2021 - Fellow of the American Educational Research Association
His primary areas of study are Teaching method, Science education, Context, Mathematics education and Computer-Assisted Instruction. His Teaching method research is multidisciplinary, incorporating elements of Knowledge management, Task, Goal orientation and Forcing. His biological study spans a wide range of topics, including Academic standards and Curriculum development, Curriculum.
His work carried out in the field of Mathematics education brings together such families of science as Working memory, Phenomenon, Sensemaking and Goal structure. His study focuses on the intersection of Computer-Assisted Instruction and fields such as Software with connections in the field of Human–computer interaction, Scaffold, Field and Face. His Human–computer interaction research includes elements of Bayesian Knowledge Tracing, Set and Cognitive tutor.
The scientist’s investigation covers issues in Mathematics education, Pedagogy, Science education, Human–computer interaction and Curriculum. His Mathematics education study incorporates themes from Context and Set. His Science education study integrates concerns from other disciplines, such as Teaching method and Teacher education.
Brian J. Reiser has researched Teaching method in several fields, including Computer-Assisted Instruction and Face. His Human–computer interaction research is multidisciplinary, relying on both Scaffold, TUTOR, Lisp, Artificial intelligence and Software. His work investigates the relationship between Lisp and topics such as Intelligent tutoring system that intersect with problems in Computer programming.
His main research concerns Science education, Pedagogy, Mathematics education, Engineering ethics and Argumentation theory. His Science education research incorporates elements of Teacher education, Chemistry and Faculty development. His work on Curriculum and Next Generation Science Standards as part of general Pedagogy study is frequently connected to Discipline and Traditional knowledge, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies in Curriculum integrate themes in fields like Structural equation modeling and Theme. His Mathematics education research incorporates themes from Class, Phenomenon, Sensemaking and Communication. In his study, which falls under the umbrella issue of Argumentation theory, Information Dissemination, Academic standards and Persuasion is strongly linked to Science instruction.
Brian J. Reiser mostly deals with Mathematics education, Science education, Pedagogy, Curriculum and Argumentation theory. In general Mathematics education study, his work on Educational psychology and Learning sciences often relates to the realm of Discipline and Coherence, thereby connecting several areas of interest. He frequently studies issues relating to Professional development and Science education.
His study in the field of Student engagement also crosses realms of Unit. Brian J. Reiser interconnects Educational technology, Professional learning community, Teacher education and Faculty development in the investigation of issues within Curriculum. Brian J. Reiser combines subjects such as Science instruction, Persuasion, Class, Phenomenon and Scientific misconceptions with his study of Argumentation theory.
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.
Intelligent tutoring systems
John R. Anderson;C. Franklin Boyle;Brian J. Reiser.
Policy Implementation and Cognition: Reframing and Refocusing Implementation Research
James P Spillane;Brian J Reiser;Todd Reimer.
Review of Educational Research (2002)
Visual images preserve metric spatial information: Evidence from studies of image scanning.
Stephen M. Kosslyn;Thomas M. Ball;Brian J. Reiser.
Journal of Experimental Psychology: Human Perception and Performance (1978)
A Scaffolding Design Framework for Software to Support Science Inquiry
Chris Quintana;Brian J. Reiser;Elizabeth A. Davis;Joseph Krajcik.
The Journal of the Learning Sciences (2004)
Scaffolding Complex Learning: The Mechanisms of Structuring and Problematizing Student Work
Brian J. Reiser.
The Journal of the Learning Sciences (2004)
Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners
Christina V. Schwarz;Brian J. Reiser;Elizabeth A. Davis;Lisa Kenyon.
Journal of Research in Science Teaching (2009)
Explanation-Driven Inquiry: Integrating Conceptual and Epistemic Scaffolds for Scientific Inquiry
William A. Sandoval;Brian J. Reiser.
Science Education (2004)
Making sense of argumentation and explanation
Leema Kuhn Berland;Brian J. Reiser.
Science Education (2009)
Taking science to school: Learning and teaching science in grades K-8. Committee on Science Learning, Kindergarten through 8th grade: National Research Council, Board on Science Education, Division of Behavioral and Social Sciences and Education
Brian Reiser;Richard A. Duschl;Heidi A. Schweingruber;Andrew W. Shouse.
Learning‐goals‐driven design model: Developing curriculum materials that align with national standards and incorporate project‐based pedagogy
Joseph Krajcik;Katherine L. McNeill;Brian J. Reiser.
Science Education (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: