The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: “Academic Self-Efficacy and Preparedness”, “Autonomy and Resource Utilization”, “Interest and Engagement”, and “Self-Regulation and Goal Setting.” Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments.

As the digital age permeates higher education, the cybersecurity awareness of university students has emerged as a pressing concern. This study examines the behavioral factors influencing students’ cybersecurity practices, developing a robust, empirically validated survey. Our research applies a comprehensive framework employing both exploratory and confirmatory factor analyses (EFA; CFA) to affirm the survey’s ability to capture the intricate dimensions of students’ cybersecurity awareness. A structural equation model (SEM) has been developed to delineate and scrutinize five key dimensions of cybersecurity behaviors within the student body. Post-validation, we utilized this model to conduct a thorough comparative analysis of cybersecurity behaviors among members of the varied student demographic that participated in the survey. The investigation included an examination of behavior across genders, age groups, academic disciplines, and cultural backgrounds, shedding light on the diverse cybersecurity behaviors that define the modern student experience. Our research ultimately strives to contribute to the enhancement of digital security in educational environments, aligning student online practices with robust security measures and nurturing a cybersecurity-aware culture in academia.

In this paper 16 different Moodle Machine Learning models for predicting the success of 57 full-time students enrolled in the Applied Statistics course at the University of Dunaújváros in Hungary have been developed and tested in terms of “goodness”. The success can be affected by several factors, but here only students' cognitive activities are examined. The predictors used in the models are based on: number of view of PDF lecture notes, number of views of video lectures, number of views of books of solved exercises, number of views of Minitab videos (videos for problem solving with a statistical software), number of attempts of quizzes and best grades achieved by students on quizzes. The models differed in the number and in the types of predictors. Binary Logistic Regression was used for model training and evaluation. The target of the models indicates whether a student is at risk of not achieving the minimum grade to pass the course. The impact of cognitive predictors that are part of the Moodle core Analytics API on predictive power was also examined. Having evaluated the goodness of the different models, it was shown that students' success can be predicted purely from cognitive activities, but their predictive powers are very diverse. The predictors of quizzes have the largest impact on the success, however, supplementing the model with other even less effective predictors much better model can be made. Models built from purely Moodle core cognitive predictors give much less reliable results.