Students’ Behaviours in using Learning Resources in Higher Education: How do behaviours reflect success in Programming Education?

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.

High Academic Performance and Engagement Behaviours in Online Learning Environments

“…The quiz formative activity is the online activity where high-performing students contributed the most in programming courses and non-programing courses. This behaviour of having high contributions to formative activities such as quizzes in coding is consistent with the studies that high-performing students in programming education have been consistently active in practice as their study progresses (Hassinen & Mäyrä, 2006;Mai et al, 2021;Pedrosa et al, 2016).…”

“…Studies have shown a positive relationship between student motivation and academic achievement (Pintrich & De Groot, 1990). It has also been indicated that high-achieving students show a refined ability to select and modify study behaviors (Mai et al, 2021), manage their time and use more effective study strategies (Dunlosky et al, 2013) and routinely engage in and adapt skills to pursue these behaviors (Zimmerman & Schunk, 2001). Despite evidence of the association between high performance and effective study behaviors as a primary study strategy, Persky (2018) found in his longitudinal study that high-achieving students relied on re-reading texts and re-watching videos.…”

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

“…From RMT, eigenvalues falling outside of are assumed to deviate from its expected predictions [ 63 , 64 ]. Hence, we can use this theory to test the reliability of the relationships in our empirical data [ 65 ].…”

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

“…It has been noticed [ 18 , 59 ] that, in practical usage of the online learning system, students tend to interact sporadically with course material items. This phenomenon appears to create noise in the data and may limit the chance of detecting the difference in learning behaviours among students.…”