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PredictCS: Personalizing Programming learning by leveraging learning analytics

Azcona, David orcid logoORCID: 0000-0003-3693-7906, Hsiao, I-Han orcid logoORCID: 0000-0002-1888-3951 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2018) PredictCS: Personalizing Programming learning by leveraging learning analytics. In: International Workshop on Orchestrating Learning Analytics (OrLA): Learning Analytics Adoption at the Classroom Level, 5-9 Mar 2018, Sydney, Australia. ISBN 978-1-4503-6400-3

This paper presents a new framework to harness sources of programming learning analytics at a Higher Education Institution and how it has been progressively adopted at the classroom level to improve personalized learning. This new platform, called PredictCS, automatically detects lower-performing or “at-risk” students in computer programming modules and automatically and adaptively sends them feedback. PredictCS embeds multiple predictive models by leveraging multi-modal learning analytics of student data, including student characteristics, prior academic history, logged interactions between students and online resources, and students' progress in programming laboratory work, and their progression from introductory to advanced CS courses. Predictions are generated every week during the semester's classes. In addition, students are flexible to opt-in to receive pseudo real-time personalized feedback, which permits them to be aware of their predicted course performance. The adaptive feedback ranges from programming suggestions from top- performers in the class to resources that are suitable to bridge their programing knowledge gaps.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Learning Analytics; Machine Learning; Computer Science Education
Subjects:Social Sciences > Education
Computer Science > Machine learning
Computer Science > Artificial intelligence
Social Sciences > Educational technology
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18). . ACM. ISBN 978-1-4503-6400-3
Copyright Information:© 2018 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council for Science Engineering and Technology, National Forum for the Enhancement of Teaching and Learning, Fulbright
ID Code:22280
Deposited On:22 Mar 2018 10:36 by David Azcona . Last Modified 11 Feb 2019 14:50

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