Using an Innovative Intervention to Promote Active Learning in an Introductory Microbiology Course
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
We found that actively engaging students in our introductory microbiology course was a challenge given our large class sizes and many non-majors taking the course as a program requirement. Therefore, we introduced a novel active learning strategy to our course. Students grouped into teams of three had to create PowerPoint virtual posters on one of three themes: (a) a report on a microbe or immunology story in the news, b) interview a research scientist, or c) research a microbiological topic of their choice. To assess the intervention’s effectiveness, a pre- and post-course assessment was done. Pre-posttest analysis revealed a significant drop in surface learning and rise in deep learning. Also, there was a drop in the extrinsic (grade-dependent) goal orientation and affective (test anxiety) components. We found a decline in task value, self-efficacy for learning and performance, organization, critical thinking, time and study environment, and help seeking in students’ post-test scores. Qualitative findings also indicated the importance of group activity, gaining extra knowledge outside the curriculum, and long-term course content retention. In conclusion, we propose that the creation of digital posters in teams is an effective strategy to increase student engagement in large classes.
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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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