Mining Code Submissions to Elucidate Disengagement in a Computer Science MOOC

Efrat Vinker, Amir Rubinstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite the growing prevalence of Massive Open Online Courses (MOOCs) in the last decade, using them effectively is still challenging. Particularly, when MOOCs involve teaching programming, learners often struggle with writing code without sufficient support, which may increase frustration, attrition, and eventually dropout. In this study, we assess the pedagogical design of a fresh introductory computer science MOOC. Keeping in mind MOOC "end-user"instructors, our analyses are based merely on features easily accessible from code submissions, and methods that are relatively simple to apply and interpret. Using visual data mining we discover common patterns of behavior, provide insights on content that may require reevaluation and detect critical points of attrition in the course timeline. Additionally, we extract students' code submission profiles that reflect various aspects of engagement and performance. Consequently, we predict disengagement towards programming using classic machine learning methods. To the best of our knowledge, our definition for attrition in terms of disengagement towards programming is novel as it suits the unique active hands-on nature of programming. To our perception, the results emphasize that more attention and further research should be aimed at the pedagogical design of hands-on experience, such as programming, in online learning systems.

Original languageEnglish
Title of host publicationLAK 2022 - Conference Proceedings
Subtitle of host publicationLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages142-151
Number of pages10
ISBN (Electronic)9781450395731
DOIs
StatePublished - 21 Mar 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: 21 Mar 202225 Mar 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period21/03/2225/03/22

Keywords

  • Introductory computer science education
  • automated tutoring systems
  • code analysis
  • educational data mining
  • learning analytics
  • machine learning
  • massive open online courses (MOOCs)

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