Mining student coding behaviors in a programming MOOC: there are no actionable learner stereotypes
DOI:
https://doi.org/10.55056/etq.611Keywords:
individual differences, Java, MOOC, student modelingAbstract
Education often involves categorizing students into broad groups based on perceived attributes like academic abilities, learning pace, and unique challenges. However, the validity and applicability of these stereotypes require closer examination. This research investigates student grouping factors, exploring both conventional variables like gender and education level, as well as innovative methodologies that utilize students’ problem-solving behaviors. The study critically evaluates the effectiveness of these grouping techniques in capturing and distinguishing students’ diverse learning patterns. Through a comprehensive analysis of ten methodologies used to cluster students in traditional programming courses and programming MOOCs, we aim to reveal how students from different cohorts exhibit varying learning behaviors and outcomes. By examining diverse models of student learning, we assess whether students in distinct groups indeed demonstrate discernible disparities in their educational journeys. Our meticulous data analysis uncovers compelling insights that challenge the notion of predefined student stereotypes and their practical utility within group-based adaptation settings. This research contributes to the discourse on student grouping by highlighting the limitations of traditional categorizations and introducing innovative approaches to understanding student diversity and tailoring educational interventions accordingly. By transcending simplistic generalizations, we strive to foster a nuanced understanding of students’ individual strengths, challenges, and potentials, promoting inclusive and effective educational practices.
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Ayres, J., Flannick, J., Gehrke, J. and Yiu, T., 2002. Sequential PAttern Mining Using a Bitmap Representation. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery, KDD ’02, p.429–435. Available from: https://doi.org/10.1145/775047.775109. DOI: https://doi.org/10.1145/775047.775109
Baker, R.S.J.D. and Yacef, K., 2009. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), p.3–17. Available from: https://doi.org/10.5281/zenodo.3554657.
Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D. and Seaton, D.T., 2013. Studying Learning in the Worldwide Classroom Research into edX’s First MOOC. Research & Practice in Assessment, 8, pp.13–25. Available from: https://www.rpajournal.com/dev/wp-content/uploads/2013/05/SF2.pdf.
Champaign, J., Colvin, K.F., Liu, A., Fredericks, C., Seaton, D. and Pritchard, D.E., 2014. Correlating Skill and Improvement in 2 MOOCs with a Student’s Time on Tasks. Proceedings of the First ACM Conference on Learning @ Scale Conference. New York, NY, USA: Association for Computing Machinery, L@S ’14, p.11–20. Available from: https://doi.org/10.1145/2556325.2566250. DOI: https://doi.org/10.1145/2556325.2566250
Corbett, A.T. and Anderson, J.R., 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), pp.253–278. Available from: https://doi.org/10.1007/BF01099821. DOI: https://doi.org/10.1007/BF01099821
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R. and Lin, C.J., 2008. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 9(61), pp.1871–1874. Available from: http://jmlr.org/papers/v9/fan08a.html.
Ferguson, R. and Buckingham Shum, S., 2012. Social Learning Analytics: Five Approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York, NY, USA: Association for Computing Machinery, LAK ’12, p.23–33. Available from: https://doi.org/10.1145/2330601.2330616. DOI: https://doi.org/10.1145/2330601.2330616
Fournier-Viger, P., Lin, J.C., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z. and Lam, H.T., 2016. The SPMF Open-Source Data Mining Library Version 2. In: B. Berendt, B. Bringmann, É. Fromont, G.C. Garriga, P. Miettinen, N. Tatti and V. Tresp, eds. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part III. Springer, Lecture Notes in Computer Science, vol. 9853, pp.36–40. Available from: https://doi.org/10.1007/978-3-319-46131-1_8. DOI: https://doi.org/10.1007/978-3-319-46131-1_8
Glassman, E.L., Scott, J., Singh, R., Guo, P.J. and Miller, R.C., 2015. OverCode: Visualizing Variation in Student Solutions to Programming Problems at Scale. ACM Transactions on Computer-Human Interaction, 22(2), p.7. Available from: https://doi.org/10.1145/2699751. DOI: https://doi.org/10.1145/2699751
Guerra, J., Sahebi, S., Lin, Y. and Brusilovsky, P., 2014. The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises. In: J.C. Stamper, Z.A. Pardos, M. Mavrikis and B.M. McLaren, eds. Proceedings of the 7th International Conference on Educational Data Mining, EDM 2014, London, UK, July 4-7, 2014. International Educational Data Mining Society (IEDMS), pp.153–160. Available from: https://www.researchgate.net/publication/262967165.
Guo, P.J. and Reinecke, K., 2014. Demographic Differences in How Students Navigate through MOOCs. Proceedings of the First ACM Conference on Learning @ Scale Conference. New York, NY, USA: Association for Computing Machinery, L@S ’14, p.21–30. Available from: https://doi.org/10.1145/2556325.2566247. DOI: https://doi.org/10.1145/2556325.2566247
Hood, N. and Littlejohn, A., 2016. MOOC Quality: the need for new measures. Journal of Learning for Development, 3(3). Available from: https://doi.org/10.56059/jl4d.v3i3.165. DOI: https://doi.org/10.56059/jl4d.v3i3.165
Hosseini, R. and Brusilovsky, P., 2013. JavaParser: A Fine-Grain Concept Indexing Tool for Java Problems. In: E. Walker and C. Looi, eds. Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, Memphis, USA, July 9-13, 2013. CEUR-WS.org, CEUR Workshop Proceedings, vol. 1009. Available from: https://ceur-ws.org/Vol-1009/0907.pdf.
Hosseini, R., Vihavainen, A. and Brusilovsky, P., 2014. Exploring Problem Solving Paths in a Java Programming Course. In: B. du Boulay and J. Good, eds. Proceedings of the 25th Annual Workshop of the Psychology of Programming Interest Group, PPIG 2014, Brighton, UK, June 25-27, 2014. Psychology of Programming Interest Group, p.9. Available from: https://ppig.org/papers/2014-ppig-25th-hosseini/.
Huang, J., Piech, C., Nguyen, A. and Guibas, L.J., 2013. Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC. In: E. Walker and C. Looi, eds. Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, Memphis, USA, July 9-13, 2013. CEUR-WS.org, CEUR Workshop Proceedings, vol. 1009. Available from: https://ceur-ws.org/Vol-1009/0105.pdf.
Murphy, C., Kaiser, G.E., Loveland, K. and Hasan, S., 2009. Retina: helping students and instructors based on observed programming activities. In: S. Fitzgerald, M. Guzdial, G. Lewandowski and S.A. Wolfman, eds. Proceedings of the 40th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2009, Chattanooga, TN, USA, March 4-7, 2009. ACM, pp.178–182. Available from: https://doi.org/10.1145/1508865.1508929. DOI: https://doi.org/10.1145/1539024.1508929
Paas, F.G.W.C. and Van Merriënboer, J.J.G., 1993. The Efficiency of Instructional Conditions: An Approach to Combine Mental Effort and Performance Measures. Human Factors, 35(4), pp.737–743. Available from: https://doi.org/10.1177/001872089303500412. DOI: https://doi.org/10.1177/001872089303500412
Pavlik, P.I., Cen, H. and Koedinger, K.R., 2009. Performance Factors Analysis - A New Alternative to Knowledge Tracing. In: V. Dimitrova, R. Mizoguchi, B. du Boulay and A.C. Graesser, eds. Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, Proceedings of the 14th International Conference on Artificial Intelligence in Education, AIED 2009, July 6-10, 2009, Brighton, UK. IOS Press, Frontiers in Artificial Intelligence and Applications, vol. 200, pp.531–538. Available from: https://doi.org/10.3233/978-1-60750-028-5-531.
Perkins, D.N., Hancock, C., Hobbs, R., Martin, F. and Simmons, R., 1986. Conditions of Learning in Novice Programmers. Journal of Educational Computing Research, 2(1), pp.37–55. Available from: https://doi.org/10.2190/GUJT-JCBJ-Q6QU-Q9PL. DOI: https://doi.org/10.2190/GUJT-JCBJ-Q6QU-Q9PL
Piech, C., Sahami, M., Koller, D., Cooper, S. and Blikstein, P., 2012. Modeling How Students Learn to Program. Proceedings of the 43rd ACM Technical Symposium on Computer Science Education. New York, NY, USA: Association for Computing Machinery, SIGCSE ’12, p.153–160. Available from: https://doi.org/10.1145/2157136.2157182. DOI: https://doi.org/10.1145/2157136.2157182
Rivers, K. and Koedinger, K.R., 2013. Automatic Generation of Programming Feedback: A Data-Driven Approach. In: E. Walker and C. Looi, eds. Proceedings of the Workshops at the 16th International Conference on Artificial Intelligence in Education AIED 2013, Memphis, USA, July 9-13, 2013. CEUR-WS.org, CEUR Workshop Proceedings, vol. 1009. Available from: https://ceur-ws.org/Vol-1009/0906.pdf.
Sharma, K., Jermann, P. and Dillenbourg, P., 2015. Identifying Styles and Paths toward Success in MOOCs. In: O.C. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, M.C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura and M.C. Desmarais, eds. Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015. International Educational Data Mining Society (IEDMS), pp.408–411. Available from: https://eric.ed.gov/?id=ED560766.
Tóth, K., Rölke, H., Greiff, S. and Wüstenberg, S., 2014. Discovering Students’ Complex Problem Solving Strategies in Educational Assessment. In: J.C. Stamper, Z.A. Pardos, M. Mavrikis and B.M. McLaren, eds. Proceedings of the 7th International Conference on Educational Data Mining, EDM 2014, London, UK, July 4-7, 2014. International Educational Data Mining Society (IEDMS), pp.225–228. Available from: https://www.dropbox.com/s/crr6y6fx31f36e0/EDM%202014%20Full%20Proceedings.pdf.
Vihavainen, A., Vikberg, T., Luukkainen, M. and Pärtel, M., 2013. Scaffolding Students’ Learning Using Test My Code. Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education. New York, NY, USA: Association for Computing Machinery, ITiCSE ’13, p.117–122. Available from: https://doi.org/10.1145/2462476.2462501. DOI: https://doi.org/10.1145/2462476.2462501
Wang, X., Yang, D., Wen, M., Koedinger, K.R. and Rosé, C.P., 2015. Investigating How Student’s Cognitive Behavior in MOOC Discussion Forum Affect Learning Gains. In: O.C. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, M.C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura and M.C. Desmarais, eds. Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015. International Educational Data Mining Society (IEDMS), pp.226–233. Available from: https://eric.ed.gov/?id=ED560568.
Wen, M. and Rose, C.P., 2014. Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY, USA: Association for Computing Machinery, CIKM ’14, p.1983–1986. Available from: https://doi.org/10.1145/2661829.2662033. DOI: https://doi.org/10.1145/2661829.2662033
Yudelson, M., Fancsali, S., Ritter, S., Berman, S.R., Nixon, T. and Joshi, A., 2014. Better Data Beats Big Data. In: J.C. Stamper, Z.A. Pardos, M. Mavrikis and B.M. McLaren, eds. Proceedings of the 7th International Conference on Educational Data Mining, EDM 2014, London, UK, July 4-7, 2014. International Educational Data Mining Society (IEDMS), pp.205–208. Available from: https://www.researchgate.net/publication/279530590.
Yudelson, M., Hosseini, R., Vihavainen, A. and Brusilovsky, P., 2014. Investigating Automated Student Modeling in a Java MOOC. In: J.C. Stamper, Z.A. Pardos, M. Mavrikis and B.M. McLaren, eds. Proceedings of the 7th International Conference on Educational Data Mining, EDM 2014, London, UK, July 4-7, 2014. International Educational Data Mining Society (IEDMS), pp.261–264. Available from: http://d-scholarship.pitt.edu/21833/.
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Copyright (c) 2023 Muskan Yadav
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Accepted 2023-10-05
Published 2023-10-26
