Using an Innovative Intervention to Promote Active Learning in an Introductory Microbiology Course
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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".