A mentor for every student: One challenge for instructional software

Computational instruction provides engaging material for learners, personalizes instruction, assesses individual effort, and measures students' needs for assistance. Such instruction makes learning available and accessible, and therefore actionable. This paper describes two promising approaches for using digital instruction to personalize teaching. First, we describe computational instruction that adapts the curricula and computer responses to reflect an individual student's learning needs and emotions. Second, we report on data science for education that is used to reason about the dynamics of the knowledge process for an individual student and, on a larger scale, has the potential to improve research, evaluation, and accountability. This paper also describes MathSpring as an example online tutor that has made contributions to both research areas, based on several years of experimentation and real-world experience.

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