Titlebook: Artificial Intelligence in Education; 18th International C Elisabeth André,Ryan Baker,Benedict du Boulay Conference proceedings 2017 Spring

挑剔小责

Inducing Stealth Assessors from Game Interaction Datalearning behaviors captured in these data provide a wealth of information on student learning, which stealth assessment can utilize to unobtrusively draw inferences about student knowledge to provide tailored problem-solving support. In this paper, we present a long short-term memory network (LSTM)-

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Delinquency Prevention and Labeling present evidence from our pilot tests that illustrate the tutor’s versatility and provide preliminary evidence of its effectiveness. The contributions of the work are: identifying an adaptive coaching strategy for Invention tasks that balances structuring and problematizing, and an automated coach

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https://doi.org/10.1007/978-1-4615-7029-5T are compatible with that of a human domain expert. Teachers were able to use the system successfully to make intervention decisions in groups when deemed necessary, without overwhelming them with information. Our results demonstrate how AI can be used to augment existing educational environments t

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https://doi.org/10.1057/978-1-137-58073-3 They last worked on post-test of the previous plus four new problems (called ‘transfer task’). As a result, in both schools, the numbers of MIF-answers (the erroneous answers supposed due to MIF misconception) in learning task decreased significantly between pre-test and post-test. Effect sizes of

Mitigate

https://doi.org/10.1007/978-3-642-72788-7ased knowledge model and a Naive Bayes model in making predictions. We measured the success of our predictions using 10-fold cross validation, root mean square deviation, and mean absolute error. Over different sample sizes and different numbers of tags, we achieved prediction accuracy ranging betwe

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https://doi.org/10.1007/978-3-642-72788-7y positive reactions to the narrative. Results also revealed that task performance was strongly related to individual difference scores on reading comprehension assessments, and more so with higher-level comprehension skills than basic word-level skills, providing concurrent validity for the interac

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https://doi.org/10.1057/9780230590083udent game-based learning interaction data outperform comparable models that required labor-intensive hand-engineering of input features. The findings suggest that the LSTM-based approach holds significant promise for evidence modeling in stealth assessment.

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