Investigating Factors That Affect Student Engagement and Academic Performance in Novice Students in CS Education

Abstract

In recent years, improving student engagement in Computer Science Education (CSE) has gained significant attention. Despite the abundance of research on student engagement, the relationships between the dimensions of student engagement (behavioural, cognitive, emotional, and social), students’ confidence in learning Computer Science (CS), and their beliefs in the usefulness of learning CS have not been thoroughly investigated in CSE. Thus, the primary objective of this thesis is to address the above arguments by examining multidimensional student engagement factors and identifying factors influencing engagement and CS learning performance among novice students in CS courses. This thesis uses a mixed methods approach and includes six research studies building on each other. The research was conducted between 2021 and 2024. It was carried out in various educational institutions, including schools and universities, across two countries: Saudi Arabia and Scotland. This thesis is structured into five phases, starting with a systematic literature review (SLR) to investigate previous research on student engagement in CSE at both school and higher education (HE) levels. The review aimed to identify study objectives, methods, and different indicators used to measure student engagement in CSE. This SLR led to the second phase, which includes two studies focusing on confidence and perceived usefulness factors. The first study explored the relationship between different dimensions of student engagement and the confidence and perceived usefulness among female high school students in Saudi Arabia. The second study applied ML algorithms to analyse engagement indicators and predict these two factors among female students. The third phase involves a quantitative study using structural equation modeling to examine the relationships between CS academic performance, student engagement, confidence, and perceived usefulness. The study also explores and compares student engagement levels between two different groups of students, considering academic cultural contexts and gender differences. The fourth phase includes a qualitative study examining novice students’ perspectives on factors that affect their engagement and academic performance in CS classes. The last phase includes a study that explored student engagement, log data (learning analytics) and used ML prediction models to explore and determine the predictive power of different engagement indicators that could be used to predict CS learning performance. The findings support the thesis claim that student engagement, which comprises behavioural, cognitive, emotional, and social dimensions, and is influenced by students’ confidence in learning CS and their perceived usefulness of learning CS, affects CS learning performance. This thesis makes several important contributions to the CS field. A key contribution is the development of confidence, perceived usefulness, and student engagement model to enhance CS learning performance (CUSEL). This model could be used by CS educators to support the learning and teaching process in CS courses. The thesis provides implications for educators, researchers, and any stakeholders who want to improve or design effective interventions that would increase engagement to improve student learning outcomes in CSE.