Pedagogical Design: Bridging Learning Theory and Learning Analytics
Bibliographic record
Abstract
Which learning analytics (LA) approach might be the best choice for your teaching and learning context? Learning analytics as a field of research and application seeks to collect, analyze, report, and interpret educational data with the goal of improving teaching and learning. But hasty adoption of learning analytics tools and methods that are simply convenient, promoted or available risks allowing learning analytics to ‘drive the pedagogical bus’. In this paper, we propose that careful reflection on pedagogical design choices and the learning theory that underpins them can and should inform selection of relevant learning analytics tools and approaches. We broadly review established learning theories and the implications of each for pedagogical design; for each design approach we offer examples of learning analytics most clearly aligned with the theoretical perspectives on learning and knowledge that have shaped it. Moreover, we argue that careful consideration of the learning theory underpinning the pragmatics of pedagogical design choices should guide LA implementation, and help educators and designers avoid the risk of gathering data on, and measuring outcomes for, activities that are not relevant to their pedagogical design or goals.
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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 | 0.076 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".