Improving Student Engagement in Online Learning: A Case Study of a Graduate Program in Canada
Bibliographic record
Abstract
The landscape of higher education has undergone a transformative shift towards online learning globally since COVID-19. Student engagement in online learning emerges as a pivotal area of inquiry due to its critical role in learning outcomes and academic success. While extensive research has explored student engagement in traditional face-to-face settings, there remains a notable gap in understanding engagement in online learning environments, particularly at the graduate level. Adopting an integrated framework of improvement science and an online student engagement framework, this study examines the engagement experiences of graduate students in a fully online program offered by a Canadian university, utilizing a qualitative case study methodology. Through the lenses of cognitive, emotional, behavioral, collaborative, and social engagement, this study is guided by the holistic analysis of improvement science. Pedagogical, organizational, and socio-structural factors are identified as closely linked to student engagement in online learning. Strategies are proposed to improve interaction, provide personalized support, and cultivate a sense of community, while policy recommendations advocate for learner-centric approaches and quality assurance mechanisms. This study provides a better understanding of online student engagement, offering valuable insights for educators, policymakers, and institutions striving to improve the online learning environment for graduate students in Canada and beyond.\nKeywords: student engagement, online learning, higher education, improvement science, case study
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".