Do students’ attitudes toward active learning in science affect buy-in?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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 it