OLabERATE: OLab Education Research Analytics Toolset Expansion - project charter
Bibliographic record
Abstract
OLab (https://olab.ca) is an educational research platform that supports branching scenarios and activity metrics. Case designs can be varied, with embedded videos and natural-language support, to explore problem-solving and communication skills, rather than memorization. A recent analysis of OLab metrics showed a rich combination of learner interactivity. However, complex decision pathways are difficult to analyze and improve, both with the existing platform and with traditional approaches that have tried to assess them in clinical practice and examinations. OLab’s central design architecture is based on directed acyclical graphs (DAGs). DAG-based analytic tools are available in a number of disciplines including social sciences (structural equation modeling), engineering (hyperparameter optimization) and computer science (genetic algorithms) but they tend to assume a best-practice or optimum path. We need a more flexible toolset that allows assessment of ‘good enough’ choice pathways in a multi-step complex decision process. Previous assessment practices have treated professional decisions as single point events. This project will improve accessibility for case authors, with shareable, reusable components; redirectable narratives; and communication skills assessment in a team-based learning context. Integration of OLab with DAG-based analytic tools will extend the analytic capabilities of researchers who wish to explore and optimize complex decision pathways, and how well clinical professionals navigate these
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.041 |
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".