羊栏
发表于 2025-3-28 15:48:52
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ingenue
发表于 2025-3-28 22:15:18
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Fraudulent
发表于 2025-3-28 23:48:33
Couples in Times of the Pandemicical patterns and their evolution are suitable tools to directly and reliably assess learners’ attention. Intelligent learning systems can greatly benefit from using these results to enhance and adapt their interventions.
愤愤不平
发表于 2025-3-29 03:52:46
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Tartar
发表于 2025-3-29 07:22:19
https://doi.org/10.1007/978-1-137-51496-7demonstrate some ways in which learner facial expressions are dependent on both dialogue and task context. The findings also hold design implications for affect recognition and tutorial strategy selection within tutorial dialogue systems.
Ordnance
发表于 2025-3-29 13:24:57
Assessment of Learners’ Attention While Overcoming Errors and Obstacles: An Empirical Studyical patterns and their evolution are suitable tools to directly and reliably assess learners’ attention. Intelligent learning systems can greatly benefit from using these results to enhance and adapt their interventions.
Annotate
发表于 2025-3-29 18:49:23
When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?e show that using multiple disengagement types to predict learning improves predictive power. Overall, our results suggest that although adapting to disengagement should improve learning, maximizing learning requires different system interventions depending on disengagement type.
抚慰
发表于 2025-3-29 23:26:43
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净礼
发表于 2025-3-30 02:50:37
William L. Miller,Lucy M. Candibrtual 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.
使腐烂
发表于 2025-3-30 04:36:42
Mmaphuti Maria Mamaleka,Freddy Skobiper post-tests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student’s data from a tutor lesson) in order to reach near-asymptotic predictive power.