Call For Papers

Topics of Interests:
Topics of interest to the conference include but are not limited to:

• Developing new techniques for mining educational data
• Bridging EDM research and learning sciences
  • Informing data mining research with educational and/or motivational theories
  • Actionable advice rooted in educational data mining research, experiments, and outcomes
  • Evaluating the efficacy of curriculum and interventions
• Domain Knowledge Modeling
  • Deriving representations of domain knowledge from data
  • Algorithms for discovering relationships, associations, and prerequisite structures between learning resources
  • Algorithms to improve existing domain models
  • Human-in-the-loop methods to collect domain knowledge models, including crowd-sourcing and expert tagging
• Educational Recommenders, Instructional Sequencing, and Personalized Learning
  • Learning resource recommendation algorithms, remedial recommendations, and learner
  • choice in selecting the next activity
  • Goal-oriented instructional sequencing
  • Personalized course recommendations
  • Peer recommendation for collaborative learning
  • Methodologies for evaluating educational recommender systems and sequencing algorithms
• Equity, Privacy, Transparency, and Fairness
  • Ethical considerations in EDM
  • Legal and social policies to govern EDM
  • Developing privacy-protecting EDM algorithms and detecting learner privacy violations in
  • existing methods
  • Developing and applying fairer learning algorithms, and detecting and correcting instances of algorithmic unfairness in existing methods
• Human Factors, Transparency, and Explainability
  • Developing, improving, and evaluating explainable schemes for EDM systems
  • Human factors that shape educational users’ acceptance of EDM-based
  • recommendations
  • Educational users’ trust and attitudes towards EDM technologies
• Learner Cognitive and Behavior Modeling and its association with performance
  • Modeling and detecting students’ affective and cognitive states (e.g., engagement, confusion) with multimodal data
  • Temporal patterns in student behavior including gaming the system, procrastination, and
  • sequence modeling
  • Data mining to understand how learners interact with various pedagogical environments such as educational games and exploratory learning environments
• Learner Knowledge and Performance Modeling
  • Automatically assessing student knowledge
  • Learner knowledge gain and forgetting models in domains with complex concept structures
  • Modeling real-world problem-solving in open-ended domains
  • Causal inference of students’ learning
  • Predicting students’ future performance
  • Detecting cheating and academic dishonesty
• Comparing Human and Artificial Intelligence
  • Analyzing generative AI’s capabilities to perform complex learning tasks
  • Developing methods to identify the use of generative AI in learning environments (e.g., for cheating)
• Learning Analytics
  • Institutional analytics
  • Learner profiling
  • Multimodal analytics
• Social and Collaborative Learning
  • Modeling student and group verbal and non-verbal interactions for collaborative and/or competitive problem-solving
  • Social network analysis of student and teacher interactions
  • Data mining to understand how learners interact in formal and informal educational contexts
  • Peer-assessment modeling
  • Social learner modeling
• Reproducibility
  • Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts
  • Facilitating accessible benchmarking systems and publishing educational datasets that are useful for the community