Accelerating Evidence-Based Learning Design Improvement with Adaptive Interventions
Bibliographic record
Abstract
This thesis explores adaptive interventions aimed at enhancing learning experience and outcomes within the learning design space. It addresses two key research questions by combining design research and knowledge mobilization perspectives:(1) What are the specific design characteristics and constraints associated with introducing adaptive interventions in learning design? (2) What are the ways to communicate the concept, benefits, and constraints of adaptive interventions to instructors, designers, and analysts? This thesis is grounded in data-driven lessons from participation in the XPRIZE Digital Learning Challenge and other field deployments. It outlines organizational and technical workflows for designing adaptive interventions and reflects on their potential integration with learning engineering and learning analytics. It also proposes decision affordances for designing, monitoring, and analyzing adaptive interventions in learning design settings. Additionally, it reports on the development and piloting of an open educational resource (OER) on adaptive interventions and examines case studies that highlight aspects of real-world applications. Finally, the thesis concludes with a discussion of adaptive interventions' design opportunities and constraints, and suggests future research directions. It provides practical guidelines for integrating adaptive interventions into digital learning platforms, bridging the gap between theory and practice and making its insights applicable to educators, learning designers, and other stakeholders.
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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 | 0.079 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".