An examination of teachers’ strategies to foster student engagement in blended learning in higher education
Bibliographic record
Abstract
This qualitative study examined how teachers fostered student engagement in blended learning (BL), i.e., blended, blended online, and blended synchronous courses that combine synchronous and asynchronous activities. Twenty semi-structured interviews with teachers in various disciplines, at the undergraduate or graduate level in four universities, were conducted and analyzed using an inductive approach. Therefore, the study proposed a broad and comprehensive picture of teachers' strategies to enhance student engagement in BL, that were classified in three meta-categories concerning (i) the course structure and pace; (ii) the selection of teaching and learning activities; and (iii) the teacher's role and course relationships. Strategies were also linked with student engagement dimensions (behavioral, emotional, cognitive), whenever possible. The findings particularly emphasized the importance of a well-structured and -paced course, fully exploiting and integrating synchronous and asynchronous modes of BL. Clearly communicating how the course would unfold and corresponding expectations as well as establishing trusting relationships at the beginning of the semester also appeared as key to foster student engagement in BL. The use of various digital tools was also highlighted to promote student behavioral and emotional engagement at the undergraduate level, whereas cognitive and emotional engagement of graduate students was mainly targeted through experience-sharing and learning co-construction between students.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".