Tag
This paper introduces MBP-KT, a framework for enhanced knowledge tracing that leverages meta-behavioral patterns to extract global collaborative information from learner interactions, improving performance across various downstream models.
This paper introduces Context-Aligned Contrastive Regression to improve lexical difficulty prediction by addressing cross-lingual alignment and ordinal structure challenges in language learning datasets.
This paper introduces NSMQ Riddles, a novel benchmark using scientific and mathematical riddles from Ghana's National Science and Maths Quiz to evaluate Large Language Models, addressing the underrepresentation of Global South datasets in AI research.
Researchers from Arizona State University present a framework for evaluating adaptive personalization of educational reading materials using theory-grounded simulated learners, incorporating memory models, misconception revision, and Bayesian Knowledge Tracing. Experiments across three subjects show adaptive reading significantly improved outcomes in computer science but had mixed results in chemistry and biology.