Learning Design and Learning Analytics: Snapshot 2020
Bibliographic record
Abstract
“Learning design” belongs to that interesting class of concepts that appear on the surface to be simple and self-explanatory, but which are actually definitionally vague and contested in practice. Like “learning analytics,” the field of learning design aspires to improve teaching practice, the learning experience, and learning outcomes. And like learning analytics, this interdisciplinary field also lacks a shared language, common vocabulary, or agreement over its definition and purpose, resulting in uncertainty even about who its practitioners are — Educators? Designers? Researchers? All of these? (Law, Li, Farias Herrera, Chan & Pong, 2017). Almost a decade ago, however, learning analytics researchers pointed to the rich potential for synergies between learning analytics and learning design (Lockyer & Dawson, 2011). These authors (and others since, as cited below) argued that effective alignment of learning analytics and learning design would benefit both fields, and would offer educators and investigators the evidence they need that their efforts and innovations in learning design are “worth it” in terms of improving teaching practice and learning: "The integration of research related to both learning design and learning analytics provides the necessary contextual overlay to better understand observed student behavior and provide the necessary pedagogical recommendations where learning behavior deviates from pedagogical intention" (Lockyer & Dawson, 2011, p. 155).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.029 |
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".