Development and Implementation of an Intelligent Tutoring System Using Bayesian Network in Teaching C# Programming Language

Juliet V. Menor
Page No. 1-12

The utilization of computer and educational technologies paved to the implementation of an intelligent tutoring system to proactively assist the student in their learning process, especially once a learning difficulty needs to be remediated. This study aims to develop an intelligent tutoring system that dynamically identifies the learning difficulty then employs Bayesian network to help students. Student’s responses to the programming questions or exercises were stored and collected as images running screenshots and converted into numerical weight as an input to the Bayesian. Control and an experimental group composed of 100 students were used in the study. Pre-test and post-test results were analyzed using standard deviation and statistical correlation. The results of mean scores and standard deviation show that there is a significant difference between the control and experimental group. Correlation results show no significant relation. Data from diagnostic and post-assessment is a highly significant difference based on academic performance, skill acquisition and problem solving. The experimental group performs better, an indication that the intelligent tutoring system with remediation is better than the control group. Based on the results, an intelligent tutoring system helps the students while doing the actual programming by employing the Bayesian network.
Keywords: Bayesian Network, Artificial, Intelligent, Programming Language, Academic Performance