A Probabilistic Model for Knowledge Component Naming.
Bibliographic record
Abstract
Recent years have seen significant advances in automatic identification of the Q-matrix necessary for cognitive di-agnostic assessment. As data-driven approaches are intro-duced to identify latent knowledge components (KC) based on observed student performance, it becomes crucial to de-scribe and interpret these latent KCs. We address the prob-lem of naming knowledge components using keyword auto-matically extracted from item text. Our approach identifies the most discriminative keywords based on a simple proba-bilistic model. We show this is effective on a dataset from the PSLC datashop, outperforming baselines and retrieving unknown skill labels in nearly 50 % of cases. 1. OVERVIEW The Q-matrix, introduced by Tatsuoka [9], associates test items with attributes of students that the test intends to as-sess. A number of data-driven approaches were introduced to automatically identify the Q-matrix by mapping items to latent knowledge components (KCs), based on observed stu-dent performance [1, 6], using, e.g. matrix factorization [2, 8], clustering [5] or sparse factor analysis [4]. A crucial issue with automatic methods is that latent skills may be hard to describe and interpret. Manually-designed Q-matrices may also be insufficiently described. A data-generated descrip-tion is useful in both cases. We propose to extract keywords relevant to each KC from the textual content corresponding to each item. We build a simple probabilistic model, with which we score keywords. This proves surprisingly effective on a small dataset obtained from the PSLC datashop. 2. MODEL We focus on extracting keywords from the textual content
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".