Supporting the Flipped Classroom Approach to Higher Education Through a Computer-Based Learning Environment
Bibliographic record
Abstract
This study exemplified two successful implementations of a flipped classroom approach using the computer-based learning environment Toolbox TeacherEducation (TTE) integrated into two separate university courses. We questioned how the TTE can be used in a flipped classroom to teach and learn successfully and how participants’ self-regulated learning and user experience contribute to learning. We analyzed two university courses (N1 = 34, N2 = 73) designed as flipped classrooms. To measure knowledge increase, we developed multiple-choice items to collect knowledge before and after learning. Participants showed a significant learning gain in both courses (average p = .025) with an average effect size of d = 1.02. Since self-regulated learning competencies and user experience affect computer-based learning, we addressed these concepts using different questionnaires. Regarding self-regulated learning, the participants reported above-average skills, but we did not find meaningful correlations with learning. Regarding user experience, the participants rated the TTE as highly usable and well designed. Based thereon, we showed how the TTE could be implemented in a flipped classroom to teach and learn successfully.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".