Student use of learning resources to understand antimicrobial actions: use in hybrid vs on-line format (37.6)
Bibliographic record
Abstract
Abstract Increased demand for online computer-based instructional resources has led to the need for effective utilization of learning objects in health biology courses. Our aim was to examine how well learning objects assist students in understanding challenging concepts in antimicrobial activity against infectious disease. Our specific focus was on learning object use in an introductory microbiology and immunology course in two different settings: a fully on-line section and a hybrid section, which integrates online materials with face-to-face lectures. To collect information on use of the learning objects, an anonymous survey was given to both sections. Survey participation rates varied, with a 56% rate from the online course, and only 28% from the hybrid course. Survey data analysis indicated many similarities between online and hybrid sections in learning object utilization, including high usage as study material for examinations. Differences were observed in the positive impact learning resources have on students learning difficult concepts, with the online course showing 100% agreement, and the hybrid course only 76% agreement, suggesting differences in learning object utilization for learning challenging new concepts. Qualitative responses provided further indications for effective learning object integration into differentiated instruction formats. Funded by UOIT’s Teaching Innovation Fund.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".