Intelligent Tutoring System Using Bayesian Network for Vocational High Schools in Indonesia

Abstract

The lack of personal tutorials during school hours caused the learning approach at Vocational High Schools to be less optimal, and the student's competence is not achieved maximumly. Some computer-based self-learning systems have been developed as solutions to these problems. Unfortunately, the system's weakness is that learning does not pay attention to the diversity of students' abilities. Based on those, this research proposes an Intelligent Tutoring System (ITS) model using Bayesian Network at Vocational High Schools (SMK) to determine the level of students' abilities and teach skills competency materials based on each student's ability level. This is quantitative research with quasi-experimental using one group pretest-posttest design. The research participants were 69 students of the Computer and Network Engineering expertise program at SMK Negeri 4 Gowa and SMK Negeri 1 Gowa, South Sulawesi Province, Indonesia. The results showed significant differences in students' learning outcomes after the use of the proposed ITS; in other words, the proposed ITS was effective in increasing the skill competency of Vocational High School Students. The evaluation results showed that the created Bayesian Network model had a high level of accuracy, reaching 84%.