LLMs for Structured Educational Data: A Zero and Few-Shot Approach to Student Performance Prediction

Early and accurate prediction of student performance is crucial for enabling timely academic support to the at-risk students. The past work have used machine learning and deep learning approaches on educational datasets such as OULAD and achieved strong results for prediction of student outcomes. But they relied on feature engineering, required retraining and did not offered any interpretability. This study tries to check whether large language models (LLMs) can be used to overcome these limitations by using zero and few-shot prompting strategies on structured tabular datasets. Evaluation has been done on three open-source LLMs across binary, multiclass and per-class settings. The models reached up to accuracies of 89.6% and 75.9% on binary and multi-class predictions respectively, all without the need for fine-tuning. They even produced short natural language explanations to describe why a particular prediction was made. The findings show that LLMs can not only become an alternative to the traditional prediction models but can also guide educators to make informed decisions through their explanation based insights.