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Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints
Azcona, David; Hsiao, I-Han; Smeaton, Alan F.
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.
Keyword(s): Education; Educational technology; Computer Science Education; Learning analytics; Predictive modelling; Machine learning; Peer learning; Educational data mining
Publication Date:
2019
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Azcona, David ORCID: 0000-0003-3693-7906 <https://orcid.org/0000-0003-3693-7906>, Hsiao, I-Han ORCID: 0000-0002-1888-3951 <https://orcid.org/0000-0002-1888-3951> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <https://orcid.org/0000-0003-1028-8389> (2019) Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction . ISSN 0924-1868
Publisher(s): Springer
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/23223/2/Azcona_AAM.pdf,
https://doi.org/10.1007/s11257-019-09234-7
First Indexed: 2019-05-03 06:05:02 Last Updated: 2019-05-03 06:05:02