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 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".