Student Engagement Tracks with Success In-person and Online in a Hybrid-Flexible Course
Bibliographic record
Abstract
Some university students face barriers to learning in physical classrooms, while others are reluctant to return to in-person learning environments because of COVID-19. Hybrid-flexible (HyFlex) learning environments give students the option to participate in-person or virtually, but there are concerns about student engagement and success. In this pre-pandemic study, we conducted a program-wide survey to explore student perceptions of and experiences with a HyFlex teaching and learning platform (n=238). Our survey data revealed that 86.17% of students find features of this platform helpful when accessing, engaging with, and learning course content. This was particularly true among students who reported having a flexible learning need. We also compared engagement with the HyFlex teaching and learning platform (calculated as a score out of 100 based on attendance and participation in interactive slides) and final grades between students who chose to participate predominantly in-person or online in two HyFlex offerings during the 2019/20 academic year. We found no significant difference in engagement or final grade between in-person dominant and online dominant learners in either course. We found a moderate correlation between engagement and final grade in both courses, such that highly engaged students achieved high grades regardless of their preferred mode of attendance. Our findings suggest that giving students the option to learn in-person or virtually from class to class does not negatively affect engagement or success and may in fact support success among students with flexible learning needs. As Canadian universities emerge from the pandemic, our findings remind us to retain the flexibility that virtual teaching and learning affords to support our diverse student bodies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".