Do Self-Regulated Learning Practices and Intervention Mitigate the Impact of Academic Challenges and COVID-19 Distress on Academic Performance During Online Learning?
Bibliographic record
Abstract
The COVID-19 pandemic introduced significant disruptions and challenges to the learning environment for many post-secondary students with many shifting entirely to remote online learning. Barriers to academic success already experienced in traditional face-to-face classes may be compounded in the online environment and exacerbated by stressors related to the pandemic. In 2020–2021, post-secondary institutions were faced with the reality of rolling out fully online instruction with limited access to resources for assisting students in this transition. Instructional interventions that target students’ ability to self-regulate their learning have been shown to improve academic performance and self-regulated learning (SRL) competencies have also been found to mediate the effect of SRL interventions on higher education. However, few studies have examined the efficacy of fully online SRL intervention on mitigating the impact of psychological distress and academic challenges on academic success. This study examined the moderating roles of self-regulatory practices and SRL intervention in buffering the influence of COVID-related psychological distress and academic challenges on academic outcomes (self-reported grade point average (GPA) and academic challenges) in a Canadian sample of undergraduate students ( n = 496). We found (a) levels of metacognitive and motivational challenges fully mediated the impact of COVID distress on GPA, (b) SRL adapting practices moderated the impact of metacognitive challenges on GPA, and (c) semester-long SRL intervention buffered the impact of COVID distress on academic challenges and resulted in lower levels of social-emotional, cognitive, and metacognitive challenges for first year undergraduate students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".