What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Mathematics education
His primary areas of investigation include Multimedia, Mathematics education, Cognitive tutor, TUTOR and Curriculum.
His Multimedia study incorporates themes from Abstraction, Control and Metadata.
Albert T. Corbett combines subjects such as Descriptive knowledge, Cognitive style and Informal learning with his study of Mathematics education.
His studies in TUTOR integrate themes in fields like Lisp, Machine learning, Cognitive model and Artificial intelligence.
His studies deal with areas such as Bayesian Knowledge Tracing, Computer-Assisted Instruction, Teaching method and Human–computer interaction as well as Cognitive model.
His Curriculum research integrates issues from Software, User modeling, Learned helplessness and Domain knowledge.
His most cited work include:
- Cognitive Tutors: Lessons Learned (1472 citations)
- Knowledge tracing: Modeling the acquisition of procedural knowledge (989 citations)
- Cognitive modeling and intelligent tutoring (456 citations)
What are the main themes of his work throughout his whole career to date?
TUTOR, Multimedia, Mathematics education, Cognitive tutor and Artificial intelligence are his primary areas of study.
Albert T. Corbett has researched TUTOR in several fields, including Mastery learning, Knowledge management, Bayesian Knowledge Tracing, Reading and Intelligent tutoring system.
His research in Multimedia intersects with topics in Software, Metadata and Human–computer interaction.
He interconnects Cognitive model and Set in the investigation of issues within Mathematics education.
His Cognitive tutor study combines topics from a wide range of disciplines, such as Curriculum, Algebra II, Formative assessment, User assistance and Natural language.
Albert T. Corbett has included themes like Domain, Machine learning, Tracing and Natural language processing in his Artificial intelligence study.
He most often published in these fields:
- TUTOR (50.00%)
- Multimedia (31.90%)
- Mathematics education (31.03%)
What were the highlights of his more recent work (between 2010-2018)?
- TUTOR (50.00%)
- Mathematics education (31.03%)
- Bayesian Knowledge Tracing (9.48%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in TUTOR, Mathematics education, Bayesian Knowledge Tracing, Cognitive tutor and Multimedia.
Albert T. Corbett has researched TUTOR in several fields, including Robust learning, Intelligent tutoring system, Artificial intelligence, Knowledge management and Goodness of fit.
His Mathematics education research is multidisciplinary, relying on both Pedagogy and Set.
Albert T. Corbett has included themes like Machine learning and Reading in his Bayesian Knowledge Tracing study.
Albert T. Corbett combines subjects such as Educational psychology, Abductive reasoning, Meaning, Human–computer interaction and Domain knowledge with his study of Cognitive tutor.
His study on Multimedia is mostly dedicated to connecting different topics, such as Multimedia information systems.
Between 2010 and 2018, his most popular works were:
- The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. (341 citations)
- Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key (43 citations)
- Towards predicting future transfer of learning (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Mathematics education
Albert T. Corbett focuses on Bayesian Knowledge Tracing, Educational data mining, TUTOR, Machine learning and Artificial intelligence.
His Bayesian Knowledge Tracing research integrates issues from Teaching method, Cognitive science, Educational research and Knowledge representation and reasoning.
His TUTOR research includes themes of Knowledge state and Robust learning.
His biological study spans a wide range of topics, including Cooperative learning, Synchronous learning, Graph and Mathematics education.
His Artificial intelligence research includes elements of Experiential learning and Active learning.
Predictive power is integrated with Cognitive tutor and Human–computer interaction in his study.
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