Titlebook: Generative Intelligence and Intelligent Tutoring Systems; 20th International C Angelo Sifaleras,Fuhua Lin Conference proceedings 2024 The E

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Improving LLM Classification of Logical Errors by Integrating Error Relationship into Promptst are used, the average classification performance is about 21% higher than the ones without them. We also conducted an experiment for exploiting the relations among errors in generating a new logical error dataset using LLMs. As there is very limited dataset for logical errors such benchmark datase

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Enhancement of Knowledge Concept Maps Using Deductive Reasoning with Educational Dataom forest, and hidden Markov model for three datasets of the company. Next, we derived additional prerequisite relationships by applying deductive reasoning. The results showed that the knowledge maps of the three datasets had accuracies of 59%, 55%, and 84%, respectively, which were 3%, 10%, and 4%

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Individualised Mathematical Task Recommendations Through Intended Learning Outcomes and?Reinforcemenhe process of creating a recommendation pool, experts identified the mathematical concept and the taxonomy level addressed by existing e-assessments in order to identify their possible association with ILOs. The RL agent utilizes this dynamic measurement of the student’s ILO progress - measured by t