Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints

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 b...

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Vydané v:User modeling and user-adapted interaction Ročník 29; číslo 4; s. 759 - 788
Hlavní autori: Azcona, David, Hsiao, I-Han, Smeaton, Alan F.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.09.2019
Springer Nature B.V
Predmet:
At risk students
Computer programming
Computer Science
Demographics
Digital computers
Feedback
Footprints
Higher education institutions
Management of Computing and Information Systems
Multimedia Information Systems
Prediction models
Students
User Interfaces and Human Computer Interaction
Learning analytics
Educational data mining
Predictive modelling
Computer Science Education
Peer learning
Machine learning
ISSN:0924-1868, 1573-1391
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Shrnutí: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 new research 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0924-1868
1573-1391
DOI:10.1007/s11257-019-09234-7