Examining effects of instructional strategies on student engagement in blended online courses
Bibliographic record
Abstract
Abstract Background Blended online courses, which combine synchronous and asynchronous online activities, have expanded rapidly in higher education. How to enhance student engagement in such courses is unclear, although it is recognized that student engagement is malleable through instructional strategies. Objectives Given the above, this study aims to examine the influence of categories of strategies on student engagement in blended online courses. Methods A conceptual framework of instructional strategies indicated as fostering student engagement in the relevant literature was first presented, divided in eight categories (structure, pace, relevance, active, choice, relationships, explanations, guide). Then a research framework linking the categories of strategies to student engagement dimensions (emotional‐cognitive, social, behavioral) was built and tested in blended online courses. Data collected in various disciplines and university levels at four universities (n = 482) were examined using partial least squares structural equation modeling. Results and Conclusions The structural model examination confirmed the combined effects of categories of instructional strategies on student engagement in such courses in all disciplines. Particularly, this study revealed that 1) establishing trusting relationships, 2) demonstrating the relevance of activities, content, and resources, and 3) maintaining a sustained course pace significantly impacted student engagement in blended online courses in all disciplines. Takeaways This study draws upon the blended learning literature to bring together key instructional strategies that foster student engagement while highlighting empirical quantitative evidence of their effects on student engagement in blended online courses. Detailed measures of categories of instructional strategies and student engagement dimensions also provide reliable instruments for future research.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".