A Review of Self-Regulated Learning and Self-Efficacy: The Key to Tertiary Transition in Science, Technology, Engineering and Mathematics (STEM)
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".