updated schedule with accepted papers

Author: jakubkuzilek

content/schedule.md

@@ -29,37 +29,29 @@ The schedule is as follows:
 
 ## Accepted papers
 
-To be announced.
-
-<!--
-
-The official joint proceedings (with the [L3MNGET Workshop](https://sites.google.com/view/llmworkshopedm/home)) can be found at [CEUR-WS here](https://ceur-ws.org/Vol-3840/).
+<!--The official joint proceedings (with the [L3MNGET Workshop](https://sites.google.com/view/llmworkshopedm/home)) can be found at [CEUR-WS here](https://ceur-ws.org/Vol-3840/).-->
 
 
 ### Research papers
 
-- **The Actionable Explanations for Student Success Prediction Models: A Benchmark Study on the Quality of Counterfactual Methods** [<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper1.pdf" target="_blank">paper @ CEUR-WS</a>]\
+- **Explainable Survival Student Dropout Prediction Models: A Case Study on Open University Learning Analytics Dataset** <!--[<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper1.pdf" target="_blank">paper @ CEUR-WS</a>]-->\
 *Mustafa Cavus and Jakub Kuzilek*
-- **Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks** [<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper2.pdf" target="_blank">paper @ CEUR-WS</a>]\
-*Yuang Wei, Yizhou Zhou, Yuan-Hao Jiang and Bo Jiang*
-- **Combining Cognitive and Generative AI for Self-Explanation in Interactive AI Agents** [<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper3.pdf" target="_blank">paper @ CEUR-WS</a>]\
-*Shalini Sushri, Rahul Dass, Rhea Basappa, Hong Lu and Ashok Goel*
-
-### Position papers
-
-- **Towards a Unified Framework for Evaluating Explanations** [<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper4.pdf" target="_blank">paper @ CEUR-WS</a>]\
+- **Human-grounded XAI: An evaluation of explanation faithfulness and intelligibility for interpretable neural networks** <!--[<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper2.pdf" target="_blank">paper @ CEUR-WS</a>]-->\
 *Juan Pinto and Luc Paquette*
+- **Human Experts vs. LLMs: Who Is Better at Explaining Student Clustering?** <!--[<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper3.pdf" target="_blank">paper @ CEUR-WS</a>]-->\
+*Elad Yacobson, Shelley Rap, Ron Blonder and Giora Alexandron*
+- **RegKT: Interpretable and robust Deep Knowledge Tracing with IRT-regularizer** <!--[<a href="https://ceur-ws.org/Vol-3840/HEXED24_paper3.pdf" target="_blank">paper @ CEUR-WS</a>]-->\
+*Samuel Girard, Juan Pinto, Jill-Jênn Vie and Amel Bouzeghoub*
+
+### Encore papers
 
+- **Improving Course Recommendation Systems with Explainable AI: LLM-Based Frameworks and Evaluations** <!--[<a href="https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.69/index.html" target="_blank">paper @ EDM proceedings</a>]-->\
+*Jiawei Li, Qianru Lyu, Wei Qiu and Andy W. H. Khong*
 
-## Encore papers
 
-- **Making Course Recommendation Explainable: A Knowledge Entity-Aware Model using Deep Learning** [<a href="https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.69/index.html" target="_blank">paper @ EDM proceedings</a>]\
-*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?** [<a href="https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.70/index.html" target="_blank">paper @ EDM proceedings</a>]\
+<!-- - **How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?** [<a href="https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.70/index.html" target="_blank">paper @ EDM proceedings</a>]\
 *Subhankar Maity, Aniket Deroy and Sudeshna Sarkar*
 - **Easing the Prediction of Student Dropout for everyone by integrating AutoML and Explainable Artificial Intelligence** [<a href="https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.98/index.html" target="_blank">paper @ EDM proceedings</a>]\
 *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** [<a href="https://educationaldatamining.org/edm2022/proceedings/2022.EDM-long-papers.9/" target="_blank">paper @ EDM proceedings</a>]\
-*Vinitra Swamy, Bahar Radmehr, Natasa Krco, Mirko Marras and Tanja Käser*
-
--->
+*Vinitra Swamy, Bahar Radmehr, Natasa Krco, Mirko Marras and Tanja Käser*-->