TY - GEN
T1 - Behavioral Analytics for Distributed Practices in Programming Problem-Solving
AU - Alzaid, Mohammed
AU - Hsiao, I. Han
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - This Research Full Paper aims to investigate the learning analytics of students' problem solving when working on distributed programming practices. Typical programming practice activities (i.e. assignments) in a lecture-dominant course may violate the principles of distributed retrieval practice. However, there are tradeoffs between managing depth and breadth of the content and classroom disruptions with the modern platforms and technologies. In this work, we investigate students' behavioral analytics in distributed programming practices. A classroom study was conducted in an introductory programming course and the learners' patterns were observed. Results showed that there were three distinct patterns found: affirmative, experimental, and surrendering. Better-performing students demonstrated more affirmative behaviors and fewer surrendering acts; Below-average students showed a lack of persistence in distributed practices. Additionally, the study reconfirmed the value of spacing effects on learning, which is the importance of spending time and to spreading the working sessions to solve diverse quizzes. Ineffective trial-and-error strategy and neglect the power of practices can be two alarming behaviors in distributed programming practices. Finally, predictive models of performance were presented based on the behavioral patterns.
AB - This Research Full Paper aims to investigate the learning analytics of students' problem solving when working on distributed programming practices. Typical programming practice activities (i.e. assignments) in a lecture-dominant course may violate the principles of distributed retrieval practice. However, there are tradeoffs between managing depth and breadth of the content and classroom disruptions with the modern platforms and technologies. In this work, we investigate students' behavioral analytics in distributed programming practices. A classroom study was conducted in an introductory programming course and the learners' patterns were observed. Results showed that there were three distinct patterns found: affirmative, experimental, and surrendering. Better-performing students demonstrated more affirmative behaviors and fewer surrendering acts; Below-average students showed a lack of persistence in distributed practices. Additionally, the study reconfirmed the value of spacing effects on learning, which is the importance of spending time and to spreading the working sessions to solve diverse quizzes. Ineffective trial-and-error strategy and neglect the power of practices can be two alarming behaviors in distributed programming practices. Finally, predictive models of performance were presented based on the behavioral patterns.
KW - Behavioral Analytics
KW - Distributed Practices
KW - Educational Data Mining
KW - Problem Solving
KW - Programming
KW - Self-Assessment
UR - http://www.scopus.com/inward/record.url?scp=85082485442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082485442&partnerID=8YFLogxK
U2 - 10.1109/FIE43999.2019.9028583
DO - 10.1109/FIE43999.2019.9028583
M3 - Conference contribution
AN - SCOPUS:85082485442
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2019 IEEE Frontiers in Education Conference, FIE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE Frontiers in Education Conference, FIE 2019
Y2 - 16 October 2019 through 19 October 2019
ER -