Generalized Follow-Up WHODAS 2.0 Assessment Through Language Models and Adaptive Clustering Ensemble in Higher Education
Rakshil Vijaybhai Kevadiya, Abbas Akkasi, Kathleen Fraser, Boris Vukovic, Jessie Gunnell, Sonia Tanguay, Majid Komeili · 2025
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.