TY - GEN
T1 - Mining Code Submissions to Elucidate Disengagement in a Computer Science MOOC
AU - Vinker, Efrat
AU - Rubinstein, Amir
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/3/21
Y1 - 2022/3/21
N2 - 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.
AB - 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.
KW - Introductory computer science education
KW - automated tutoring systems
KW - code analysis
KW - educational data mining
KW - learning analytics
KW - machine learning
KW - massive open online courses (MOOCs)
UR - http://www.scopus.com/inward/record.url?scp=85126220013&partnerID=8YFLogxK
U2 - 10.1145/3506860.3506877
DO - 10.1145/3506860.3506877
M3 - פרסום בספר כנס
AN - SCOPUS:85126220013
T3 - ACM International Conference Proceeding Series
SP - 142
EP - 151
BT - LAK 2022 - Conference Proceedings
PB - Association for Computing Machinery
Y2 - 21 March 2022 through 25 March 2022
ER -