Rating learning object quality with distributed Bayesian belief networks: the why and the how
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As differing evaluation instruments are adopted in learning object repositories serving specialized communities of users, what methods can be adopted for translating evaluative data across instruments in order to share this data among different repositories? How can evaluation from different reviewers be properly integrated? How can explicit and implicit measures of preference and quality be combined to recommend objects to users? In this research we studied the application of Bayesian belief network (BBN) to the problem of insufficient and incomplete reviews during learning objects evaluation, and translating and integrating data among different quality evaluation instruments and measures. Two BBNs were constructed to probabilistically model relationships among different roles of reviewers as well as among items of different evaluation measurements. Initial testing using hypothetic data showed that the model was able to make potentially useful inferences about different dimensions of learning object quality. We further extend our model over geographic distances assuming that the reviewers would be distributed and that each reviewer would change the underlying BBN network (to a certain extent) to suit his/her expertise. We highlight issues that arise due to a highly distributed and personalized BBN network that can be used to make valid inferences about learning object quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it