Education in today's technologically advanced environments makes complex cognitive demands on students pre-learning, during, and post-learning. Not surprisingly, these analytical learning processes--metacognitive processes--have become an important focus of study as new learning technologies are assessed for effectiveness in this area.Rich in theoretical models and empirical data, the International Handbook of Metacognition and Learning Technologies synthesizes current research on this critical topic. This interdisciplinary reference delves deeply into component processes of self-regulated…mehr
Education in today's technologically advanced environments makes complex cognitive demands on students pre-learning, during, and post-learning. Not surprisingly, these analytical learning processes--metacognitive processes--have become an important focus of study as new learning technologies are assessed for effectiveness in this area.Rich in theoretical models and empirical data, the International Handbook of Metacognition and Learning Technologies synthesizes current research on this critical topic. This interdisciplinary reference delves deeply into component processes of self-regulated learning (SRL), examining theories and models of metacognition, empirical issues in the study of SRL, and the expanding role of educational technologies in helping students learn. Innovations in multimedia, hypermedia, microworlds, and other platforms are detailed across the domains, so that readers in diverse fields can evaluate the theories, data collection methods, and conclusions. And for the frontline instructor, contributors offer proven strategies for using technologies to benefit students at all levels. For each technology covered, the Handbook: Explains how the technology fosters students' metacognitive or self-regulated learning.Identifies features designed to study or support metacognitve/SRL behaviors.Reviews how its specific theory or model addresses learners' metacognitive/SRL processes.Provides detailed findings on its effectiveness toward learning.Discusses its implications for the design of metacognitive tools.Examines any theoretical, instructional, or other challenges.These leading-edge perspectives make the International Handbook of Metacognition and Learning Technologies a resource of great interest to professionals and researchers in science and math education, classroom teachers, human resource researchers, and industrial and other instructors.
Roger Azevedo is an Associate Professor in the Department of Psychology and an affiliated member of the Institute for Intelligent Systems (IIS) at The University of Memphis. He is currently the Director of the Cognitive Psychology Area in the Department of Psychology and the Director of the Cognition and Technology Research Laboratory (azevedolab.autotutor.org). In 1998 he received his Ph.D. in Educational Psychology from McGill University. He did his postdoctoral training in the Department of Psychology at Carnegie Mellon University. Dr. Azevedo then earned a faculty appointment in the Department of Human Development at the University of Maryland. His primary research interests are in cognitive science, human learning and performance, and the learning sciences. More specific interests include metacognition and self-regulated learning, complex learning, human and computerized tutoring, intelligent computer-based learning environments, and education. He is an associate editor of the Journal of Educational Psychology and serves on the editorial board of six top-tiered international journals including Metacognition and Learning, Educational Psychologist, Educational Psychology Review, and Instructional Science. He reviews papers and performs committee functions for the American Educational Research Association, International Society of Artificial Intelligence in Education, International Society of the Learning Sciences, American Psychological Association, and European Association for Research on Learning and Instruction. He has received several research awards, including an NSF Early Career Grant and Educational Technology Research & Development¿s 2008 outstanding journal article. He is also the recipient of several NSF and NIH grants that focus on metacognition, SRL, and complex science topics with learning technologies. Dr. Azevedo is an advisory board member for the Pittsburgh Science of Learning Center (PSLC) and a regular panel member for the Institute of Education Sciences and National Science Foundation. In addition to publishing over 100 articles in journals, books, and conference proceedings, he has (co-)edited five special issues of key journals in the learning and cognitive sciences. He has played a major role in bringing in to the University of Maryland and the University of Memphis over $7 million in grant funding during the last ten years as either PI or co-PI. He has designed, developed, and tested advanced computerized, intelligent learning environments for medical and biological sciences including the RadTutor, SICUN tutor, CircSysWeb, and MetaTutor. Vincent Aleven is (as of July 1, 2009) an Associate Professor in Carnegie Mellon¿s Human-Computer Interaction Institute, and has over 17 years of experience in research and development of educational software based on cognitive science theory, with a focus on intelligent tutors for middle-school and high-school mathematics. His research has been published in journals such as Cognitive Science, Review of Educational Research, Educational Psychology Review, the International Journal on Artificial Intelligence and Education, and Artificial Intelligence. In a number of projects, he has demonstrated that intelligent tutor functionality can effectively support metacognition. He demonstrated empirically that students learn better when the tutor supports metacognition in the form of self-explanation; improvements developed in this research project were incorporated into Carnegie Learning¿s Cognitive Tutor Geometry¿, which is used daily in hundreds of U.S. schools. In research focused on a different metacognitive skill, help seeking, Aleven and colleagues showed that a tutor agent based on a detailed computer model of help seeking can lead to lasting improvement in students¿ help-seeking behavior with tutoring software. In other research, he and colleagues have created the CTAT authoring tools that enable non-programmers to create tutors much more cost-effectively than programmers used to create them. He is currently funded by Institute of Education Sciences (IES) to use the CTAT tools to develop a freely available website for middle-school mathematics learning, called MathTutor (webmathtutor.org). Further, Aleven and his colleagues have shown empirically that student learning with a cognitive tutor is improved when the tutor is enhanced with interactive examples. A paper describing this research won the Cognition and Student Learning prize sponsored by IES, given to "the best full paper submission to the 2008 Annual Conference of the Cognitive Science Society on a topic directly related to cognitive science, educational practice and subject-matter learning." He twice won a best paper award at the International Conference on Intelligent Tutoring Systems. Aleven is a member of the Executive Committee of the Pittsburgh Science of Learning Center, an NSF-sponsored research center spanning multiple departments both at Carnegie Mellon and the University of Pittsburgh. He is also a member of the steering committee of CMU¿s PIER pre-doctoral scholarship program for educational research, funded by IES. He is a co-founder of Carnegie Learning, Inc., a Pittsburgh-based company that markets Cognitive Tutor¿ math courses. He has served on the program committee of major conferences in intelligent tutoring systems, and has organized numerous workshops during these conferences. He will be the Program Committee Co-Chair of the 2010 International Conference on Intelligent Tutoring Systems. He has been PI on four major research grants and co-PI on six others.
Inhaltsangabe
Planning, sub-goaling, and metareasoning
Metacognitive monitoring and control
Strategy instruction to support metacognition and learning
Control of behavior (e.g., help-seeking behavior)
Development of metacognition (knowledge and strategy)
Interface between affective and/or motivation processes with metacognition
Scaffolding of metacognition
External regulating agents (human and artificial) and metacognition
Methodological issues in using computer environments as data collection tools to study metacognition.
