What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- The Internet
- World Wide Web
Katrien Verbert mainly investigates Learning analytics, Recommender system, Data science, World Wide Web and Human–computer interaction.
Her studies deal with areas such as Usability and Knowledge management as well as Learning analytics.
Her Recommender system study combines topics from a wide range of disciplines, such as Intelligent decision support system, Software deployment, Relevance and Personalization.
Her work on Analytics as part of general Data science study is frequently linked to Data sharing, therefore connecting diverse disciplines of science.
Her World Wide Web research is multidisciplinary, incorporating elements of Interactive visualization and Sociotechnical system.
Her Human–computer interaction research includes themes of Object-oriented design, Machine learning, Information visualization and Artificial intelligence.
Her most cited work include:
- Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (356 citations)
- Learning analytics dashboard applications (319 citations)
- Learning analytics dashboard applications (319 citations)
What are the main themes of her work throughout her whole career to date?
Katrien Verbert spends much of her time researching Recommender system, Learning analytics, Data science, Human–computer interaction and World Wide Web.
Her Recommender system research is multidisciplinary, relying on both Personalization, Relevance, Control, User control and User experience design.
The concepts of her Learning analytics study are interwoven with issues in Analytics and Knowledge management.
Her Analytics research integrates issues from Visual analytics and Cultural analytics, Semantic analytics.
Her study in Data science is interdisciplinary in nature, drawing from both Domain and Information visualization.
Her Human–computer interaction study combines topics from a wide range of disciplines, such as Visualization and Dashboard.
She most often published in these fields:
- Recommender system (30.65%)
- Learning analytics (31.18%)
- Data science (24.19%)
What were the highlights of her more recent work (between 2019-2021)?
- Learning analytics (31.18%)
- Recommender system (30.65%)
- Data science (24.19%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Learning analytics, Recommender system, Data science, Visualization and Artificial intelligence.
The study incorporates disciplines such as Learning sciences and Medical education in addition to Learning analytics.
Her work carried out in the field of Recommender system brings together such families of science as Internet privacy and Openness to experience.
Her Data science research includes elements of Variety and Multimodal data.
Her work on Data visualization as part of general Visualization research is frequently linked to Meaning, Design process and Online learning, thereby connecting diverse disciplines of science.
Her work in Artificial intelligence covers topics such as Machine learning which are related to areas like Domain knowledge, Decision support system, Task and Domain.
Between 2019 and 2021, her most popular works were:
- Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation (42 citations)
- LADA: A learning analytics dashboard for academic advising (22 citations)
- Interpretability of machine learning‐based prediction models in healthcare (13 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
Her scientific interests lie mostly in Learning analytics, Data science, Medical education, Academic advising and Visualization.
Her Learning analytics study integrates concerns from other disciplines, such as Performance indicator, Analytics and Learning sciences.
Data science is closely attributed to Implementation in her work.
Her research integrates issues of Domain, Variety, Multimodal data and Personalization in her study of Visualization.
Her Personalization research incorporates themes from Recommender system and Human–computer interaction.
Katrien Verbert interconnects Creative visualization, Conceptual model and User control in the investigation of issues within Human–computer interaction.
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