Face to Face, Online or Something in Between: Student Perceptions of Student Engagement in Different Learning Environments
Bibliographic record
Abstract
When COVID-19 began, educational institutions quickly shifted traditional face-to-face (F2F) courses to an online model, termed Emergency Remote Education (ERE) (Bozkurt et al., 2020). Previous studies have developed extensive theories on F2F and online education, demonstrating a positive link between student engagement (SE) and student success. Using Critical Incident Technique, this paper examined the changes in SE from F2F to ERE and identified the factors influencing these changes. The results confirmed that SE differs between F2F and ERE, with primary factors including course design and organization, learning with peers, student-faculty interaction, and social interaction. Notably, a supportive environment, integral to the SE model in F2F settings but absent in the Community of Inquiry framework, emerged as a key factor in ERE. Based on these findings, this study proposes a new SE model for flexible delivery methods. Post-pandemic reports (Irhouma & Johnson, 2022; D. N. Johnson, 2021; N. Johnson, 2023; National Survey of Student Engagement, 2021, 2023; Veletsianos et al., 2023) highlight the ongoing impact of the pandemic on students and teachers. The 2023 Pan-Canadian Report on Digital Learning Trends indicates that faculty and students now favor more flexible teaching and learning methods (Johnson, 2023). Current research on ERE remains limited (Stewart et al., 2023), with fewer studies on models for engagement in ERE. This paper contributes insight which aims to provide a foundation for further investigation into online and flexible learning.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".