What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematics education
- Statistics
The scientist’s investigation covers issues in Mathematics education, Cognitive tutor, TUTOR, Cognition and Multimedia.
His research investigates the connection between Mathematics education and topics such as Pedagogy that intersect with issues in Verbal reasoning, Algebraic reasoning and Variety.
He has researched Cognitive tutor in several fields, including Test, Curriculum, Metacognition, Software and Intelligent tutoring system.
His TUTOR research incorporates elements of Glossary, Coding, Dialog box and Cognitive model.
His research integrates issues of Descriptive knowledge, Expert system, Artificial intelligence and Learning theory in his study of Cognition.
His Multimedia study combines topics in areas such as User assistance, Human–computer interaction and Set.
His most cited work include:
- Cognitive Tutors: Lessons Learned (1472 citations)
- Intelligent Tutoring Goes To School in the Big City (962 citations)
- An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor (672 citations)
What are the main themes of his work throughout his whole career to date?
Mathematics education, Artificial intelligence, TUTOR, Multimedia and Cognitive tutor are his primary areas of study.
His Mathematics education study integrates concerns from other disciplines, such as Test, Pedagogy and Cognition.
His Artificial intelligence study also includes fields such as
- Machine learning which connect with Bayesian Knowledge Tracing,
- Cognitive model that intertwine with fields like Cognitive skill.
His TUTOR research includes elements of Learning by teaching, Dialog box, Intelligent tutoring system and Set.
His work carried out in the field of Multimedia brings together such families of science as Context, Human–computer interaction, Peer tutor, Software and Collaborative learning.
His Cognitive tutor research includes themes of Cognitive psychology, Curriculum, Control, Metacognition and User assistance.
He most often published in these fields:
- Mathematics education (31.91%)
- Artificial intelligence (29.08%)
- TUTOR (26.48%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (29.08%)
- Mathematics education (31.91%)
- Educational technology (9.69%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Mathematics education, Educational technology, Multimedia and Machine learning.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Multi-task learning, Contrast, Instructional design and Cognitive model.
His studies examine the connections between Mathematics education and genetics, as well as such issues in Knowledge management, with regards to Self-regulated learning.
The Educational technology study combines topics in areas such as TUTOR, Cognitive tutor, Human–computer interaction, Teaching method and Educational data mining.
His research investigates the connection between Cognitive tutor and topics such as Task analysis that intersect with problems in Cognitive psychology.
His work deals with themes such as Bayesian Knowledge Tracing, Cognition and Online tutoring, which intersect with Machine learning.
Between 2014 and 2021, his most popular works were:
- Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC (124 citations)
- Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor (117 citations)
- Help Helps, But Only So Much: Research on Help Seeking with Intelligent Tutoring Systems (92 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Programming language
Kenneth R. Koedinger spends much of his time researching Multimedia, Mathematics education, Educational technology, Artificial intelligence and Context.
Kenneth R. Koedinger interconnects Collaborative learning, Adaptive learning, Human–computer interaction and Embodied cognition in the investigation of issues within Multimedia.
His Mathematics education study frequently draws parallels with other fields, such as Curriculum.
His biological study spans a wide range of topics, including TUTOR, Cognitive tutor, Metacognition, Teaching method and Cognitive model.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Instructional design.
His Context research incorporates elements of Cognition and Dropout.
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