Blended Learning and Student-centered Active Learning Environment: a Case Study with STEM Undergraduate Students
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
Abstract This paper describes an embedded case study of “blended” teaching integrated with traditional lessons in a Student-Centered Active Learning Environment and social activities on the platform. The didactic phenomena were designed by creating learning environments, artifacts, and teaching/learning sequences in authentic educational contexts. We aim at improving the task design of a mathematics lesson with an impact on students’ performance in mathematics. Quantitative results show considerable benefits in the evolution of the use and coordination of several systems of semiotic representation. As a result, a better predisposition to the study of the subject seems to appear; moreover, the satisfaction test shows the achievement of alternative teaching methodologies for most of the students.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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