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Titlebook: Advances in Intelligent Tutoring Systems; Roger Nkambou,Jacqueline Bourdeau,Riichiro Mizoguc Book 2010 Springer-Verlag Berlin Heidelberg 2

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21#
發(fā)表于 2025-3-25 05:49:25 | 只看該作者
https://doi.org/10.1007/978-3-642-14363-2Artificial Intelligence in Education; Computational Intelligence; E-learning; Emotion; Intellignet Tutor
22#
發(fā)表于 2025-3-25 08:06:04 | 只看該作者
23#
發(fā)表于 2025-3-25 14:24:11 | 只看該作者
1860-949X ons be with You: The Stimulating World of AIED and ITS ResearchIt is my pleasure to write the foreword for Advances in Intelligent Tutoring S- tems. This collection, with contributions from leading researchers in the field of artificial intelligence in education (AIED), constitutes an overview of th
24#
發(fā)表于 2025-3-25 17:34:43 | 只看該作者
Development as Enhancement of Life Chancesmains, representing students’ domain knowledge and also higher-level skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. We present CBM, its foundation and extensions, various types of instructional domains we applied it to, and conclude with avenues for future research.
25#
發(fā)表于 2025-3-25 21:11:35 | 只看該作者
Development as Enhancement of Life Chancestation based on the following three complementary and important perspectives: the characteristics of ill-defined domains, the approaches to represent and reason with domain knowledge in these domains, and suitable teaching models. Numerous examples are given throughout the chapter to illustrate the discussion.
26#
發(fā)表于 2025-3-26 01:29:33 | 只看該作者
27#
發(fā)表于 2025-3-26 05:32:12 | 只看該作者
28#
發(fā)表于 2025-3-26 08:41:31 | 只看該作者
29#
發(fā)表于 2025-3-26 15:03:47 | 只看該作者
Selin Deliceirmak,Ilkay Salihoglulogical engineering research, this chapter discusses the modeling of tutoring actions as well as target-world modeling per se, using an example of learning/instructional actions from the OMNIBUS/SMARTIES project.
30#
發(fā)表于 2025-3-26 17:14:54 | 只看該作者
Keiichi Mochida,Lam–Son Phan Tranach learner. Moreover, they will include information on the cultural preferences of learners, their personal interests, learning goals, and personal characteristics. Ultimately, student model servers will separate student models from tutors and will be a part of wide area networks, serving more than one application instance at a time.
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