6 citations · 9 across the 4 of their papers we have counts for
4 papers
Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection
Isadora Salles, Paola Mejia-Domenzain, Vinitra Swamy +2
Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectivenes…
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
Thiemo Wambsganss, Xiaotian Su, Vinitra Swamy +3
Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. Despite their potential, LLMs are known to harbor inh…
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Vinitra Swamy, Malika Satayeva, Jibril Frej +5
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potenti…
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Vinitra Swamy, Bahar Radmehr, Natasa Krco +2
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainab…