Using Machine Learning Algorithms for Analysing the Factors That Affect Pupil Engagement and Learning Outcomes in CSE

Abstract

Student engagement in computing science education (CSE) is crucial for student learning. However, little is known about the effects of all four student engagement dimensions on pupils’ learning outcomes in CSE. Moreover, little is known about measuring behavioural engagement (BE), cognitive engagement (CE), emotional engagement (EE), and social engagement (SE) and how to identify student engagement levels in CS classes in high schools. The study investigates the effects of BE, CE, EE, and SE on pupils’ learning outcomes in high schools’ CS classes and uses machine learning approaches to better understand and optimise the learning process and environments in which it occurs.