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Targeting at-risk students using engagement and effort predictors in an introductory computer programming course

Azcona, David orcid logoORCID: 0000-0003-3693-7906 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2017) Targeting at-risk students using engagement and effort predictors in an introductory computer programming course. In: The 12th. European Conference on Technology-Enhanced Learning (EC-TEL), 12-15 Sept 2017, Tallinn, Estonia. ISBN 978-3-319-66610-5

Abstract
This paper presents a new approach to automatically detect- ing lower-performing or “at-risk” students on computer programming modules in an undergraduate University degree course. Using histori- cal data from previous student cohorts we built a predictive model using logged interactions between students and online resources, as well as stu- dents’ progress in programming laboratory work. Predictions were cal- culated each week during a 12-week semester. Course lecturers received student lists ranked by their probability of failing the next computer- based laboratory exam. At-risk students were targeted and offered assis- tance during laboratory sessions by the lecturer and laboratory tutors. When we group students into two cohorts depending on whether they failed or passed their first laboratory exam, the average margin of im- provement on the second laboratory exam between the higher and lower- performing students was four times higher when our predictions were run and subsequent laboratory support targeted at these students, compared to students from the year our model was trained on.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Computer science education; Learning Analytics; Prediction
Subjects:Social Sciences > Education
Computer Science > Machine learning
Computer Science > Artificial intelligence
Social Sciences > Educational technology
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Data Driven Approaches in Digital Education. Lecture Notes in Computer Science (LNCS) 10474. Springer International Publishing AG. ISBN 978-3-319-66610-5
Publisher:Springer International Publishing AG
Official URL:https://doi.org/10.1007/978-3-319-66610-5_27
Copyright Information:© 2017 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council with the National Forum for the Enhancement of Teaching and Learning in Ireland, GOIPG/2015/3497, Science Foundation Ireland (SFI) Grant Number SFI/12/RC/2289
ID Code:22041
Deposited On:25 Sep 2017 15:18 by David Azcona . Last Modified 29 Apr 2021 12:44
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