IMPROVING THE EFFECTIVENESS OF DATA-DRIVEN LEARNING MANAGEMENT

Volkov Alexander, Otbetkina Tatiana Alekseevna, Vidmanova Anna Nikolaevna

Big data represents a significant decision-making tool in educational processes, allowing for not only improvements in the quality of education, but also the formation of effective strategies, allowing users to effectively make decisions based on the results of predictive and prescriptive analytics. In addition, educational data are used to analyze and predict student behaviors and learning outcomes to ensure the high quality of educational programs. Nevertheless, even though educational data offer significant opportunities, there are a few challenges associated with their effective and timely application, such as compatibility, data processing and storage, security, confidentiality, and ethics. This article reports on a study that aimed to determine whether the effectiveness of learning management could be enhanced through the development of a more rigorous academic environment and a stimulation intervention designed for a particular graduate program at a particular university. The study used data from several study groups. It collected qualitative and quantitative data to examine the impact of the environment and the stimulation of 173 students, as well as benchmark group data from 22 students. The results showed that exposure to a more rigorous academic environment with minimized gamification, as well as the application of a stimulation intervention, was successful in improving scores for several metrics. This article indicates the importance of updating and using educational data to enhance learning management, as well as the need to increase knowledge in this area.

Keywords:learning management; educational data; stimulating intervention