Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming

• URL: http://arxiv.org/abs/2310.10690v3
• Date: Fri, 3 May 2024 22:03:43 GMT
• Title: Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming
• Authors: Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla,
• Abstract summary: We explore the application of large language models (LLMs) for in-context student modeling in open-ended learning domains. We introduce a novel framework, LLM for Student Synthesis (LLM-SS), that leverages LLMs for a student's behavior. We instantiate several methods based on LLM-SS framework and evaluate them using an existing benchmark, StudentSyn, for student attempt synthesis in a visual programming domain.
• Score: 29.65988680948297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions. To approach these challenges, we explore the application of large language models (LLMs) for in-context student modeling in open-ended learning domains. More concretely, given a particular student's attempt on a reference task as observation, the objective is to synthesize the student's attempt on a target task. We introduce a novel framework, LLM for Student Synthesis (LLM-SS), that leverages LLMs for synthesizing a student's behavior. Our framework can be combined with different LLMs; moreover, we fine-tune LLMs to boost their student modeling capabilities. We instantiate several methods based on LLM-SS framework and evaluate them using an existing benchmark, StudentSyn, for student attempt synthesis in a visual programming domain. Experimental results show that our methods perform significantly better than the baseline method NeurSS provided in the StudentSyn benchmark. Furthermore, our method using a fine-tuned version of the GPT-3.5 model is significantly better than using the base GPT-3.5 model and gets close to human tutors' performance.

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