A Review of Self-Regulated Learning and Self-Efficacy: The Key to Tertiary Transition in Science, Technology, Engineering and Mathematics (STEM)

Abstract

The ability to distinguish between effective and ineffective study strategies based on feedback is of utmost importance for secondary school leavers transitioning to tertiary education (Brinkworth et al., 2009; Salisbury & Karasmanis, 2011). Often accompanying this learning environment transition is academic difficulty and an increased possibility of failure, and it is therefore essential for undergraduate students, in particular those studying the disciplines of Science, Technology, Engineering and Maths (STEM), to establish a solid repertoire of learning strategies early in their academic career. Self-regulation is a key component of learning that can be fostered to encourage a successful transition from secondary school to university (Vosniadou, 2020). Self-regulated learning refers to learning that is fostered by one’s metacognition, strategy adaptability, and motivation. Of these constructs, metacognition is fundamental, as having self-awareness allows one to identify the requirement for corrective action in the learning process, allowing learners to monitor their behaviour and reflect on the success of their learning strategies, where the motivation to do this should lead to strategy adaptation. In addition, students must make accurate self-efficacious judgements about their learning in order to evaluate the effectiveness of their learning strategies or to decide when they have sufficiently completed a learning task. Therefore, in order to develop a means of improving students’ transition from secondary school to university, one must first appreciate the impacts of self-regulated learning and self-efficacy on academic performance. This review aims to focus on self-regulated learning and self-efficacy, of which self-regulated learning is a construct of metacognition, motivation and strategy adaptability. This review will also evaluate self-regulated learning with an emphasis on Zimmerman’s model, the calibration of self-efficacy, and how students might break the cycle of poor learning with a focus on STEM.