E-learning as a new educational paradigm

4. Learning analytics

In the last two decades, the digital transformation of education has brought about a series of profound changes in the way the teaching process is organised, implemented and evaluated. The development of e-learning, which was initially developed as a complement to distance education and is now increasingly used as an integrated part of higher education, has opened space for the systematic collection and analysis of large amounts of data on student behavior and achievement. It is precisely this data availability that has created the foundations for the development of learning analytics, a new interdisciplinary approach that views education not only through a pedagogical and didactic prism, but also through the possibilities offered by computer science and the processing of large data sets.

Learning analytics is defined as the process of collecting, measuring, analysing and interpreting data about students and their educational contexts with the aim of understanding and optimising the learning process and making informed pedagogical decisions. Although it shares its roots with the evaluation of the educational process, learning analytics focuses on the processing of real, mostly digitally collected traces of user behavior, such as activities in the e-learning system, time spent with certain contents, quiz results, interactions on forums or ways of navigating through teaching material.

In the context of higher education, the application of learning analytics can span multiple levels. At the student level, it enables the provision of personalised feedback and guidance to students based on their learning needs, habits and achievements. At the teacher level, it provides valuable insights into the effectiveness of teaching materials and methods, uncovers patterns in learning that may indicate a risk of dropout or low achievement, and helps design more effective curricula. At the institutional level, analytics can serve strategic management, programme evaluation and data-driven educational policymaking.

Theoretically speaking, learning analytics relies on several disciplines: pedagogy serves to understand pedagogical concepts and goals, computing and information sciences develop tools for data processing and visualisation, while psychology contributes to the interpretation of behaviour and motivation. Such interdisciplinarity requires careful interpretation of the results, because quantitative data cannot fully cover all dimensions of learning.

It is important to emphasize that the effective application of learning analytics is not limited to the technical implementation of tools, but requires pedagogically thoughtful design of metrics and indicators, clear ethical regulation of data collection and use, and active involvement of teachers in the interpretation of results. Also, the integration of analytics into the higher education context presupposes digital literacy of teaching staff, institutional support and continuous education of all stakeholders in the education system.