Revolutionizing Student Success Prediction with Large Language Models

A groundbreaking study is reimagining the way student success is predicted using Artificial Intelligence (AI). By applying Large Language Models (LLMs) to classification tasks, researchers have made significant strides in improving accuracy and explainability. The study proposes repurposing LLMs to predict student success, leveraging their ability to process and analyze large amounts of data. Despite promising results, traditional Machine Learning algorithms still outperform LLMs in this specific task. This innovative approach has far-reaching implications for educators and researchers, enabling them to better support students and improve overall educational outcomes.

Reimagining Student Success Prediction: A New Era for Educational AI

The application of Large Language Models (LLMs) in various fields has been on the rise, and their potential in Educational AI (EdAI) is no exception. LLMs have shown remarkable flexibility in performing natural language processing tasks such as question answering, text generation, and text summarization. However, despite their versatility, LLMs are often applied to generative AI tasks like text-to-image generation.

This paper aims to explore the application of LLMs for a classification task in EdAI by re-proposing the original PreSS (Predicting Student Success) model. The PreSS model uses traditional Machine Learning (ML) algorithms to predict CS1 students at risk of failing or dropping out. The authors have two primary goals: first, to identify the best and most accurate method to repurpose LLMs for a classification task; second, to explore and assess the explainability of the model outputs.

Investigating LLM Techniques for Student Success Prediction

To achieve their first goal, the researchers investigate different techniques for using LLMs, including FewShot Prompting, Fine-Tuning, and Transfer Learning using Gemma 2B as a base model. Additionally, they examine two distinct prompting techniques to determine which approach yields the most accurate results.

The authors employ various LLM techniques to predict student success, including:

• FewShot Prompting: This technique involves providing the LLM with a few examples of input-output pairs to fine-tune its performance.
• Fine-Tuning: In this method, the researchers adjust the pre-trained LLM’s weights to fit their specific task.
• Transfer Learning: The authors utilize Gemma 2B as a base model and adapt it for student success prediction.

The results of these experiments are then compared with the original PreSS model to evaluate whether LLMs can outperform traditional ML algorithms. Notably, Naïve Bayes still emerges as the best algorithm for predicting student success, once again confirming its superiority in this domain.

Exploring Explainability in LLMs for Student Success Prediction

To address their second goal, the researchers focus on attention scores of LLM transformers to understand which features the model considers when generating responses. By analyzing these attention scores, the authors aim to identify the most important factors that influence student success prediction.

The obtained results demonstrate that LLMs can provide valuable insights into the decision-making process behind student success predictions. However, it is essential to note that Naïve Bayes still outperforms all other models in this task.

The Role of Explainability in Educational AI

Explainability plays a crucial role in EdAI, as it enables educators and policymakers to understand the reasoning behind model predictions. By providing transparent and interpretable results, LLMs can help identify areas where students may need additional support or resources.

The authors emphasize the importance of explainability in EdAI, stating that it is essential for building trust between stakeholders and ensuring that models are fair and unbiased. By incorporating explainability into their research, the researchers aim to create more transparent and accountable AI systems.

Implications for Educational AI

The findings of this study have significant implications for EdAI. Firstly, they highlight the potential of LLMs in predicting student success, which can inform targeted interventions and support strategies. Secondly, the results demonstrate the importance of explainability in EdAI, emphasizing the need for transparent and interpretable models.

The authors conclude that their research contributes to a more comprehensive understanding of LLMs in EdAI, shedding light on their potential applications and limitations. By exploring the intersection of LLMs and explainability, the researchers aim to create more effective and accountable AI systems that support student success.

The study’s authors identify several avenues for future research, including:

• Investigating other LLM techniques: The researchers suggest exploring additional LLM methods, such as attention-based models or graph neural networks.
• Developing more sophisticated explainability techniques: The authors propose developing more advanced explainability methods to provide deeper insights into model decision-making processes.
• Applying LLMs in real-world educational settings: The researchers emphasize the need for further research on applying LLMs in actual educational contexts, with a focus on scalability and practicality.

By pursuing these avenues of research, the authors aim to advance our understanding of LLMs in EdAI and contribute to the development of more effective AI systems that support student success.