Международная лаборатория проектирования и исследований в онлайн-обучении

Публикации

• Статья

  Gorbunova A., Adamovich K., Savelyev A. et al.

  Behavior patterns characterize students’ choices and relate to cognitive load and performance in learner-controlled environments

  Internet and Higher Education. 2026. Vol. 69.

• Статья

  Boitcov M., Kapuza A., Al-Safi H. et al.

  Left unread: how message orientation shapes engagement in adaptive nudging systems

  Interactive Learning Environments. 2026. P. 1-14.

• Статья

  Kapuza A., Getman A., Kotlikova A. et al.

  Predicting students engagement in asynchronous online learning: a mixed-method approach

  Predicting the level of student learning engagement in online learning is crucial for student success, especially for asynchronous courses. While digital traces can track students’ activity on the platform and help to measure the engagement level, they could provide contradictory results, so it is crucial to incorporate complementary methods which can triangulate the findings obtained from digital traces. This study aimed to develop and validate a model to determine the level of learning engagement in adult learners on an asynchronous online platform using a mixed-method approach. Data from digital traces, surveys, and interviews were combined. The study involved 2234 students and employed Extreme Gradient Boosting and Logistic Regression with L2 regularisation models to predict the level of engagement. The Extreme Gradient Boosting model more accurately predicted students in the low engagement group, providing crucial support for potentially vulnerable students. The number of finished homework assignments and attempts were found to increase the probability of high engagement. The diversity of activities, such as access to text materials, played a pivotal role in sustaining engagement. Interviews corroborated these results, suggesting the model effectively reflects engagement levels. The article discusses implications for constructing similar models in future research.

  Educational Technology Research and Development. 2026. Vol. 74. P. 769-791.

• Статья

  Shulgina G., Adamovich K., Zhang H. et al.

  Comment Type Matters: Analysing the Implementation of Summary, Problem/Solution, and Praise Comments in Peer Feedback

  Active Learning in Higher Education. 2025.

• Статья

  K. Adamovich, A. Getman, A. Kapuza et al.

  Does slow and steady win the race? The relationship between adult EFL learners’ time-related task strategies and achievement

  Language Learning Journal. 2025. P. 1-12.