Publications de Vinitra Swamy | EPFL Graph Search

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Deep neural networks form the backbone of artificial intelligence (AI) research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

In recent decades, we have witnessed the democratization of AI-powered Educational Technology (AI-EdTech). However, despite the increased accessibility and evolving technological capabilities, its adoption is accompanied by significant challenges, predomin ...

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting point for downstream tasks. While these models are undeniably useful, it is a c ...

The increasing impact of machine learning and algorithmic decision making on education has brought about growing opportunities and concerns. Evidence has shown that these technologies can perpetuate and even magnify existing educational and social inequiti ...

Feedback plays a crucial role in learning by helping individuals understand and improve their performance. Yet, providing timely, personalized feedback in higher education presents a challenge due to the large and diverse student population, often resultin ...

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extr ...

Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a cou ...

Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art models are understandable for non-technical users such as educators and students. In response, we introduce iLLuMin ...

Vinitra Swamy

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A Human-Centric Approach to Explainable AI for Personalized Education

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Interpreting Language Models Through Knowledge Graph Extraction

FATED 2022: Fairness, Accountability, and Transparency in Educational Data

AI or Human? Evaluating Student Feedback Perceptions in Higher Education

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

Meta Transfer Learning for Early Success Prediction in MOOCs

iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback