Investigating a blended learning context that incorporates two-stage quizzes and peer formative feedback in STEM education
Bibliographic record
Abstract
Researchers have expressed concern about the state of STEM education. To improve this situation, new pedagogies, such as blended learning, have been proposed and tested. The last decade has seen an increase in the use of blended learning to support learning; however, the effect of blended learning on learning remains unclear and often mixed. The two studies in this paper draw on data from pre-university science students in the following courses: (1) Electricity and Magnetism (E&M) and (2) Waves, Optics & Modern Physics (Waves). In study 1, the treatment group (blended learning coupled with two-stage quizzes & peer formative feedback) performed significantly higher than the control group (lecture format with online homework & instant feedback) in the standardized final exam. In contrast, in study 2, there was a non-significant main effect of groups, indicating that the treatment group (blended learning with online homework & instant feedback) and the control group (lecture format with online homework & instant feedback) performed similarly in the standardized final exam. The finding of study 1 suggests that the effect of an instructional pedagogical framework embedded in a blended learning context improves performance in STEM education. Whereas the finding of study 2 suggests that a blended learning context without incorporating any instructional framework or support for cognition other than the lecture is comparable to a traditional face-to-face course.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".