A Fuzzy-Logic-Based Student Learning Assessment System for Outcome-Based Education

Contribution: This research designs a student evaluation framework integrating the fuzzy-logic system that assesses the student’s performances in the soft boundary system for outcome-based education (OBE), measuring the course learning outcome (CLO) and program learning outcome (PLO). The framework fills the gap between conventional grading methods and offers insights into learning for course assessment and continuous development. Background: A well-established evaluation technique is a requirement to deliver a productive, skilled, worthy, and compatible student and faculty. Moreover, OBE, with a documented and structured academic curriculum, has to ensure the accreditation of an academic program. Research Questions: What are the drawbacks of traditional student evaluation techniques? Does the proposed system work as a better, more reliable, and meaningful student evaluation method? Methodology: To assess, it considers the final examination paper containing several questions and continuous assessment comprising a few items like class tests, quizzes, viva voce, homework, etc., where the course teachers and moderators assign marks on these questions and items considering the CLOs, learning methods, and Bloom’s taxonomy. Then, the framework records and tracks the ratio of earned marks to assigned marks for the fuzzification, while the defuzzification computes the values indicating the CLOs and PLOs earned by a student. Findings: The results study cases for 40 courses of a particular student and analyze statistics for 100 students from the consecutive eight semesters. This fuzzy-logic-based evaluation technique is fairer, reliable, and unbiased to the learners and greatly helps to get accreditation and recognition for the degree worldwide.