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Student behaviours in using learning resources in higher education: How do behaviours reflect success in programming education?

Mai, Tai Tan orcid logoORCID: 0000-0001-6657-0872, Crane, Martin orcid logoORCID: 0000-0001-7598-3126 and Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 (2021) Student behaviours in using learning resources in higher education: How do behaviours reflect success in programming education? In: 7th International Conference on Higher Education Advances (HEAd’21), 22-23 June 2021, Valencia, Spain (Online).

Abstract
Programming education traditionally has been an important part of Information Technology-related degrees but, more recently, it is also becoming essential in many STEM domains as well. Despite this, drop-out rates in programming courses in higher education institutions are considerable and cannot be ignored. At the same time, analysing learning behaviours has been reported to be an effective way to support the improvement of teaching and learning quality. This article aims to deliver an in-depth analysis of students’ learning behaviours when using course material items. We analyse an introductory programming course at a University in Dublin. The dataset is extracted from automatically logged learning data from a bespoke online learning system. The analysis makes use of the power of Principal Component Analysis and Random Matrix Theory to reduce dimensionality in, and to extract information from, the data, verifying the results with rigorous statistical tests. Overall, we found that all the students follow a common learning pattern in accessing all given learning items. However, there is a noticeable difference between higher and lower-performing cohorts of students when using practical and theoretical learning items. The high performing students have been consistently active in practice during the study progress. On the other hand, the students who failed the exam have more recorded activities in reading lecture notes and appear to become discouraged and unmotivated from the practical activities, especially in the later stage of the semester.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Programming Education; Learning Behaviours; Principal Component Analysis (PCA); Random Matrix Theory (RMT); Educational Data Mining
Subjects:Computer Science > Information technology
Computer Science > Software engineering
Social Sciences > Education
Social Sciences > Educational technology
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceeding of 7th International Conference on Higher Education Advances (HEAd’21). . Editorial Universitat Politècnica de València.
Publisher:Editorial Universitat Politècnica de València
Official URL:http://dx.doi.org/10.4995/HEAd21.2021.12939
Copyright Information:© 2021 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council
ID Code:26013
Deposited On:15 Jul 2021 09:36 by Tai Tan Mai . Last Modified 13 Sep 2023 12:07
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