期刊
USER MODELING AND USER-ADAPTED INTERACTION
卷 29, 期 4, 页码 759-788出版社
SPRINGER
DOI: 10.1007/s11257-019-09234-7
关键词
Computer Science Education; Learning analytics; Predictive modelling; Machine learning; Peer learning; Educational data mining
资金
- Irish Research Council
- National Forum for the Enhancement of Teaching and Learning in Ireland [GOIPG/2015/3497]
- Science Foundation Ireland [12/RC/2289]
- Fulbright Ireland
- Irish Research Council (IRC) [GOIPG/2015/3497] Funding Source: Irish Research Council (IRC)
Different sources of data about students, ranging from static demographics to dynamic behavior logs, can be harnessed from a variety sources at Higher Education Institutions. Combining these assembles a rich digital footprint for students, which can enable institutions to better understand student behaviour and to better prepare for guiding students towards reaching their academic potential. This paper presents a newresearch methodology to automatically detect students at-risk of failing an assignment in computer programming modules (courses) and to simultaneously support adaptive feedback. By leveraging historical student data, we built predictive models using students' offline (static) information including student characteristics and demographics, and online (dynamic) resources using programming and behaviour activity logs. Predictions are generated weekly during semester. Overall, the predictive and personalised feedback helped to reduce the gap between the lower and higher-performing students. Furthermore, students praised the prediction and the personalised feedback, conveying strong recommendations for future students to use the system. We also found that students who followed their personalised guidance and recommendations performed better in examinations.
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