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.

In today's modem world, the pace of technological development can be considered exponential. Education must constantly adapt to this dynamic development. It must be able to innovate, to use modern tools and methods that are effectively integrated into the learning process. Education should provide an appropriate learning environment to meet changing needs, one way of which is e-learning. This environment can be an excellent support for the learning process; however, it will hardly be effective without developing the right student learning attitude. The e-learning environment gives freedom and independence to the individual, at the same time. For the learner's individual endeavor to be successfully completed as expected, control and continuous feedback are needed. One way to do this is through self-quizzing. Self-quizzes, divided into units, related to the learning material, with appropriate difficulty and amount, can help to understand and engrave the processed material, and thus improve the effectiveness of learning. Self-quizzes create the opportunity for immediate feedback, which is a very important feature of an e-learning environment. According to many scientific research, immediate feedback can greatly help maintain interest and motivation, and quizzes are suitable tools for this purpose. In our research, we sought to answer the question of how the continuous, self-monitoring practice opportunity provided by online quizzes affects student achievement. In the case of an online course held at the University of Dunainvaros in 2019, we examined whether students who continuously perform self-quizzes will be more effective by the end of the learning process than their peers who are less receptive to independent self-quizzing. We also addressed the effect of the nature of time spent on self-reflexive quizzing on learning success.

Higher education institutions are facing a major issue with student dropout rates, which is a global phenomenon that affects a significant portion of enrolled students, particularly those in their first year. The challenge is how to retain students who do not meet requirements during their first year and are at high risk of dropping out, which can have significant economic and social consequences as well as personal ramifications for the students themselves. Universities must prioritize identifying at-risk students and providing targeted assistance to prevent them from leaving the system. Machine learning (ML) models have proven effective in identifying students at risk of dropping out with a high degree of accuracy. In this study, we aim to construct a machine learning model using data extracted from the administration system (Neptun) to predict student dropout rates in the Business Informatics BSc course at the Faculty of Finance and Accounting of Budapest Business School.

Starting with version 3.4 of Moodle, it has been possible to build educational ML models using predefined indicators in the Analytics API. These models can be used primarily to identify students at risk of failure. Our research shows that the goodness and predictability of models built using predefined core indicators in the API lags far behind the generally acceptable level. Moodle is an open-source system, which on the one hand allows the analysis of algorithms, and on the oth-er hand its modification and further development. Utilizing the openness of the system, we examined the calculation algorithm of the core indicators, and then, based on the experience, we built new models with our own indicators. Our re-sults show that the goodness of models built on a given course can be significant-ly improved. In the article, we discuss the development process in detail and pre-sent the results achieved.

A 2000-es években indult Big Data forradalom óta folyamatosan sokszorozódik a digitális tartalom. A jelenség kutatók tízezreit sarkallja szerte a világon a nagy mennyiségű adatból kinyerhető új és hasznos információk megszerzésére. A tömérdek digitalizált dokumentum szövegében rejlő rejtett összefüggések feltárására matematikai, statisztikai, valószínűségszámítási és számítástudományi algoritmusok léteznek, vagy lehet akár egyedi algoritmust is fejleszteni. Adatbányászati módszerekkel viszonylag egyszerű az olyan dokumentumok szövegkorpuszának elemzése, amely azonos vázzal, közel szabványos módon, formanyomtatvány jelleggel rendelkezik. Ilyen iratok például az oktatásban is használt jegyzőkönyvek vagy a bíróságok által hozott határozatok. Az ezekhez hasonló, félig strukturált szövegek automatikus feldolgozására kezdtünk el egy adatbányászati alapokon nyugvó saját algoritmust fejleszteni, amelyet később önálló döntéshozásra szeretnénk „rábírni”. Módszerünket a bírósági határozatokra, mint szövegkorpuszokra munkáltuk ki, azok közül is azokon szeretnénk a tesztelést elvégezni, amelyek könyvszakértőt említenek.