Nowadays, the theme of computer thinking is a common topic for educational research. The scientific literature on the subject has gradually appeared, in which psychologists emphasize the need for the development of thinking of children. Research often relates only to the development of computational thinking at elementary and high schools. Nowadays, almost everything is digitalized, so it is important to also develop the computational thinking skills of students at higher levels. In our study, we present the results of the conducted data analysis in which we examined the development of students’ efficiency. On the basis of the results, we propose a possible solution for the development of computational thinking. Using data research processes, we examined the results of the object-oriented (OO) planning and programming subject of Budapest Business School, going back 5 years. The results show that the level of particular computational thinking could be measured using the exam results, and teacher advancement (experience) improved the level of particular computational thinking. Today, education has been greatly influenced by COVID-19, challenging not only teachers but also students. The production of particular computational thinking under COVID-19 or online is much more effective than the pursuit of full computational thinking through traditional teaching.

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 major contributor to climate change is the emissions from Information and Communication Technology (ICT) devices and digitalisation. Energy use, heat production, and the operation of assets all contribute to the production of harmful emissions. However, indirect emissions, such as production and disposal, also play a role. We rely mainly on the output of small and medium-sized enterprises (SMEs). This paper focuses on the emissions of SMEs. Is it certain that cloud services (remote data storage and management) leave a much smaller carbon footprint than ICT devices for own use? These two solutions lead to a paradox: using more modern devices to produce less emissions requires more energy and generates more heat. This article analyses how to resolve this paradox for SMEs.

Integrált rendszerek szükségessége kis- és középvállalkozások esetében • Necessity of Enterprise Resource Planning Systems for Small and Medium-sized Enterprises