Vaclav Bayer

ORCID: 0000-0001-8953-6335
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About
Contact & Profiles
Research Areas
  • Online Learning and Analytics
  • E-Learning and Knowledge Management
  • Online and Blended Learning
  • Intelligent Tutoring Systems and Adaptive Learning
  • Recommender Systems and Techniques
  • Explainable Artificial Intelligence (XAI)
  • Advanced Graph Neural Networks
  • Scientific Computing and Data Management
  • Ethics and Social Impacts of AI
  • Semantic Web and Ontologies
  • Educational Innovations and Technology
  • Higher Education Learning Practices
  • Innovative Teaching and Learning Methods
  • Expert finding and Q&A systems

The Open University
2021-2025

Learning analytics dashboards (LADs) can provide learners with insights about their study progress through visualisations of the learner and learning data. Despite potential usefulness to support learning, very few studies on LADs have considered learners' needs engaged in process design evaluation. Aligning that, there is a limited understanding what specific student cohorts, particular distance online learners, may seek from effectively studies. In this study, we present findings 21...

10.1186/s41239-021-00284-9 article EN cc-by International Journal of Educational Technology in Higher Education 2021-09-01

Abstract Educational outcomes from traditionally underrepresented groups are generally worse than for their more advantaged peers. This problem is typically known as the awarding gap (we use term over ‘attainment gap’ attainment places responsibility on students to attain at equal levels) and continues pose a challenge educational systems across world. While Learning Analytics (LA) dashboards help identify patterns contributing gap, they often lack stakeholder involvement, offering very...

10.1111/bjet.13509 article EN cc-by British Journal of Educational Technology 2024-07-12

In this paper, we argue why and how the integration of recommender systems for research can enhance functionality user experience in repositories. We present latest technical innovations CORE Recommender, which provides article recommendations across global network repositories journals. The Recommender has been recently redeveloped released into production system also deployed several third-party explain design choices unique evaluation processes have place to continue raising quality...

10.48550/arxiv.1705.00578 preprint EN cc-by arXiv (Cornell University) 2017-01-01
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