HEXED Workshop

Schedule

The HEXED Workshop was held on Sunday, 14 July 2024 9:00AM–5:00PM UTC-4 (US-East). The schedule was as follows:

9:00–9:30amWelcome and logistics
9:30–10:30amPoster session (+ lightning presentations)
10:30–11:00amBreak + networking
11:00am–12:00pmWorking session 1: Small group brainstorming
12:00–1:00pmLunch break
1:00–2:00pmKeynote presentation: Personalized XAI (Cristina Conati)
2:00–2:45pmWorking session 2: Framing problems and needs
2:45–3:30pmPanel
3:30–4:00pmBreak + networking
4:00–4:45pmWorking session 3: Creating a shared vision
4:45–5:00pmClosing thoughts

Accepted papers

The official joint proceedings (with the L3MNGET Workshop) can be found at CEUR-WS here.

Research papers

  • The Actionable Explanations for Student Success Prediction Models: A Benchmark Study on the Quality of Counterfactual Methods [paper @ CEUR-WS]
    Mustafa Cavus and Jakub Kuzilek
  • Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks [paper @ CEUR-WS]
    Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang and Bo Jiang
  • Combining Cognitive and Generative AI for Self-Explanation in Interactive AI Agents [paper @ CEUR-WS]
    Shalini Sushri, Rahul Dass, Rhea Basappa, Hong Lu and Ashok Goel

Position papers

  • Towards a Unified Framework for Evaluating Explanations [paper @ CEUR-WS]
    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