Authors:
Daevesh Singh
;
Indrayani Nishane
and
Ramkumar Rajendran
Affiliation:
IDP in Educational Technology, Indian Institute of Technology Bombay, Mumbai, India
Keyword(s):
Digital Learning Environments, Programming Behaviour, Process Mining, PyGuru.
Abstract:
Programming courses have high failure rates and to address this, it is crucial to better understand learning strategies associated with higher learning gains. Digital learning environments capture fine-grained data that offer valuable insights into learners’ learning strategies. Although much research has been dedicated to analysing student programming behaviours in integrated development environments, it remains unclear how their reading and video-watching behaviours, which are used for knowledge acquisition, influence these programming behaviours. In this study, we aim to bridge this gap by analysing learners’ actions in PyGuru, a learning environment for Python programming, using process mining techniques to capture their temporal learning behaviours. Our objective is to understand the behaviours associated with high and low-scoring learners. Study reveals that high-scoring learners execute codes more, indicating a correlation between execution actions and conceptual reinforcement
and engaging in active video-watching behaviours, contributing to higher learning gains. Conversely, low-scoring learners tend to rely on trial and error techniques, neglecting content review after execution. Furthermore, despite the frequent use of the ‘highlight’ action, low-scoring learners fail to revisit highlighted content, suggesting a lack of comprehensive information processing. By uncovering such behaviours, we aim to shed light on effective strategies associated with higher performance, thereby helping instructors provide feedback to struggling learners.
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