Do students’ attitudes toward active learning in science affect buy-in?
Bibliographic record
Abstract
Active learning aims to support students’ construction of knowledge and understanding via their direct engagement with activities that support the learning process. It has been well documented that students benefit from active learning; however, students often report disliking this method of learning and disfavour the student-centered approach as it puts them in charge of their learning. Furthermore, little is known about specific attitudes and attributes that may influence engagement with and adoption of these practices. To explore this knowledge gap, we assessed students’ motivation, self-efficacy, introversion/extroversion, science identity and evaluated their relationships with student buy-in to active learning; measured using the exposure-persuasion-identification-commitment (EPIC) process model (Cavanagh et al., 2016). Undergraduate science students (n=123) at Carleton University and the University of Ottawa had 76% of students report engaging with active learning in their science courses. Students engaged with an average of 11 (M = 11.19, SD = 3.29) out of 16 possible activities. Of the active learning activities, 34% of students liked this way of learning while 20% report only doing it because it was required of them. Motivation, self-efficacy, introversion/extroversion, and science identity were positively correlated with persuasion. In line with previous studies, buy-in was positively correlated with students' engagement in active learning behaviours. The relationships identified will allow us to make recommendations to help shape the pedagogical practices of educators and further improve student buy-in to this type of learning. This research has been approved by the research ethics board at Carleton University and the University of Ottawa.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".