At the intersection of computer science and cognitive science, we conduct applied research into human-centered explainable AI and intelligent data-intensive systems and their application in technology-enhanced learning and knowledge management domains. Headed by Prof. Dr. Mohamed Amine Chatti, we take an interdisciplinary approach in designing and developing trustworthy human-centered AI technologies and novel analytics-driven methods and visualizations for tomorrow’s smart learning and working environments. We offer lectures, seminars, and practical courses in learning technologies, web technologies, data science, visual analytics, and human-AI interaction following a technology-enhanced, student-centered learning approach.

We're part of the Department of Human-centered Computing and Cognitive Science (HCCS), Faculty of Computer Science at the University of Duisburg-Essen, one of the top universities in Germany.

News & Events

Our papers “Top-Down vs. Bottom-Up Approaches for Automatic Educational Knowledge Graph Construction in CourseMapper” by Qurat Ul Ain (available on arXiv here) and “Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs” by Mohamed Abdelmagied (available on arXiv here) got accepted as full research papers on the 9th European MOOCs stakeholders Summit 2025 (EMOOCs 2025).

Our paper “The ISC Creator: Human-Centered Design of Learning Analytics Interactive Indicator Specification Cards” by Shoeb Joarder got accepted as a full research paper on the 17th International Conference on Education Technology and Computers (ICETC 2025). A link to the paper on arXiv is available here.

In the upcoming summer semester 2025, we will offer a lab course on "Human-Centered Learning Analytics (HCLA)". In this lab, bachelor and master students from different study programs at the Faculty of Computer Science will work together on the evaluation and further development of transparent and interactive user interfaces that enable different stakeholders (e.g., students, teachers, researchers) to co-design and implement learning analytics indicators and dashboards with minimum effort. More information about the lab is available here. Registration is still possible until March 17th. Places are limited.

The Social Computing group was present at the 18th ACM Conference on Recommender Systems (RecSys 2024) in Bari, Italy, from October 14-18, 2024. RecSys is the premier international forum for the presentation of new research results, systems, and techniques in the broad field of recommender systems, well attended by researchers and industries around the globe. We were excited to present our latest work on user control and transparency at the 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS). At the workshop, Qurat Ul Ain presented our paper titled "Designing and Evaluating an Educational Recommender System with Different Levels of User Control", contributing valuable insights into the impact of control levels on transparency, trust, and satisfaction. Here is the link to the paper.

On September 2 & 3, 2024, Qurat Ul Ain and Shoeb Joarder attended the 3rd Learning AID at the Ruhr-University of Bochum. The conference and workshop were organized by the state-funded project KI:edu.nrw – a project to promote Didactics, Ethics, and Technology of Learning Analytics and AI in Higher Education.  The event focused on the practical use of learning analytics, artificial intelligence, and data mining in higher education.

Qurat presented our ongoing research on transparent recommender systems and demonstrated how our MOOC platform CourseMapper enables user interaction and control over the input, process, and output of an educational recommender system to enhance the learning experience. CourseMapper is available here.

Shoeb presented our ongoing Human-Centered Learning Analytics (HCLA) research and demoed the Indicator Editor in our Open Learning Analytics Platform (OpenLAP). The Indicator Editor provides an intuitive user interface that supports self-service learning analytics by empowering end-users to take control of the indicator development process. A demo can be found here.

After a lengthy review process, our paper "Visualization for Recommendation Explainability: A Survey and New Perspectives" has just been published in the prestigious ACM Transactions on Interactive Intelligent Systems and is now available online. This publication is particularly relevant for those who want to learn more about the theoretical background of visual explanations in AI and recommender systems and how to systematically develop explanatory visualizations in recommender systems. For more details, you can read the paper here. And, here is the link to the paper on arXiv.

The Social Computing Group was present at the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP'24) in Cagliari, Sardinia, Italy, from July 1-4, 2024. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. We presented our ongoing work on GNN-based user modeling in the context of our project CourseMapper. At the 1st International Workshop on User-Centered Practices of Knowledge Discovery in Educational Data (UKDE 2024), Rawaa Alatrash presented our paper titled "Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural Networks", available in UMAP24 Adjunct Proceedings here.

On June 19, 2024, Shoeb Joarder attended the event Learning Analytics in Practice (LAP24). LAP24 is a 24-hour, global online event that is intended to highlight and feature learning analytics practitioners around the world discussing and showcasing learning analytics in practice. Shoeb presented our ongoing research on Human-Centered Learning Analytics (HCLA) and gave a demo on the human-centered design and implementation of learning analytics indicators using OpenLAP. We received positive feedback from LAP24 participants, demonstrating the potential of the OpenLAP ISC Creator and OpenLAP Editor to actively involve different stakeholders in the learning analytics process. A demo can be found here.

Our paper "Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural Networks" got accepted at the workshop on User-Centered Practices of Knowledge Discovery in Educational Data (UKDE 2024) in conjunction with the 32nd ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2024). A preprint version of the paper is available here.

Just in time for the start of the summer semester of 2024, we are super excited to announce the launch of the pilot phase of our MOOC platform CourseMapper. CourseMapper will be used this semester to support collaborative learning in our Advanced Web Technologies (AWT) lecture course and Interactive Data Exploration and Analytics (IDEA) lab course.

Please feel free to test CourseMapper yourself here. If you have any questions, need more information, would like to join us in the further development of CourseMapper, or would like to collaborate on a research project around CourseMapper to democratize learning, please do not hesitate to contact us.

The Social Computing Group was present at the prestigious International Learning Analytics and Knowledge Conference (LAK24) in Kyoto, Japan, from March 18-22, 2024. LAK is the premier conference in the field of learning analytics and educational data science. We presented two papers in the context of our ongoing projects CourseMapper and OpenLAP. Qurat Ul Ain presented our full paper "Learner Modeling and Recommendation of Learning Resources using Personal Knowledge Graphs", available as open access here. Shoeb Joarder presented our poster paper "A No-Code Environment for Implementing Human-Centered Learning Analytics Indicators", available in LAK24 Companion Proceedings here.

In our new paper "ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT", recently published in Computers and Education: Artificial Intelligence, we propose ConceptGCN, an end-to-end framework that combines knowledge graphs, graph convolutional networks, and transformer sentence encoders to enhance the representations of knowledge concepts and learner models and provide personalized and explainable recommendation of concepts in our MOOC platform CourseMapper. The paper is available as open access here.

Earlier News & Events