By Paula J. Durlach, Alan M. Lesgold
This edited quantity presents an summary of the newest developments in adaptive education expertise. clever tutoring has been deployed for well-defined and comparatively static academic domain names corresponding to algebra and geometry. besides the fact that, this adaptive method of computer-based education has but to come back into wider utilization for domain names which are much less good outlined or the place student-system interactions are much less based, akin to in the course of scenario-based simulation and immersive critical video games. with the intention to tackle how you can extend the succeed in of adaptive education know-how to those domain names, top specialists within the box current their paintings in parts corresponding to scholar modeling, pedagogical method, wisdom evaluate, common language processing, and digital human brokers. numerous methods to designing adaptive expertise are mentioned for either conventional academic settings education domain names. This e-book will attract somebody serious about academic and coaching expertise at a qualified point, together with researchers, education structures builders, and architects.
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Extra resources for Adaptive Technologies for Training and Education
3. Overview of technologies to support learner modeling Source: From “Adaptive technologies,” by V. J. Shute and D. ZapataRivera, 2007, in J. M. Spector, D. Merrill, J. van Merriënboer, & M. ), Handbook of research on educational communications and technology (3rd Edition) (pp. 277–294). New York: Lawrence Erlbaum Associates, Taylor & Francis Group. Copyright © 2007 by the Taylor & Francis Group; adapted by permission of the publisher. quantitative modeling In general, quantitative modeling of learners obtains estimates about the current state of some attribute.
Picard, R. W. (2002). Real-time, fully automatic upper facial feature tracking. Paper presented at the 5th International Conference on Automatic Face and Gesture Recognition, Washington, DC. Kay, J. (1999). A scrutable user modelling shell for user-adapted interaction. D. Thesis, Basser Department of Computer Science, University of Sydney, Sydney, Australia. Kay, J. (2000). Stereotypes, student models and scrutability. In G. , & K. ), Lecture Notes in Computer Science (pp. 19–30). Berlin: Springer Verlag.
What to Adapt Variables Examples Learner Variables Cognitive Math skills, reading skills, abilities cognitive development stage, problem solving, analogical reasoning. Metacognitive Self-explanation, selfskills assessment, reflection, planning. Affective Motivated, attentive, states engaged, frustrated. Additional Personality traits, learner variables styles, social skills, perceptual skills. Instructional Variables Feedback Types: hints, explanations. Timing: immediate, delayed. Content Concepts, learning objects, sequencing tasks, items, cases or problems to solve.
Adaptive Technologies for Training and Education by Paula J. Durlach, Alan M. Lesgold