TY - GEN
T1 - Investigating patterns of study persistence on self-assessment platform of programming problem-solving
AU - Chung, Cheng Yu
AU - Hsiao, I. Han
N1 - Publisher Copyright:
© 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2020/2/26
Y1 - 2020/2/26
N2 - A student's short-term study behavior may not necessary infer his/her long-term behavior. It is very common to see a student changes study strategy throughout a semester and adapts to learning condition. For example, a student may work very hard before the first exam but gradually reducing the effort due to several possible reasons, e.g., being overwhelmed by various course work or discouraged by increasing complexity in the subject. Consistency or differences of one student's behavior is more likely to be discovered by multiple granularity of learning analytics. In this study, we investigate students' study persistence on a self-assessment platform and explore how such a behavioral pattern is related to the performance in exams. A probabilistic mixture model trained by response streams of log data is applied to cluster students' behavior into persistence patterns, which are further categorized into micro (short-term) and macro (long-term) patterns according to the span of time being modeled. We found four types of micro persistence patterns and several macro patterns in the analysis and analyzed their relations with exam performances. The result suggests that the consistency of persistence patterns can be an important factor driving student's overall performance in the semester, and students achieving higher exam scores show relatively persistent behavior compared to students receiving lower scores.
AB - A student's short-term study behavior may not necessary infer his/her long-term behavior. It is very common to see a student changes study strategy throughout a semester and adapts to learning condition. For example, a student may work very hard before the first exam but gradually reducing the effort due to several possible reasons, e.g., being overwhelmed by various course work or discouraged by increasing complexity in the subject. Consistency or differences of one student's behavior is more likely to be discovered by multiple granularity of learning analytics. In this study, we investigate students' study persistence on a self-assessment platform and explore how such a behavioral pattern is related to the performance in exams. A probabilistic mixture model trained by response streams of log data is applied to cluster students' behavior into persistence patterns, which are further categorized into micro (short-term) and macro (long-term) patterns according to the span of time being modeled. We found four types of micro persistence patterns and several macro patterns in the analysis and analyzed their relations with exam performances. The result suggests that the consistency of persistence patterns can be an important factor driving student's overall performance in the semester, and students achieving higher exam scores show relatively persistent behavior compared to students receiving lower scores.
KW - Learning analytics
KW - Poisson mixture model
KW - Self- assessment
KW - Self-regulated learning
KW - Study persistence
UR - http://www.scopus.com/inward/record.url?scp=85081558225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081558225&partnerID=8YFLogxK
U2 - 10.1145/3328778.3366827
DO - 10.1145/3328778.3366827
M3 - Conference contribution
AN - SCOPUS:85081558225
T3 - Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE
SP - 162
EP - 168
BT - SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education
PB - Association for Computing Machinery
T2 - 51st ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2020
Y2 - 11 March 2020 through 14 March 2020
ER -