Towards Deep Learning in Online Courses: A Case Study in Cross-Pollinating Universal Design for Learning and Dialogic Teaching
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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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".