Promoting student engagement with a large class (400+): Implications for large sized lectures, small group workshops and online teaching and learning
Bibliographic record
Abstract
Peer-reviewed paper presented at the Pedagogy for Higher Education Large Classes (PHELC) workshop, co-located with the Higher Education Advances (HEAd19) Conference at Universitat Politecnica de Valencia. Student engagement is widely accepted as a contributing factor on learning and success in higher education (Kahu, 2013). While a range of structural, psychosocial and psychological variables reportedly impact on student engagement, the effects of class size and particularly large classes is frequently cited as a determining influence (Mulryan-Kyne, 2010; Cuseo, 2007). This paper will present a discussion on various practices as a means of promoting student engagement with 400+ student teachers in a variety of teaching and learning environments such as small group workshops, large sized lectures and online sessions, while simultaneously highlighting that the pedagogy of the faculty is most influential and innovative course design is required to promote student engagement in large classes.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".