Exploring Two Worked Example Designs for Learning Introductory Programming from Students’ Perspectives
DOI:
https://doi.org/10.53797/jthkkss.v1i2.3.2020Keywords:
Worked examples, signalling, programming educationAbstract
Worked examples are effective for learning problem solving but, only if students engage with the content. An approach to promote engagement is through signalling. This study compared worked example designs for learning introductory programming using two approaches for signaling: labelled and visualised. It explored students’ preferences and perceptions of the designs through a crossover design where students were exposed to both worked example designs. Data was collected through a questionnaire. Quantitative analysis showed that more students favoured visualised design. Qualitative analysis showed that students found both designs helped to understand the solution. Additionally, visualised worked examples also helped in understanding the problem, the relationship between problem and solution, as well as the programming process. Other differences were also identified.
Downloads
References
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. https://doi.org/10.1037/0022-0663.95.4.762
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. http://doi.org/10.1037/0096-3445.127.4.355
Denny, P., Becker, B. A., Craig, M., Wilson, G., & Banaszkiewicz, P. (2019, July). Research this! Questions that computing educators most want computing education researchers to answer. In Proceedings of the 2019 ACM Conference on International Computing Education Research (pp. 259-267). https://doi.org/10.1145/3291279.3339402
Hanks, B., & Brandt, M. (2009). Successful and unsuccessful problem solving approaches of novice programmers. ACM SIGCSE Bulletin, 41(1), 24-28. http://doi.org/10.1145/1508865.1508876
Johnson, B. and Christensen, L. (2014) Educational Research: Quantitative, Qualitative, and Mixed Approaches, 5th ed., SAGE Publications.
Lemarié, J., Lorch Jr, R. F., Eyrolle, H., & Virbel, J. (2008). SARA: A text-based and reader-based theory of signaling. Educational Psychologist, 43(1), 27-48. https://doi.org/10.1080/00461520701756321
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
Loksa, D., & Ko, A. J. (2016, August). The role of self-regulation in programming problem solving process and success. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 83-91). ACM. https://doi.org/10.1145/2960310.2960334
Lorch, R., Lemarié, J., & Grant, R. (2011). Signaling hierarchical and sequential organization in expository text. Scientific Studies of Reading, 15(3), 267–284. http://doi.org/10.1080/10888431003747535
Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction, 42, 58–71. http://doi.org/10.1016/j.learninstruc.2015.12.002
Margulieux, L. E., Catrambone, R., & Guzdial, M. (2016). Employing subgoals in computer programming education. Computer Science Education, 26(1), 44-67. https://doi.org/10.1080/08993408.2016.1144429
Mathieson, K. (2012). Exploring student perceptions of audiovisual feedback via screencasting in online courses. American Journal of Distance Education, 26(3), 143-156. https://doi.org/10.1080/08923647.2012.689166
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2019). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2), 77-90. https://doi.org/10.1109/TE.2018.2864133
Moreno, R. (2006). When worked examples don’t work: Is cognitive load theory at an Impasse? Learning and Instruction, 16(2 SPEC. ISS.), 170–181. http://doi.org/10.1016/j.learninstruc.2006.02.006
Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. International Computing Education Research Conference (ICER), 21–29. http://doi.org/10.1145/2787622.278773
Morrison, B. B., Margulieux, L. E., Ericson, B., & Guzdial, M. (2016, February). Subgoals help students solve Parsons problems. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (pp. 42-47). https://doi.org/10.1145/2839509.2844617
Nugroho, M. A., Setyorini, D., & Novitasari, B. T. (2019). The role of satisfaction on perceived value and e-learning usage continuity relationship. Procedia Computer Science, 161, 82-89. https://doi.org/10.1016/j.procs.2019.11.102
Ozcelik, E., Arslan-Ari, I., & Cagiltay, K. (2010). Why does signaling enhance multimedia learning? Evidence from eye movements. Computers in Human Behavior, 26(1), 110–117. http://doi.org/10.1016/j.chb.2009.09.001
Peart, D. J., Rumbold, P. L., Keane, K. M., & Allin, L. (2017). Student use and perception of technology enhanced learning in a mass lecture knowledge-rich domain first year undergraduate module. International Journal of Educational Technology in Higher Education, 14(1), 40. https://doi.org/10.1186/s41239-017-0078-6
Prunuske, A. J., Henn, L., Brearley, A. M., & Prunuske, J. (2016). A randomized crossover design to assess learning impact and student preference for active and passive online learning modules. Medical Science Educator, 26(1), 135- 141. https://doi.org/10.1007/s40670-015-0224-5
Renkl, A. (2014). Toward an instructionally oriented theory of example‐based learning. Cognitive Science, 38(1), 1-37. https://doi.org/10.1111/cogs.12086
Schneider, S., Beege, M., Nebel, S., & Rey, G. D. (2018). A meta-analysis of how signaling affects learning with media. Educational Research Review, 23, 1-24. https://doi.org/10.1016/j.edurev.2017.11.001
Schreier, M. (2012). Qualitative Content Analysis in Practice. SAGE Publications.
Selby, C. C. (2015, November). Relationships: computational thinking, pedagogy of programming, and Bloom's Taxonomy. In Proceedings of the workshop in primary and secondary computing education (pp. 80-87). https://doi.org/10.1145/2818314.2818315
Skudder, B., & Luxton-Reilly, A. (2014, January). Worked examples in computer science. In Proceedings of the Sixteenth Australasian Computing Education Conference-Volume 148 (pp. 59-64). Australian Computer Society, Inc..
Smith, A. R., Cavanaugh, C., & Moore, W. A. (2011). Instructional multimedia: An investigation of student and instructor attitudes and student study behavior. BMC Medical Education, 11(1), 38. https://doi.org/10.1186/1472-6920- 11-38
Yuriev, E., Naidu, S., Schembri, L. S., & Short, J. L. (2017). Scaffolding the development of problem-solving skills in chemistry: guiding novice students out of dead ends and false starts. Chemistry Education Research and Practice, 18(3), 486-504. https://doi.org/10.1039/C7RP00009J
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Mariam Nainan, Balamuralithara Balakrishnan, Ahmad Zamzuri Mohamad Ali
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
