Generalized Follow-Up WHODAS 2.0 Assessment Through Language Models and Adaptive Clustering Ensemble in Higher Education
Bibliographic record
Abstract
This paper addresses the challenge of automating the process of generating personalized follow-up questions (FQs) for students with disabilities based on their responses to the WHODAS 2.0 questionnaire. Given the diverse nature of FQs generated by disability service providers, our research aims to cluster these questions using advanced language models and ensemble clustering techniques. We utilized three different Sentence-Transformers embedding models (RoBERTa, MiniLM and MPNet) combined with clustering algorithms such as HDBSCAN, K-Means, BIRCH, Spectral Clustering, and Gaussian Mixture Models. Furthermore, an Adaptive Clustering Ensemble (ACE) method was employed to improve clustering performance. The results indicate that the ensemble method achieves greater stability and accuracy in clustering compared to individual models. Our findings demonstrate the potential of using AI to streamline the process of assessing and supporting students with disabilities in postsecondary education settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".