Artificial Intelligence in Education: Building Technology Rich Learning Contexts that WorkR. Luckin, K.R. Koedinger, J. Greer IOS Press, 29 Jun 2007 - 764 halaman The nature of technology has changed since Artificial Intelligence in Education (AIED) was conceptualised as a research community and Interactive Learning Environments were initially developed. Technology is smaller, more mobile, networked, pervasive and often ubiquitous as well as being provided by the standard desktop PC. This creates the potential for technology supported learning wherever and whenever learners need and want it. However, in order to take advantage of this potential for greater flexibility we need to understand and model learners and the contexts with which they interact in a manner that enables us to design, deploy and evaluate technology to most effectively support learning across multiple locations, subjects and times. The AIED community has much to contribute to this endeavour. This publication contains papers, posters and tutorials from the 2007 Artificial Intelligence in Education conference in Los Angeles, CA, USA. |
Isi
HumanComputer Interface Issues | 7 |
Pedagogical Agents | 41 |
Representation | 75 |
Motivation | 109 |
Emotion and Affect | 143 |
Metacognition | 177 |
ITS Feedback and Scaffolding | 211 |
Learner Modelling | 279 |
CourseBased Experimentation | 407 |
Authoring Tools and Ontologies | 441 |
Data Mining | 477 |
Posters | 519 |
Young Researchers Track Abstracts | 677 |
Workshop Summaries | 711 |
Tutorial Abstracts | 723 |
Interactive Events Summaries | 731 |
Linguistics and Language Technology | 305 |
Linguistics and Language Technology Tutorial Dialogues | 339 |
Collaboration | 373 |
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2007 The authors algorithm analysis answer approach Artificial Intelligence assessment authors and IOS AutoTutor behavior classroom cloze cognitive model Cognitive Tutor collaborative learning concept concept map condition constraints context correct data mining developed diagram domain e-learning effect emotions engagement error evaluation example experiment feedback Figure goal hints human tutor hypermedia instruction Intelligence in Education Intelligent Tutoring Systems interaction interface International Conference IOS Press knowledge Koedinger language learner model learning environment learning gains machine learning math mathematics mental model metacognitive nodes ontology participants pedagogical agents peer tutoring performance post-test predict pretest problem solving questions rights reserved rules Science scores self-efficacy self-regulated learning semantic Semantic Web sentence session significant skills step strategies structure student learning student model Table task teachers tool topic tutorial dialogue types variables