Thank you to all participants, presenters, and attendees for making HEXED 2024 a success! We look forward to seeing you at future events. If you'd like to be added to our mailing list, please email Juan Pinto.
Schedule
The HEXED Workshop was held on Sunday, 14 July 2024 9:00AM–5:00PM UTC-4 (US-East). The schedule was as follows:
9:00–9:30am | Welcome and logistics |
9:30–10:30am | Poster session (+ lightning presentations) |
10:30–11:00am | Break + networking |
11:00am–12:00pm | Working session 1: Small group brainstorming |
12:00–1:00pm | Lunch break |
1:00–2:00pm | Keynote presentation: Personalized XAI (Cristina Conati) |
2:00–2:45pm | Working session 2: Framing problems and needs |
2:45–3:30pm | Panel |
3:30–4:00pm | Break + networking |
4:00–4:45pm | Working session 3: Creating a shared vision |
4:45–5:00pm | Closing thoughts |
Accepted papers
Research papers
- The Actionable Explanations for Student Success Prediction Models: A Benchmark Study on the Quality of Counterfactual Methods [paper]
Mustafa Cavus and Jakub Kuzilek - Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks [paper]
Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang and Bo Jiang - Combining Cognitive and Generative AI for Self-Explanation in Interactive AI Agents [paper]
Shalini Sushri, Rahul Dass, Rhea Basappa, Hong Lu and Ashok Goel
Position papers
- Towards a Unified Framework for Evaluating Explanations [paper]
Juan Pinto and Luc Paquette
Encore papers
- Making Course Recommendation Explainable: A Knowledge Entity-Aware Model using Deep Learning [paper @ EDM proceedings]
Tianyuan Yang, Baofeng Ren, Boxuan Ma, Md Akib Zabed Khan, Tianjia He and Shin’Ichi Konomi - How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors? [paper @ EDM proceedings]
Subhankar Maity, Aniket Deroy and Sudeshna Sarkar - Easing the Prediction of Student Dropout for everyone by integrating AutoML and Explainable Artificial Intelligence [paper @ EDM proceedings]
Pamela Buñay-Guisñan, Juan Alfonso Lara, Alberto Cano, Rebeca Cerezo and Cristóbal Romero - Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs [paper @ EDM proceedings]
Vinitra Swamy, Bahar Radmehr, Natasa Krco, Mirko Marras and Tanja Käser