Artificial Neural Network Model for Prediction of Students’ Success in Learning Programming

This paper presents the model for predicting students’ success in acquiring programming knowledge and skills. In order to collect the data needed for development of the model, 159 undergraduate IT students from Faculty of Technical Sciences in Cacak were tested. Other relevant data were also gathered for each student: high school, the subject he/she took at the entrance exam, size of student’s birthplace, average high school grade, points from high school, gender, previous education, existence of IT educational profile in high school, study year, percentage of attendance on classes, reason for enrolment, subjective assessment of preparedness for programming, solving sequential tasks, type of programming student prefers, subjective assessment of preparedness for working in industry, solving tasks with branching and cycle, solving complex tasks, knowledge level, formal education,  informal education, Kolb's learning style. Based on the results about the relevance of the parameters, the model reached an accuracy of 92.3%. In order to facilitate the use of the created model, a Web-based application for displaying the results were created. The application was primarily created for teachers with no experience in working with neural networks, who can use it for planning the teaching process based on prediction of students’ performance.