester, L., & Paas, F. (2011). Effects of worked examples, example‐problem, and problem‐example pairs on novices’ learning. Contemporary Educational Psychology, 36(3), 212‐218. doi:10.1016/j.cedpsych.2010.10.004. 31. Van Merriënboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: recent developments and future directions. Educational Psychology Review, 17(2), 147‐177. doi:10.1007/s10648‐005‐3951‐0. |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |