update pages for 2nd HEXED iteration, init commit

Author: jakubkuzilek

content/_index.md

@@ -1,30 +1,41 @@
-The **HEXED (Human-Centric eXplainable AI in Education) Workshop** was held in conjunction with [EDM (Educational Data Mining) 2024](https://educationaldatamining.org/edm2024/). The workshop aimed to bring together a specialized community of researchers who can work together to (1) develop a shared vision and common vocabulary for XAI in education, (2) share and disseminate work, (3) create robust methods for increasing interpretability, and (4) develop evaluation metrics for assessing explanations and model interpretability. We planned to achieve this through lively debate and discussion surrounding the current and future needs of the community.
+The **The 2nd Human-Centric eXplainable AI in Education (HEXED) Workshop** was held in conjunction with [EDM (Educational Data Mining) 2025](https://educationaldatamining.org/edm2025/). The workshop aims to bring together a specialized community of researchers who can work together to (1) establish a shared vision and common vocabulary for XAI in education, (2) facilitate the exchange of recent research and best practices, (3) brainstorm practical methods to enhance model transparency that are specific to the education domain, and (4) define evaluation metrics for assessing explanations and interpretability with teachers, students, and parents. We plan to achieve this through lively debate and discussion surrounding the current and future needs of the community.
 
-The workshop was held on **14 July 2024** in Atlanta, Georgia, USA. This is a full-day **hybrid** workshop and will feature a mix of poster presentations, a lively panel discussion, and interactive sessions to facilitate collaboration.
+The workshop will be held on **20 July 2024** in Palermo, Italy. This is a full-day **hybrid** workshop and will feature a mix of poster presentations, a lively panel discussion, and interactive sessions to facilitate collaboration.
+
+## Themes
+
+- The need for greater explainability in education.
+- The case for intrinsic vs. post-hoc explainability.
+- Ensuring explanation faithfulness.
+- Designing evaluation metrics and methods for assessing explanations and/or models.
+- Aligning explanations with teachers' and students' needs.
+- Generating actionable explanations as a basis for classroom interventions and personalized learning.
+- The role of LLMs in Explainable AI. 
 
 
 ## Proceedings
 
-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/).
+To be announced.
 
 
 ## Schedule
 
-The workshop was held from 9:00am to 5:00pm. [Click here](/schedule) for the schedule and accepted papers.
+The workshop will be held from 9:00am to 5:00pm. [Click here](/schedule) for the schedule and accepted papers.
 
 
 ## Registration
 
-The registration was managed by the EDM 2024 main conference organization at <https://educationaldatamining.org/edm2024/>. Registration is now closed.
+The registration is managed by the EDM 2025 main conference organization at <https://educationaldatamining.org/edm2025/>. 
 
 
 ## Organizers
 
 The workshop organizers are listed below. [Click here](/people) to learn more about them or to see the full program committee.
 
+- [Vinitra Swamy](https://vinitra.github.io), EPFL
+- [Jakub Kuzilek](https://jakubkuzilek.github.io/), Humboldt-Universität zu Berlin
 - [Juan D. Pinto](https://jdpinto.com), University of Illinois Urbana‐Champaign
 - [Luc Paquette](https://education.illinois.edu/faculty/luc-paquette), University of Illinois Urbana-Champaign
-- [Vinitra Swamy](https://vinitra.github.io), EPFL
 - [Tanja Käser](https://people.epfl.ch/tanja.kaeser/?lang=en), EPFL
 - [Qianhui (Sophie) Liu](https://education.illinois.edu/people/sophie-liu), University of Illinois Urbana-Champaign
 - [Lea Cohausz](https://lea-cohausz.github.io), University of Mannheim

content/call-for-papers.md

@@ -31,33 +31,33 @@ The workshop will cover a wide range of topics related to interpretable AI in ed
 
 ## Important dates
 
-- **10 May** (extended from ~~3 May~~): **Abstract submission deadline**
-- **17 May** (extended from ~~10 May~~): **Submission deadline for all papers**
-- **19 June:** Notification of acceptance
-- **29 June:** Camera-ready submission deadline
-- **14 July:** HEXED Workshop @ EDM 2024 (in-person in Atlanta, Georgia and online)
+- **17 May**: **Abstract submission deadline**
+- **24 May**: **Submission deadline for all papers**
+- **26 June:** Notification of acceptance
+- **06 July:** Camera-ready submission deadline
+- **20 July:** HEXED Workshop @ EDM 2025 (in-person in Palermo, Italy and online)
 
 **All deadlines are 11:59pm, AoE time (Anywhere on Earth).**
 
 
 ## Submission guidelines
 
-All papers must be submitted by ~~10 May~~ 17 May 2024 AoE (anywhere on Earth).
+All papers must be submitted by 24 May 2025 AoE (anywhere on Earth).
 
 We invite submissions in the following categories:
 
 - **Research papers (4-8 pages):** New or in-progress work related to human-centric explainability in education. Papers should be clearly placed with respect to the state of the art, state the contribution clearly and describe the methodology in detail.
 - **Position papers (4-6 pages):** Position papers are dedicated to technical and critical views and opinions on explainable AI in education, and should present a novel viewpoint on a problem, or on a novel solution to a problem. They do not need to contain primary research data, but are substantiated by facts or principled arguments to provide new insights or opinions to a debate.
 - **Encore Papers (no page restrictions):** This track is for recently published papers (2022 onwards) relevant to the workshop. Authors submit an abstract and a link or PDF of the recently published work.
 
-Submitted papers should be formatted using the [EDM submission templates available here](https://educationaldatamining.org/edm2024/instructions-for-authors/), and submitted to the [HEXED Workshop on EasyChair](https://easychair.org/my/conference?conf=hexed2024). Accepted papers will be invited to present at a poster session, and presenters may be invited to give spotlight talks. References do not count towards the page limit. Authors can also include appendices to more clearly describe datasets and tools if necessary, and these do not count toward the page limit.
+Submitted papers should be formatted using the [EDM submission templates available here](https://educationaldatamining.org/edm2025/instructions-for-authors/), and submitted to the [HEXED Workshop on EasyChair](https://easychair.org/my/conference?conf=hexed2025). Accepted papers will be invited to present at a poster session, and presenters may be invited to give spotlight talks. References do not count towards the page limit. Authors can also include appendices to more clearly describe datasets and tools if necessary, and these do not count toward the page limit.
 
 All submitted papers should be carefully blinded for review. Take care to remove all authors' names and identifying information (e.g. grant numbers), and refer to any of your prior work in the third person (e.g. "Previously, Smith et al. did ... [1]" rather than "In our prior work [1]").
 
 
 ## Attendance
 
-Accepted authors and all other attendees (both in-person and remote) will be required to register for the workshop. Registration will be managed by the EDM 2024 main conference organization at <https://educationaldatamining.org/edm2024/>. Registration is yet to open.
+Accepted authors and all other attendees (both in-person and remote) will be required to register for the workshop. Registration will be managed by the EDM 2024 main conference organization at <https://educationaldatamining.org/edm2025/>. Registration is yet to open.
 
 
 ## Publication

content/people.md

@@ -1,11 +1,13 @@
 # Organizers
 
+[**Vinitra Swamy**](https://vinitra.github.io) is a PhD student at EPFL. Her research with the ML4ED lab involves explainable AI for education, especially through the lens of reducing adoption barriers for neural networks. Her recent work focuses on uncovering disagreement in post-hoc explainers, using learning science experts to validate explainer accuracy and actionability, and proposing interpretable-by-design neural network architectures.
+
+[**Jakub Kuzilek**](https://jakubkuzilek.github.io/) is affiliated as a postdoctoral researcher with the Computer Science Education / Computer Science and Society research group at Humboldt-Universität zu Berlin. His research focuses on learning environments powered by machine learning systems development, explainable machine learning in education and predictive analysis of student behaviour in online environments.
+
 [**Juan D. Pinto**](https://jdpinto.com) is a PhD student at the University of Illinois Urbana‐Champaign. His research involves the development of learner models using machine learning methods and tackling issues of AI interpretability in education. He is currently conducting work as a member of the Human‐centered Educational Data Science (HEDS) Lab and the NSF AI Institute for Inclusive Intelligent Technologies for Education (INVITE).
 
 [**Luc Paquette**](https://education.illinois.edu/faculty/luc-paquette) is an associate professor in the department of curriculum \& instruction at the University of Illinois Urbana-Champaign. His research focuses on the usage of machine learning, data mining and knowledge engineering approaches to analyze and build predictive models of the behavior of students as they interact with digital learning environments such as MOOCs, intelligent tutoring systems, and educational games. He is interested in studying how those behaviors are related to learning outcomes and how predictive models of those behaviors can be used to better support the students' learning experience.
 
-[**Vinitra Swamy**](https://vinitra.github.io) is a PhD student at EPFL. Her research with the ML4ED lab involves explainable AI for education, especially through the lens of reducing adoption barriers for neural networks. Her recent work focuses on uncovering disagreement in post-hoc explainers, using learning science experts to validate explainer accuracy and actionability, and proposing interpretable-by-design neural network architectures.
-
 [**Tanja Käser**](https://people.epfl.ch/tanja.kaeser/?lang=en) is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the Machine Learning for Education (ML4ED) laboratory. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning, with a focus on building models that are generalizable, interpretable, and fair.
 
 [**Qianhui (Sophie) Liu**](https://education.illinois.edu/people/sophie-liu) is a PhD student at the University of Illinois Urbana-Champaign. Her research in the HEDS lab focuses on applying data mining methods in combination with learning science theories to help improve the efficiency of teaching and learning in various educational settings. She is interested in closing the loop of machine learning to humans for actionable insights through explainable models and techniques.
@@ -17,8 +19,11 @@
 
 - **Giora Alexandron**, Assistant Professor, Weizmann Institute of Science, Israel
 - **Ryan Baker**, Professor, University of Pennsylvania, USA
-- **Anthony Botelho**, Assistant Professor of Educational Technology, University of Florida, USA
+- **Przemyslaw Biecek**, Professor, Warsaw University of Technology
 - **Nigel Bosch**, Assistant Professor of Information Sciences, University of Illinois Urbana-Champaign, USA
+- **Anthony Botelho**, Assistant Professor of Educational Technology, University of Florida, USA
+- **Mustafa Cavus**, Assistant Professor, Eskisehir Technical University
+- **Cristina Conati**, Professor, The University of British Columbia
 - **Jibril Frej**, Postdoctoral Researcher, EPFL, Switzerland
 - **Ashish Gurung**, Postdoctoral Researcher, Carnegie Mellon University, USA
 - **Paul Hur**, PhD Student, University of Illinois Urbana-Champaign, USA
@@ -27,3 +32,4 @@
 - **Anna Rafferty**, Associate Professor of Computer Science, Carleton College, USA
 - **Yang Shi**, PhD Student, North Carolina State University, USA
 - **Diego Zapata-Rivera**, Director of LAFI research center, Educational Testing Service, USA
+

content/previous.md

@@ -0,0 +1,80 @@
+# 1st HEXED (The 1st Human-Centric eXplainable AI in Education) Workshop
+
+The **HEXED (Human-Centric eXplainable AI in Education) Workshop** was held in conjunction with [EDM (Educational Data Mining) 2024](https://educationaldatamining.org/edm2024/). The workshop aimed to bring together a specialized community of researchers who can work together to (1) develop a shared vision and common vocabulary for XAI in education, (2) share and disseminate work, (3) create robust methods for increasing interpretability, and (4) develop evaluation metrics for assessing explanations and model interpretability. We planned to achieve this through lively debate and discussion surrounding the current and future needs of the community.
+
+The workshop was held on **14 July 2024** in Atlanta, Georgia, USA. This is a full-day **hybrid** workshop and will feature a mix of poster presentations, a lively panel discussion, and interactive sessions to facilitate collaboration.
+
+
+## Proceedings
+
+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/).
+
+
+## Schedule
+
+The workshop was held from 9:00am to 5:00pm.
+
+- <a href="https://tinyurl.com/hexed2024" target="_blank">Resources working doc</a>
+
+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*](/HEXED_2024-keynote_slides.pdf) (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
+
+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>]\
+*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>]\
+*Juan Pinto and Luc Paquette*
+
+
+### 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>]\
+*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*
+
+## Registration
+
+The registration was managed by the EDM 2024 main conference organization at <https://educationaldatamining.org/edm2024/>. Registration is now closed.
+
+
+## Organizers
+
+The workshop organizers are listed below. [Click here](/people) to learn more about them or to see the full program committee.
+
+- [Juan D. Pinto](https://jdpinto.com), University of Illinois Urbana‐Champaign
+- [Luc Paquette](https://education.illinois.edu/faculty/luc-paquette), University of Illinois Urbana-Champaign
+- [Vinitra Swamy](https://vinitra.github.io), EPFL
+- [Tanja Käser](https://people.epfl.ch/tanja.kaeser/?lang=en), EPFL
+- [Qianhui (Sophie) Liu](https://education.illinois.edu/people/sophie-liu), University of Illinois Urbana-Champaign
+- [Lea Cohausz](https://lea-cohausz.github.io), University of Mannheim

content/proceedings.md

@@ -0,0 +1,5 @@
+# Proceedings
+
+## 1st HEXED (The 1st Human-Centric eXplainable AI in Education) Workshop
+
+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/).

content/review-guidelines.md

@@ -5,7 +5,7 @@ We are committed to setting a high standard of quality and integrity for the HEX
 - To decide fairly whether each paper is worthy of acceptance; and
 - To provide the authors with feedback on how to improve the quality of their research and writing.
 
-With these goals in mind, we request that all reviewers follow the review guidelines below (modified from the [EDM 2024 guidelines](https://educationaldatamining.org/edm2024/review-guidelines/)).
+With these goals in mind, we request that all reviewers follow the review guidelines below (modified from the [EDM 2025 guidelines](https://educationaldatamining.org/edm2025/review-guidelines/)).
 
 ## Reviews should include
 

content/schedule.md

@@ -1,5 +1,8 @@
 # Schedule
 
+To be announced.
+
+<!--
 - <a href="https://tinyurl.com/hexed2024" target="_blank">Resources working doc</a>
 
 The HEXED Workshop was held on **Sunday, 14 July 2024 9:00AM–5:00PM UTC-4 (US-East)**. The schedule was as follows:
@@ -50,3 +53,5 @@ The official joint proceedings (with the [L3MNGET Workshop](https://sites.google
 *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*
+
+-->