Mapping the connection between Learning Analytics and Learning Design
Bibliographic record
Abstract
Over the past decade many have attempted to articulate the connection between Learning Design (LD) and Learning Analytics (LA) in the form of a framework or model. However, there are now so many of these that it is difficult for practitioners to determine which ones are best for which circumstances. In this workshop, participants will be introduced to a new LD/LA map which brings together the key elements from across the multitude of frameworks in order to assist in the operationalisation of learning analytics in higher education. The aim of the workshop is to apply the framework to learning scenarios to evaluate and critique its effectiveness in informing the development of LA systems and interventions. The outcome of the workshop will be a better understanding of the utility of the map and a shared vocabulary relating to how we can talk about the connection of LD and LA in educational environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".