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

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Improving Sensor-Free Affect Detection Using Deep Learning, such as recurrent neural networks (RNNs), have been applied to a range of other domains including pattern recognition and natural language processing with success, but have only recently been attempted in educational contexts. In this work, we construct new “deep” sensor-free affect detectors and
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Automatic Extraction of AST Patterns for Debugging Student Programsy classify over 85% of programs based only on their structural features. For approximately 73% of incorrect submissions, we are able to generate hints that were implemented by the student in some subsequent submission.
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Inducing Stealth Assessors from Game Interaction Dataudent 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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The Federal Government and the Familycal structure on which textual complexity indices were subsequently applied. The stepwise regression model explained 30.5% of the variance in students’ scores, while a Discriminant Function Analysis predicted with substantial accuracy (75.1%) whether they are high or low performance students.
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