New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization
作者： Kenneth R Koedinger , Emma Brunskill , Ryan SJd Baker , Elizabeth A McLaughlin , John Stamper
关键词:
摘要: Increasing widespread use of educational technologies is producing vast amounts data. Such data can be used to help advance our understanding student learning and enable more intelligent, interactive, engaging, effective education. In this article, we discuss the status prospects new powerful opportunity for data-driven development optimization technologies, focusing on intelligent tutoring systems We provide examples a variety techniques develop or optimize select, evaluate, suggest, update functions tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, integrations symbolic search statistical inference.
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