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

The model for predicting students’ success in acquiring programming knowledge and skills is presented in this paper. In order to collect the data needed for development of the model, 159 undergraduate IT students from Faculty of Technical Sciences in Čačak were analyzed. Besides the score on programming knowledge test, the following 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. In order to predict students’ success in learning programming multilayer perceptron was used with backpropagation learning algorithm. The cross-validation methodology was used for the training and testing of the classifiers. Transformation process is performed on the points students achieved on the test in order to get three categories related to success. 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 model, a Web-based application for displaying the results was created. It is primarily intended for teachers with no experience in working with neural networks, who can use it for planning the teaching.