Blended learning in a biology classroom: Pre‐pandemic insights for post‐pandemic instructional strategies
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
It is increasingly important to utilize novel approaches to improve student learning. This has become especially relevant throughout the COVID-19 pandemic. Previous studies have shown positive outcomes of blended learning on student satisfaction. Yet, there are limited data in the field of biology on how blended learning practices correlate with overall student performance. Moreover, there is a dearth of information on student perceptions about how blended learning has impacted their education. Through this study, we present insights on the impact of blended learning in a first-year cell and molecular biology course. Using mixed-methods research, we evaluated the impact of a blended learning course format on student performance in the learning categories of knowledge and understanding, communication and application, and critical thinking and inquiry. Using a pre- vs. postintervention analysis, we show that a blended learning course model does not change students' performance on multiple-choice and short answer assessments when compared to a nonblended learning course model. Through a qualitative assessment of student perceptions and sentiments, however, the implemented blended learning approach does appear to provide significant perceived benefits, including learner flexibility, consolidation of content, and the opportunity to apply course content to the 'real world'. While we recognize that our report describes a very specific blended learning model, we believe that our findings are generalizable to similar introductory courses. As such, we are confident that our case study will provide course designers with a useful foundation to build future blended learning courses.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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