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The Impact of Connecting Worked Examples and Completion Problems for Introductory Programming Practice

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Technology Enhanced Learning for Inclusive and Equitable Quality Education (EC-TEL 2024)

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Abstract

Worked examples have consistently demonstrated their value in education, serving as the model solutions for solving specific problem types. Past studies indicate that combining worked examples with practice problems is more effective than providing either problems or examples in isolation. Despite these findings, the exploration of the effects of grouping worked examples and problems for programming practice is limited, especially in learning environments designed for practice. This paper compares two content organization approaches in a practice system. The first one is explicitly connecting worked examples and completion problems, allowing students to access them in smaller bundles. The other one is delivering the same set of activities separately but keeping an implicit connection by grouping them under a topic. We examined the effects of these two approaches on student engagement and performance in a semester-long classroom experiment conducted in a CS1 programming course. The results indicate that explicitly connecting worked examples and completion problems increased engagement with the completion problems and supported problem-solving performance by leading to higher success rates and persistence.

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Notes

  1. 1.

    https://acos.cs.hut.fi/python-parser.

References

  1. Bates, D., Mächler, M., Bolker, B., Walker, S.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1) (2015)

    Google Scholar 

  2. Brusilovsky, P., et al.: Increasing adoption of smart learning content for computer science education. In: Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference, pp. 31–57 (2014)

    Google Scholar 

  3. Brusilovsky, P., Malmi, L., Hosseini, R., Guerra, J., Sirkiä, T., Pollari-Malmi, K.: An integrated practice system for learning programming in python: design and evaluation. Res. Pract. Technol. Enhan. Learn. 13(18), 18.1–18.40 (2018)

    Google Scholar 

  4. Brusilovsky, P., Sosnovsky, S.: Individualized exercises for self-assessment of programming knowledge: an evaluation of quizpack. ACM J. Educ. Res. Comput. 5(3), 6 (2005)

    Article  Google Scholar 

  5. Brusilovsky, P., Yudelson, M.: From webex to navex: Interactive access to annotated program examples. Proc. IEEE 96(6), 990–999 (2008)

    Article  Google Scholar 

  6. Cafolla, R.: Project merlot: bringing peer review to web-based educational resources. J. Technol. Teach. Educ. 14(2), 313–323 (2006)

    Google Scholar 

  7. Chen, X., Mitrovic, A., Matthews, M.: Learning from worked examples, erroneous examples and problem solving: towards adaptive selection of learning activities. IEEE Trans. Learn. Technol. 13(1), 135–149 (2020)

    Article  Google Scholar 

  8. Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R.: Self-explanations: how students study and use examples in learning to solve problems. Cogn. Sci. 13(2), 145–182 (1989)

    Google Scholar 

  9. Edwards, S.H., Murali, K.P.: Codeworkout: short programming exercises with built-in data collection. In: 2017 Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE 2017, pp. 188–193. ACM Press (2017)

    Google Scholar 

  10. Ericson, B., Guzdial, M., Morrison, B., Parker, M., Moldavan, M., Surasani, L.: An ebook for teachers learning CS principles. ACM Inroads 6(4), 84–86 (2015)

    Article  Google Scholar 

  11. Ferguson, R., Sharples, M.: Innovative pedagogy at massive scale: teaching and learning in MOOCs. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) EC-TEL 2014. LNCS, vol. 8719, pp. 98–111. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11200-8_8

    Chapter  Google Scholar 

  12. Gaweda, A.M., Lynch, C.F.: Student practice sessions modeled as ICAP activity silos. In: 14th International Conference on Educational Data Mining (2021)

    Google Scholar 

  13. Guo, P.: Online python tutor: embeddable web-based program visualization for cs education. In: the 44th ACM Technical Symposium on Computer Science Education (SIGCSE 2016), pp. 579–584. ACM Press (2013)

    Google Scholar 

  14. Hicks, A., Akhuseyinoglu, K., Shaffer, C., Brusilovsky, P.: Live catalog of smart learning objects for computer science education. In: Sixth SPLICE Workshop Building an Infrastructure for Computer Science Education Research and Practice at Scale at ACM Learning at Scale 2020 (2020)

    Google Scholar 

  15. Hosseini, R., et al.: Improving engagement in program construction examples for learning python programming. Int. J. Artif. Intell. Educ. 30(2), 299–336 (2020)

    Article  Google Scholar 

  16. Hosseini, R., Brusilovsky, P.: A study of concept-based similarity approaches for recommending program examples. New Rev. Hypermedia Multimedia 23(3), 161–188 (2017)

    Article  Google Scholar 

  17. Ihantola, P., Ahoniemi, T., Karavirta, V., Seppälä, O.: Review of recent systems for automatic assessment of programming assignments. In: Koli Calling 2010: Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pp. 86–93. ACM (2010)

    Google Scholar 

  18. Janssenswillen, G., Depaire, B., Swennen, M., Jans, M., Vanhoof, K.: bupaR: enabling reproducible business process analysis. Knowl.-Based Syst. 163, 927–930 (2019)

    Article  Google Scholar 

  19. Kalyuga, S., Chandler, P., Tuovinen, J., Sweller, J.: When problem solving is superior to studying worked examples. J. Educ. Psychol. 93(3), 579–588 (2001)

    Article  Google Scholar 

  20. Kuznetsova, A., Brockhoff, P., Christensen, R.: lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82(13), 1–26 (2017)

    Article  Google Scholar 

  21. Laakso, M.J., Kaila, E., Rajala, T.: Ville-collaborative education tool: designing and utilizing an exercise-based learning environment. Educ. Inf. Technol. 23(4), 1655–1676 (2018)

    Article  Google Scholar 

  22. Mihalca, L., Mengelkamp, C., Schnotz, W., Paas, F.: Completion problems can reduce the illusions of understanding in a computer-based learning environment on genetics. Contemp. Educ. Psychol. 41, 157–171 (2015)

    Article  Google Scholar 

  23. Najar, A.S., Mitrovic, A., McLaren, B.M.: Learning with intelligent tutors and worked examples: selecting learning activities adaptively leads to better learning outcomes than a fixed curriculum. User Model. User-Adap. Inter. 26(5), 459–491 (2016)

    Article  Google Scholar 

  24. Parsons, D., Haden, P.: Parson’s programming puzzles: a fun and effective learning tool for first programming courses. In: Proceedings of the 8th Australasian Conference on Computing Education, ACE 2006, vol. 52, pp. 157–163. Australian Computer Society, Inc. (2006)

    Google Scholar 

  25. Renkl, A.: Worked-out examples: instructional explanations support learning by self-explanations. Learn. Instr. 12(5), 529–556 (2002)

    Article  Google Scholar 

  26. Shaffer, C.: OpenDSA: An interactive etextbook for computer science courses. In: Proceedings of the 47th ACM Technical Symposium on Computing Science Education, pp. 5–5. ACM (2016)

    Google Scholar 

  27. Sharrock, R., Hamonic, E., Hiron, M., Carlier, S.: Codecast: an innovative technology to facilitate teaching and learning computer programming in a c language online course. In: Proceedings of the 4th ACM Conference on Learning at Scale, pp. 147–148. ACM (2017)

    Google Scholar 

  28. Soloway, E.: From problems to programs via plans: the content and structure of knowledge for introductory lisp programming. J. Educ. Comput. Res. 1(2), 157–172 (1985)

    Article  Google Scholar 

  29. Sorva, J., Karavirta, V., Malmi, L.: A review of generic program visualization systems for introductory programming education. ACM Trans. Comput. Educ. 13(4), 1–64 (2013)

    Article  Google Scholar 

  30. Sweller, J.: Cognitive load theory, learning difficulty, and instructional design. Learn. Instr. 4(4), 295–312 (1994)

    Article  Google Scholar 

  31. Weber, G.: Episodic learner modeling. Cogn. Sci. 20(2), 195–236 (1996)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Kamil Akhuseyinoglu & Peter Brusilovsky

  2. University of Novi Sad, Novi Sad, Serbia

    Aleksandra Klašnja-Milicevic

Authors
  1. Kamil Akhuseyinoglu
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  2. Aleksandra Klašnja-Milicevic
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  3. Peter Brusilovsky
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Corresponding author

Correspondence to Kamil Akhuseyinoglu .

Editor information

Editors and Affiliations

  1. Universidade Federal Rural de Pernambuco, Recife, Pernambuco, Brazil

    Rafael Ferreira Mello

  2. Ruhr University Bochum, Bochum, Germany

    Nikol Rummel

  3. FernUniversität in Hagen, Hagen, Germany

    Ioana Jivet

  4. University for Continuing Education Krems, Krems an der Donau, Austria

    Gerti Pishtari

  5. University of Murcia, Murcia, Spain

    José A. Ruipérez Valiente

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Akhuseyinoglu, K., Klašnja-Milicevic, A., Brusilovsky, P. (2024). The Impact of Connecting Worked Examples and Completion Problems for Introductory Programming Practice. In: Ferreira Mello, R., Rummel, N., Jivet, I., Pishtari, G., Ruipérez Valiente, J.A. (eds) Technology Enhanced Learning for Inclusive and Equitable Quality Education. EC-TEL 2024. Lecture Notes in Computer Science, vol 15159. Springer, Cham. https://doi.org/10.1007/978-3-031-72315-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-72315-5_1

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