10 papers
Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
Pengfei Li, Mohammad Khalil
While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundament…
Causal Pre-training Under the Fairness Lens: An Empirical Study of TabPFN
Qinyi Liu, Mohammad Khalil, Naman Goel
Foundation models for tabular data, such as the Tabular Prior-data Fitted Network (TabPFN), are pre-trained on a massive number of synthetic datasets generated by structural causal…
Brief but Impactful: How Human Tutoring Interactions Shape Engagement in Online Learning
Conrad Borchers, Ashish Gurung, Qinyi Liu +3
Learning analytics can guide human tutors to efficiently address motivational barriers to learning that AI systems struggle to support. Students become more engaged when they recei…
Measuring the Impact of Student Gaming Behaviors on Learner Modeling
Qinyi Liu, Lin Li, Valdemar Švábenský +2
The expansion of large-scale online education platforms has made vast amounts of student interaction data available for knowledge tracing (KT). KT models estimate students' concept…
Quality Degradation Attack in Synthetic Data
Qinyi Liu, Dong Liu, Sam Urmian +2
Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the rec…
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Mohammad Khalil, Ronas Shakya, Qinyi Liu
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. Whil…