E-learning is considered a leading application of digital technologies in educational systems. The aim of the paper is to explore the utilization and impact of digital technologies on an e-learning platform. For this purpose, research was conducted at the Moodle learning management system. Data from the e-learning platform were empirically evaluated in order to find key indicators of student performance in different courses. Student success with the e-learning system was evaluated using a mixed-method: Social Network Analysis, K-Means Clustering, and Multiple Linear Regression. The research was conducted at the University of Novi Sad, Faculty of Technical Sciences, Serbia. The results indicate a significant relationship between the performance of students and the use of digital educational resources from the e-learning platform.

E-learning represents novel learning way, which increase teaching flexibility and availability of learning resources. This paper explores the evaluation of student success at e-learning platform. Authors used multi-method approach for data analysis (i.e. Social Network Analysis, K-means Clustering and Linear Regression). This approach presents novelty in the field of e-learning, which provides more detailed analysis that enable more relevant results. The research was conducted with student group at the University of Novi Sad, Faculty of Technical Sciences, Serbia. Results indicate that digital resources at the e-learning platform make strong effects on student success. Moreover, results indicate that students with the similar grades belongs to the same clusters.

This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts.

The trend to adopt more online technologies continues in the higher education sector. E-Iearning represents application of new technologies and methods in education. This paper explores the employment of digital technologies to facilitate and support virtual spaces for bringing learners together. We used Social Network Analysis (SNA) method to analyze the data collected from the e-learning platform. SNA provides a powerful mathematical approach to study the process of computer supported collaborative learning from a theoretical point of view. In this study, we empirically evaluated data from the e-learning platform to find key indicators for student performance. The research was conducted at the University of Novi Sad, Serbia. Results indicate significant relationships among performance of students and the use of educational resources from the e-learning platform.