Machine learning methods in predicting the student academic motivation

Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS) courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines) were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course

Predicting student satisfaction with courses based on log data from a virtual learning environment – a neural network and classification tree model

Student satisfaction with courses in academic institutions is an important issue and is recognized as a form of support in ensuring effective and quality education, as well as enhancing student course experience. This paper investigates whether there is a connection between student satisfaction with courses and log data on student courses in a virtual learning environment. Furthermore, it explores whether a successful classification model for predicting student satisfaction with course can be developed based on course log data and compares the results obtained from implemented methods. The research was conducted at the Faculty of Education in Osijek and included analysis of log data and course satisfaction on a sample of third and fourth year students. Multilayer Perceptron (MLP) with different activation functions and Radial Basis Function (RBF) neural networks as well as classification tree models were developed, trained and tested in order to classify students into one of two categories of course satisfaction. Type I and type II errors, and input variable importance were used for model comparison and classification accuracy. The results indicate that a successful classification model using tested methods can be created. The MLP model provides the highest average classification accuracy and the lowest preference in misclassification of students with a low level of course satisfaction, although a t-test for the difference in proportions showed that the difference in performance between the compared models is not statistically significant. Student involvement in forum discussions is recognized as a valuable predictor of student satisfaction with courses in all observed models

Smartphone activities in predicting tendency towards online financial services

Purpose: Considering the rapid progress of information and communication technology (ICT) and its influence on daily life, it is inevitable that its impact will also be visible in the financial sector, especially through efforts to present digital financial services as widely as possible and bring them closer to potential users. Therefore, the aim of this study is to investigate university students’ smartphone activities, their use and attitudes towards digital financial services, and to build a neural network model capable of distinguishing students according to their awareness of the benefits related to using online financial services. Methodology: An online questionnaire was applied to collect data on students’ smartphone activity and their tendency to use online financial services. Depending on the variable type, the Kruskal-Wallis H test and Kendall’s tau-b were used to assess the association between variables, while multilayer perceptron and radial basis function neural networks were used for the creation of the optimal model. Results: Participants in this study achieved an average score of 6.56 (SD = 1.27) for smartphone activity, and the results showed that the optimal neural network model obtained had an overall accuracy of 70.73%. However, smartphone activity did not have an excessive effect on the efficiency of this model. Conclusion: The obtained neural network model and its sensitivity analysis managed to reveal some hidden patterns which could be beneficial to educators in terms of improvements of students’ digital and financial literacy as well as to the financial sector in terms of increasing performance and interest of this population in digital financial services