PREDICTORS OF ACADEMIC WORK ENGAGEMENT IN HIGHER EDUCATION: A HIERARCHICAL MULTIPLE LINEAR REGRESSION ANALYSIS
PREDICTORS OF ACADEMIC WORK ENGAGEMENT IN HIGHER EDUCATION: A HIERARCHICAL MULTIPLE LINEAR REGRESSION ANALYSIS

Summary/Abstract: A student’s level of academic work engagement is a key predictor of their retention in higher education (HE) and their commitment to lifelong learning. While existing research frequently identifies motivation as the main driver of this engagement, other factors – such as students’ specific learning strategies and situational interests – have received limited empirical attention. Using a non-experimental two-wave longitudinal design, we examined the extent to which academic motivation, situational interest, and learning approaches predict students’ academic work engagement over the course of a semester. Data were collected across two waves from a volunteer sample of 129 bachelor’s students, comprising 86.8% females with an average age of 21.36 years. For the data analysis, we used a hierarchical multiple linear regression analysis. Our results indicated that prior engagement (step 1) was the strongest predictor of future academic work engagement. While early stages of the model (steps 2 and 3) showed intrinsic motivation for stimulation as a significant factor, the final model (step 4) revealed the deep learning approach as the dominant predictor, largely diminishing the impact of motivation. Also, situational interest (step 3) did not significantly predict engagement outcomes. Present findings indicate that, while motivation is important, how a student processes information (i.e., a deep learning approach) might be a stronger predictor for learning engagement in HE than students’ initial levels of learning motivation. Therefore, teachers might benefit more from integrating deep learning strategies, rather than relying solely on motivational interventions. Practical implications for HE teachers and research directions are further detailed in the manuscript.

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