The “IKEA Model” for pragmatic development of a custom learning analytics dashboard
Bibliographic record
Abstract
Many educators and learning analytics practitioners find themselves in ‘learning analytics limbo’, with access only to simplistic one-size-fits-all vendor-driven LA dashboards, as they wait for development of possible future LA solutions that would allow customizations that genuinely cater to differences in learning design and educator skills. We present here a simple and pragmatically oriented project that allows individual educators to build and customize an LA solution ‘at home’ with relatively simple tools. This open-source project takes advantage of data available to an educator via the LMS, and allows them to develop and customize an educator-facing dashboard that meets their teaching and learning design needs. This small-scale solution allows local educators and practitioners to continue to build their data literacy and LA-informed teaching skills, and to contribute to ongoing institutional learning through sharing their experience with institutional LA teams.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".