Towards more effective summative assessment in OBE: a new framework integrating direct measurements and technology

Abstract This paper contributes to the ongoing efforts aimed at enhancing Outcome-Based Education (OBE) assessment methodologies by addressing some critical gaps and exploring new solutions. Our work focuses on two main areas: firstly, this study proposes an improved assessment method for OBE. It refines traditional approaches by classifying course materials according to their relevance to learning outcomes, weighting them by importance, connecting these outcomes to student goals and assigning difficulty levels to modules. All modules are directly assessed through a final exam with a consistent rubric, and student success is measured by a holistic score that considers the weighted attainment levels across all learning outcomes and modules. Secondly, this paper provides theoretical guidance for integrating Generative Artificial Intelligence (AI) and blockchain technologies into OBE assessment. It examines the potential impact of these technologies at various assessment stages, laying the groundwork for practical implementation.