Intelligent tutoring systems’ measurement and prediction of students’ performance using predictive function

Abstract

Students’ performance in online learning has gained new interest due to the adaptation of Artificial Intelligence into the education sector. The Intelligent Tutoring System’s evolution is still ongoing with continuous enhancement being embedded inside the system with various purposes related to learning measurement and performance evaluation. We have introduced a predictive function to determine the students’ performance with respect to their peers. The enhancement will enable the system to predict the performance of the students based on the students’ performance history and utilization of the intelligent system. Various data will be captured by the system to supply input to this predictive function. The new function considers time of independent study, confidence level during assessment, correctness of answers and average answering time for prediction. From the experiment and analysis conducted, we conclude that the new proposed predictive function is very accurate in predicting students’ future performance.