Outcome-Based Education: Evaluating Students’ Perspectives Using Transformer

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 outperforms traditional machine learning models by leveraging the transformer’s deep understanding of language context to classify sentiment more accurately across multiple evaluation metrics. This directly contributes to OBE’s goal of achieving measurable learning outcomes.

We also applied LIME (Local Interpretable Model-Agnostic Explanations) to ensure transparency in model predictions. This provided interpretable insights into the influence of key terms on sentiment classification. Our results show that combining transformer models with LIME explanations provides a powerful and interpretable framework for analyzing student feedback — closely aligned with OBE’s goals of continuous improvement through data-driven insights.

Recommended citation: Shuvra Smaran Das, Anirban Saha Anik, Md. Kishor Morol, Mohammad Sakib Mahmood. (2024). "Outcome-Based Education: Evaluating Students Perspectives Using Transformer." ICCIT 2024.
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