Linear models of student skills for static data.
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
Abstract. Current student skills models rely on non linear models such as Bayesian Networks and Bayesian Knowledge Tracing, and on general linear models, such as IRT which can be considered a logistic regression. Only a handful of recent studies have looked at linear models based on matrix factorization techniques. These studies obtained good success over data from dynamic student knowledge states when compared with widely used techniques such as Bayesian Knowledge Tracing. However, there are no reports of linear models applied to static knowledge states data. We introduce different linear models of student skill for small, static student test data that does not contain missing values. We compare their predictive performance the traditional psychometric Item Response Theory approach, and the k-nearest-neighbours approach that is widely used in recommender systems. The results show that that the IRT model is far better than all others. These results are somewhat unexpected given the recent relative success of factorization models for dynamic student test data. They raise the question of whether there is still a large amount of potential performance gain from other non-linear models for dynamic data. 1
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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