Student perceptions of the use and effectiveness of engagement strategies in the online learning environment
Bibliographic record
Abstract
Challenges in student engagement have been identified as a significant barrier in online learning. In this study, we used a mixed method design to explore how often, and how effectively, instructors used a variety of engagement strategies in their online classrooms. First, through the use of student focus groups with 30 students, we identified engagement strategies that students used and found effective. Our results indicated that breakout rooms and interactive activities/games and icebreakers were the most frequently mentioned engagement strategies for synchronous courses and using WhatsApp and discussion forums were mentioned for asynchronous courses. Next, a total of 284 undergraduate students (84% female) took part in an online survey consisting of 33 engagement strategies in which participants rated how often the strategy was used and how effective they found it. Descriptive analyses demonstrated that students most frequently used strategies centered on structure (i.e. grading rubrics, course orientations, and instructor/peer discussion forums that enable interactions). Students reported that the most effective strategies were those that focused on clarity (i.e. posting of due dates, grading rubrics, and regular emails from the instructor). Interestingly, while peer relations were predominantly discussed in the focus groups, peer-to-peer connection strategies in the survey received the lowest ratings, both in terms of how often they were used and how effective students found them. Overall, our results suggest that students consider clarity to be the most effective method for raising engagement and that instructor relations appear to be more beneficial than peer relations.
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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 | 0.001 |
| 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".