Outcome-Based Education: Evaluating Students' Perspectives Using Transformer

TL;DR

This study applies transformer-based NLP models, specifically DistilBERT, combined with LIME explanations, to analyze student feedback, enhancing outcome measurement and understanding in Outcome-Based Education.

Contribution

It introduces a transformer-based approach with interpretability tools for analyzing student feedback, improving sentiment classification in educational contexts.

Findings

Transformer models outperform traditional machine learning in sentiment analysis.

LIME provides clear explanations of model predictions.

Framework supports data-driven educational improvements.

Abstract

Outcome-Based Education (OBE) emphasizes the development of specific competencies through student-centered learning. In this study, we reviewed the importance of OBE and implemented transformer-based models, particularly DistilBERT, to analyze an NLP dataset that includes student feedback. Our objective is to assess and improve educational outcomes. Our approach is better than other machine learning models because it uses the transformer's deep understanding of language context to classify sentiment better, giving better results across a wider range of matrices. Our work directly contributes to OBE's goal of achieving measurable outcomes by facilitating the identification of patterns in student learning experiences. We have also applied LIME (local interpretable model-agnostic explanations) to make sure that model predictions are clear. This gives us understandable information about how key terms affect sentiment. Our findings indicate that the combination of transformer models and LIME explanations results in a strong and straightforward framework for analyzing student feedback. This aligns more closely with the principles of OBE and ensures the improvement of educational practices through data-driven insights.