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Titlebook: Artificial Intelligence in Education; 15th International C Gautam Biswas,Susan Bull,Antonija Mitrovic Conference proceedings 2011 Springer-

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發(fā)表于 2025-3-25 07:12:39 | 只看該作者
A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilitiesers. Up to now, the learning capabilities of tutoring agents in educational systems have been generally very limited. In this paper, we address this issue with CELTS, a cognitive tutoring agent, whose architecture is inspired by the latest neuroscientific theories and unite several human learning ca
22#
發(fā)表于 2025-3-25 11:24:40 | 只看該作者
23#
發(fā)表于 2025-3-25 15:38:38 | 只看該作者
24#
發(fā)表于 2025-3-25 17:09:43 | 只看該作者
25#
發(fā)表于 2025-3-25 23:19:01 | 只看該作者
Extending a Teachable Agent with a Social Conversation Module – Effects on Student Experiences and Lin mathematics. The purpose of this extension is to affect constructs known to promote learning, such as self-efficacy and engagement as well as enhance students’ experiences of interacting with the game. A comparison of students that played the game with the off-task interaction to those who played
26#
發(fā)表于 2025-3-26 02:48:34 | 只看該作者
Text Categorization for Assessing Multiple Documents Integration, or John Henry Visits a Data Minemation across multiple sources and contexts. Yet reading is most often taught with single sources. In this paper, we explore techniques for analyzing student essays to give feedback to teachers on how well their students deal with multiple texts. We compare the performance of a simple regular expres
27#
發(fā)表于 2025-3-26 06:45:16 | 只看該作者
Learning by Problem-Posing for Reverse-Thinking Problemsfor learning by problem-posing. In this research, we have paid a special attention to “reverse-thinking problems” in arithmetic word problems that can be solved either by addition or subtraction. In the reverse-thinking problems, since “story operation structure” and “calculation operation structure
28#
發(fā)表于 2025-3-26 10:41:43 | 只看該作者
Affect Detection from Multichannel Physiology during Learning Sessions with AutoTutornd responding to emotions might improve learning outcomes. This study uses machine learning techniques to detect learners’ affective states from multichannel physiological signals (heart activity, respiration, facial muscle activity, and skin conductivity) during tutorial interactions with AutoTutor
29#
發(fā)表于 2025-3-26 14:35:20 | 只看該作者
30#
發(fā)表于 2025-3-26 17:12:47 | 只看該作者
Social Communication between Virtual Characters and Children with Autismrtual character in selecting objects by following the character’s gaze and/or pointing. Both accuracy and reaction time data suggest that children were able to successfully complete the task, and qualitative data further suggests that most children perceived the character as an intentional being with relevant, mutually directed behaviour.
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