Student modeling is central to many educational technologies as it enables the prediction of future learning outcomes and targeted instructional strategies. However, open-ended learning environments pose challenges for accurately modeling students due to the diverse behaviors exhibited by students and the absence of a well-defined set of learning skills. To approach these challenges, we explore the application of Large Language Models (LLMs) for in-context student modeling in open-ended learning environments. We introduce a novel framework, LLM-SS, that leverages LLMs for synthesizing student's behavior. More concretely, given a particular student's solving attempt on a reference task as observation, the goal is to synthesize the student's attempt on a target task. Our framework can be combined with different LLMs; moreover, we fine-tune LLMs using domain-specific expertise to boost their understanding of domain background and student behaviors. We evaluate several concrete methods based on LLM-SS using the StudentSyn benchmark, an existing student's attempt synthesis benchmark in visual programming. Experimental results show a significant improvement compared to baseline methods included in the StudentSyn benchmark. Furthermore, our method using the fine-tuned Llama2-70B model improves noticeably compared to using the base model and becomes on par with using the state-of-the-art GPT-4 model.

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