TY - GEN
T1 - Discovering Students’ Learning Strategies in a Visual Programming MOOC Through Process Mining Techniques
AU - Rohani, Narjes
AU - Gal, Kobi
AU - Gallagher, Michael
AU - Manataki, Areti
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Understanding students’ learning patterns is key for supporting their learning experience and improving course design. However, this is particularly challenging in courses with large cohorts, which might contain diverse students that exhibit a wide range of behaviours. In this study, we employed a previously developed method, which considers process flow, sequence, and frequency of learning actions, for detecting students’ learning tactics and strategies. With the aim of demonstrating its applicability to a new learning context, we applied the method to a large-scale online visual programming course. Four low-level learning tactics were identified, ranging from project- and video-focused to explorative. Our results also indicate that some students employed all four tactics, some used course assessments to strategize about how to study, while others selected only two or three of all learning tactics. This research demonstrates the applicability and usefulness of process mining for discovering meaningful and distinguishable learning strategies in large courses with thousands of learners.
AB - Understanding students’ learning patterns is key for supporting their learning experience and improving course design. However, this is particularly challenging in courses with large cohorts, which might contain diverse students that exhibit a wide range of behaviours. In this study, we employed a previously developed method, which considers process flow, sequence, and frequency of learning actions, for detecting students’ learning tactics and strategies. With the aim of demonstrating its applicability to a new learning context, we applied the method to a large-scale online visual programming course. Four low-level learning tactics were identified, ranging from project- and video-focused to explorative. Our results also indicate that some students employed all four tactics, some used course assessments to strategize about how to study, while others selected only two or three of all learning tactics. This research demonstrates the applicability and usefulness of process mining for discovering meaningful and distinguishable learning strategies in large courses with thousands of learners.
KW - Educational data mining
KW - Learning strategy
KW - Learning tactic
KW - Massive open online courses
KW - Process mining
KW - Visual programming
UR - http://www.scopus.com/inward/record.url?scp=85152562951&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-27815-0_39
DO - https://doi.org/10.1007/978-3-031-27815-0_39
M3 - Conference contribution
SN - 9783031278143
T3 - Lecture Notes in Business Information Processing
SP - 539
EP - 551
BT - Process Mining Workshops - ICPM 2022 International Workshops, Revised Selected Papers
A2 - Montali, Marco
A2 - Senderovich, Arik
A2 - Weidlich, Matthias
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022
Y2 - 23 October 2022 through 28 October 2022
ER -
