Towards Deep Learning in Online Courses: A Case Study in Cross-Pollinating Universal Design for Learning and Dialogic Teaching
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
This article presents a case study of an online course that cross-pollinated Universal Design for Learning (UDL) and dialogic teaching to facilitate deep learning. Conceptualized through the UDL framework, dialogue and dialogic teaching, and deep learning, our analysis employs the methods of design-based research and thematic analysis to unpack the pedagogical cross-pollination in facilitating deep learning in a postgraduate course in a virtual setting. In particular, we examine the course goals, major online compositions, instruction and pedagogies, and assessment. We also explore student learning experiences in approaching deep learning by analyzing their postings in the discussion forums. Findings include multiple pedagogical strategies that fostered deep learning in this online course. This study contributes to the growing literature on online teaching and learning, particularly through cross-pollinating UDL and dialogic teaching to facilitate deep learning in higher education.
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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 | 0.056 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.016 |
| 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