Understanding Learning in Digital Environments: Inside IOE’s International Laboratory of Research and Design in elearning
As new questions emerged, the Laboratory’s research agenda continued to evolve. In 2020, the focus shifted towards large-scale experiments with digital learning tools. The pandemic brought new questions about the use of technology in education, including the technological stress experienced by teachers and their evolving roles in an increasingly digital learning landscape. At the same time, the Laboratory began developing methods for analysing the digital traces learners leave when interacting with online learning environments.
Today, the Laboratory studies cognitive and metacognitive processes in digital environments, learning engagement and self-regulation, and collaborative learning, as well as the role of generative AI in education. Its researchers combine experimental methods with digital data to understand how learners interact with educational environments, and how these environments can be designed to support learning.
The HSE News Service spoke to Anastasia Kapuza, Head of the Laboratory, about the Laboratory's research agenda, the role of digital data in understanding learning behaviour, the recent findings of Laboratory researchers and the development of international collaborations.
What are the Laboratory’s main areas of research?
– Our research focuses on how people learn in digital and multimedia environments, and on designing these environments to support learning. One major area is cognitive and metacognitive processes. We study how learners process information in environments where they encounter videos, tasks, explanations, and other materials. We also examine how the design of these environments can support learning or place additional demands on limited cognitive resources.
Another area is learning engagement. We examine what learners do when they interact with an educational environment, and how their behaviour changes over time. We are also increasingly interested in collaborative learning and generative AI. Rather than viewing AI simply as a digital tool, we study it as a potential new participant in the learning process and explore how it can support learning rather than replace it.
Does the Laboratory mainly conduct fundamental or applied research? Is it difficult to maintain a balance between the two?
– Our primary interest lies in understanding how people learn and cope with the ever-growing volume of information they encounter. We work in education and want our research to be useful in educational practice.
This creates a productive link between fundamental and applied research. We conduct experiments and develop research-based recommendations, while also collaborating with educational product and platform providers. The Laboratory has collaborated with organisations, using real educational environments to explore how students interact with learning materials and how these environments can be improved.
What kinds of digital data are useful for understanding students’ engagement in learning?
– Data generated by learners as they interact with online systems can help us to understand learning behaviour as a dynamic process. For instance, we can examine how learners approach assignments, how much time they devote to various activities, whether they seek assistance, communicate with instructors, and make use of supplementary learning materials.
However, individual indicators can be misleading. Our research shows that it is more useful to examine complex behavioural patterns than isolated traces. This was one of the key findings of Aleksandra Getman’s dissertation, which examined digital traces of learners’ behaviour and engagement across different online courses. Her work demonstrated that engagement is dynamic and cyclical, and that more complex models are required to understand and predict it.
How can learner control in an online environment increase motivation–and, on the contrary, lead to disorientation?
– Giving learners more control can support their need for autonomy and boost motivation. But in a digital environment, learners may have to make many decisions about what to study, in what order and how to organise their learning. This can create additional cognitive load: they have to think not only about the content, but also about how to structure the learning process itself.
Research led by Anna Gorbunova shows that learner control is not just about giving students more choice. What matters is how that choice is designed and supported. In one study, students who were allowed to choose their own learning trajectory performed better and reported greater satisfaction than those who received additional training in self-control. The Laboratory studies the conditions under which learner control can foster agency and reflection, rather than leading to overload and disorientation.
Which findings or studies by your colleagues would you highlight?
– One particularly interesting area is cognitive load, where many questions remain open. Anna Gorbunova’s dissertation examined how the sequence of learning tasks can affect cognitive load. One question was whether giving learners a more difficult problem first, asking them to identify the problem before moving to simpler tasks, might discourage them. This approach is central to problem-oriented learning. Her research revealed that, with appropriate instructional support, this initial challenge does not necessarily have a negative effect.
Anna Korchak studied productive interaction between learners and AI and demonstrated that it can be divided into three zones. In the red zone, AI replaces learning rather than supporting it. In the yellow zone, students still learn, but their gains in knowledge and skills are smaller than they could have been without AI. In the green zone, AI is used to support the acquisition of genuinely new knowledge and skills. This distinction helps frame a key question for education: not whether students use AI, but how that interaction contributes to learning.
How are the Laboratory’s research findings used in teaching?
– In our work, research and teaching are closely connected. For instance, when we study cognitive load, we apply principles for reducing unnecessary cognitive demands in our own courses. Similarly, when we study the productive use of generative AI, we design learning activities that encourage students to use these tools to support their learning.
We also incorporate current research into our courses and involve students in research projects. Students can work with real research data, read up-to-date studies, and develop their term papers and theses as part of ongoing projects. For a research university, participating in an actual study is a valuable learning experience.
Does the Laboratory work with early-career researchers?
– One of our key missions is to train highly qualified researchers for HSE University, other Russian research centres, and the international academic market. At the moment, we have a productive structure for a research laboratory: four experienced researchers and five doctoral students. Senior researchers develop broader research themes and set strategic directions, while doctoral students pursue their own ideas within these areas.
More generally, we aim to create a supportive environment in which doctoral students can become immersed in our academic culture, develop their strengths, and grow as independent researchers. We are also always interested in talented doctoral students and postdoctoral researchers who bring their own research ideas within the Laboratory’s areas of expertise. We are happy to help them develop these ideas into strong research projects and support them throughout this process.
How does the Laboratory maintain and develop international research collaboration?
– International collaboration remains an important part of our work. We publish and conduct research with colleagues from countries including China, the UK, Kuwait and Finland. These collaborations allow us to test our ideas against different perspectives and research traditions.
For us, one of the most effective ways to remain visible internationally is to conduct high-quality research. Strong research creates opportunities for new collaborations, co-authorships, and invitations to international conferences. Our goal is to build a research group that is recognised by specialists in our fields and increasingly sought out for collaboration.
