A smart system for student performance assessment (SPA)

Abstract

Abstract The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Keywords

• Expert systems
• Interference engine
• Knowledge base
• Students’ achievements
• Course learning outcomes