Abstract
Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data‐driven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.
Highlights
We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference
Cognitive Tutor mathematics courses are in regular use, about two-days a week, by 600,000 students a year in 2600 middle or high schools, and full-year evaluation studies of Cognitive Tutor algebra have demonstrated better student learning compared to traditional algebra courses (Ritter et al 2007)
We present different data-driven symbolic and statistical machine-learning approaches for automated or semiautomated development of the key components and functionalities of intelligent tutoring systems as illustrated in figures 1–3
Summary
We discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems and illustrating techniques especially in the context of cognitive tutors. To perform the evaluate and suggest functions, the tutor uses a cognitive model that represents possible solutions to the activity, infers how a student’s input may relate to common misunderstandings, and predicts what feedback or hints will best help the student complete the activity. Most intelligent tutoring systems have been built through extensive knowledge engineering and, ideally, cognitive task analysis to develop models of student and expert skill and performance These models are used to generate hints and feedback (inner loop of figure 1). We present different data-driven symbolic and statistical machine-learning approaches for automated or semiautomated development of the key components and functionalities of intelligent tutoring systems as illustrated in figures 1–3
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