Exploring Preservice Teachers Engagement With Live Models of Universal Design for Learning and Blended Learning Course Delivery
Bibliographic record
Abstract
Universal Design for Learning (UDL) and Blended Learning (BL) formats, are widely adopted across K–12 learning environments. Upon graduation, preservice teachers may be expected to implement UDL and BL practices. The present study was motivated by the need to provide preservice teachers with live modeling of UDL and BL concepts. Learning analytics data from 197 preservice teachers was examined for engagement with UDL/BL Access features (location, day-of-the-week, time-of-day, and regularity), Content features (screencasts and quizzes), and to determine if there was a relationship between engagement and achievement. Examination of the learning management system login data revealed regular access to the digital content across differing locations, week days, and time of day. Associations were significant between academic performance and all features. Designing the BL digital course components following UDL principles appears to have served as a self-regulation enabler for preservice teachers themselves while providing exemplars to adopt in their future practice.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".