What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Software
- The Internet
His primary scientific interests are in Human–computer interaction, Collaborative learning, Knowledge management, Educational technology and Multimedia.
His Human–computer interaction research is multidisciplinary, incorporating perspectives in Cooperative learning and Artificial intelligence.
In general Collaborative learning, his work in Computer-supported collaborative learning is often linked to Team learning linking many areas of study.
His Knowledge management study combines topics from a wide range of disciplines, such as Social relation, Educational data mining, Data science and Educational research.
His biological study spans a wide range of topics, including Context, Conceptual framework, Learning environment, Virtual learning environment and Instructional design.
The various areas that Pierre Dillenbourg examines in his Multimedia study include Analytics and Wearable computer.
His most cited work include:
- What do you mean by collaborative learning (1492 citations)
- Collaborative learning : cognitive and computational approaches (1180 citations)
- The evolution of research on collaborative learning (1125 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Human–computer interaction, Multimedia, Collaborative learning, Robot and Artificial intelligence.
In his research, Learning analytics is intimately related to Eye tracking, which falls under the overarching field of Human–computer interaction.
Pierre Dillenbourg combines subjects such as Learning environment, Computer-supported collaborative learning and Mobile device with his study of Multimedia.
His Collaborative learning research incorporates themes from Cooperative learning, Educational technology and Scripting language.
His studies in Robot integrate themes in fields like Perception and Handwriting.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
He most often published in these fields:
- Human–computer interaction (37.75%)
- Multimedia (23.50%)
- Collaborative learning (18.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Human–computer interaction (37.75%)
- Robot (16.00%)
- Artificial intelligence (15.75%)
In recent papers he was focusing on the following fields of study:
His main research concerns Human–computer interaction, Robot, Artificial intelligence, Learning analytics and Handwriting.
His Human–computer interaction research is multidisciplinary, incorporating elements of Context, Multimedia, Eye tracking, Collaborative learning and Object.
His study in Tangible user interface extends to Collaborative learning with its themes.
His research integrates issues of Perception and Haptic technology in his study of Robot.
Pierre Dillenbourg combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence.
His Data science research incorporates elements of Educational technology and Knowledge management.
Between 2015 and 2021, his most popular works were:
- Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research (144 citations)
- Teaching analytics: towards automatic extraction of orchestration graphs using wearable sensors (51 citations)
- Learning by Teaching a Robot: The Case of Handwriting (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Software
- The Internet
Pierre Dillenbourg spends much of his time researching Human–computer interaction, Robot, Multimedia, Artificial intelligence and Learning analytics.
His Human–computer interaction study combines topics from a wide range of disciplines, such as Collaborative learning, Educational technology, Eye tracking and Visualization.
His Collaborative learning research is multidisciplinary, relying on both Developmental psychology, Attribution and Tangible user interface.
He interconnects Peer assessment and Generalizability theory in the investigation of issues within Multimedia.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Machine learning, with regards to Empirical research.
His work in Data science addresses issues such as Knowledge management, which are connected to fields such as Educational research.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
