TY - GEN
T1 - Exploring the Connections Between the Use of an Automated Feedback System and Learning Behavior in a MOOC for Programming
AU - Gabbay, Hagit
AU - Cohen, Anat
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Automated Testing and Feedback (ATF) systems are widely applied in programming courses, providing learners with immediate feedback and facilitating hands-on practice. When it comes to Massive Open Online Courses (MOOCs), where students often struggle and instructors’ assistance is scarce, ATF appears to be particularly essential. However, the impact of ATF on learning in MOOCs for programming is understudied. This study explores the connections between ATF usage and learning behavior, addressing relevant measures of learning in MOOCs. We extracted data of learners’ engagement with the course material, code-submissions and self-reported questionnaire in a Python programming MOOC with an ATF system embedded, to compile an overall and unique picture of learning behavior. Learners’ response to feedback was determined by sequence analysis of code submission, identifying improved or feedback-ignored re-submissions. Clusters of learners with common learning behaviors were identified, and their response to feedback was compared. We believe that our findings, as well as the holistic approach we propose to investigate ATF impact, will contribute to research in this field and to effective integration of ATF systems to maximize learning experience in MOOCs for programming.
AB - Automated Testing and Feedback (ATF) systems are widely applied in programming courses, providing learners with immediate feedback and facilitating hands-on practice. When it comes to Massive Open Online Courses (MOOCs), where students often struggle and instructors’ assistance is scarce, ATF appears to be particularly essential. However, the impact of ATF on learning in MOOCs for programming is understudied. This study explores the connections between ATF usage and learning behavior, addressing relevant measures of learning in MOOCs. We extracted data of learners’ engagement with the course material, code-submissions and self-reported questionnaire in a Python programming MOOC with an ATF system embedded, to compile an overall and unique picture of learning behavior. Learners’ response to feedback was determined by sequence analysis of code submission, identifying improved or feedback-ignored re-submissions. Clusters of learners with common learning behaviors were identified, and their response to feedback was compared. We believe that our findings, as well as the holistic approach we propose to investigate ATF impact, will contribute to research in this field and to effective integration of ATF systems to maximize learning experience in MOOCs for programming.
KW - Automated feedback
KW - Clustering
KW - Learning analytics
KW - MOOCs for programming
UR - http://www.scopus.com/inward/record.url?scp=85138007541&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-16290-9_9
DO - https://doi.org/10.1007/978-3-031-16290-9_9
M3 - منشور من مؤتمر
SN - 9783031162893
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 116
EP - 130
BT - Educating for a New Future
A2 - Hilliger, Isabel
A2 - Muñoz-Merino, Pedro J.
A2 - De Laet, Tinne
A2 - Ortega-Arranz, Alejandro
A2 - Farrell, Tracie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022
Y2 - 12 September 2022 through 16 September 2022
ER -