Automatic Question Generation for Cognitive Domain of Outcome-Based Education System

In recent years, the educational system has seen numerous improvements, including the addition of assessment criteria to evaluate educational outcomes. However, the manual creation of test questions often fails to accurately assess students’ competency levels and is time-consuming. This paper addresses the need for automated question generation (AQG) in the context of outcome-based education (OBE), a student-centric approach that has yet to incorporate AQG techniques. OBE, grounded in Bloom’s taxonomy, encompasses three domains: cognitive, psychomotor, and affective. Research focuses on the cognitive domain and its six levels of question generation. OBE algorithms for AQG are based on accuracy, time consumption, and question quality. The DistilBERT question-answering model and transformers for error correction are used in our AQG model. The model is trained on QGSTEC and assessed using performance measures. Comparatively, the model has higher accuracy, precision, and F1-score.