Employing artificial intelligence techniques for student performance evaluation and teaching strategy enrichment: An innovative approach

Abstract

An intelligent tutoring system is an excellent Artificial Intelligence (AI) alternative for the haunting problems of the teaching and evaluation system in university education. It evinces a paradigm shift in the current system by employing AI techniques to evaluate students’ performance and enrich the myriad teaching strategies. Unlike in regular classes where a teacher has to control 30 to 50 students, a teacher has to monitor hundreds of students, which is quite difficult and mentally exhausting. In such circumstances, mentors or teachers alone are not enough for monitoring the students and offering each student’s optimum attention and care. A new and original approach is needed to facilitate reliable and flexible methods of university student monitoring systems. The system should be able to evaluate the performance of many students, predict the final grade, and formulate intelligent decisions in real-time. Several computer-based models of AI are progressively performing an important role in teaching and performance evaluation of students. This paper proposes a new strategy to illustrate the advantages of applying AI techniques to predict the final grade of students. The validation process was carried out with the real-time 1000 students’ dataset of 12 core and 18 elective courses in Bachelor of Computer Science during the academic year 2018-2019. In this paper, hybrid SVM with a Fuzzy Expert System is proposed to show the techniques proficiency for teaching and students’ final grade prediction and the possibility of future work.