OPINION: Personalization, Possibilities and Challenges with Learning Analytics | EdSurg... - 34 views
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shared by Sharin Tebo on 02 Nov 14
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Many of these challenges result from trying to personalize within the context of traditional school structures that standardize the curriculum, the assessments, the grouping, and the instructional time.
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a genuine problem: how to achieve the tremendous academic gains that are possible through personalized instructional methods within the constraints of a traditional classroom.
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Knowledge mapsFormalizing a learning map--sequences of connected concepts and skills that define how one masters a domain, such as beginning Algebra--and mapping student mastery on the map, enables intelligent learning systems to recommend the next concept or skill to be learned, propose aligned instructional content, and present appropriate questions and tasks to assess mastery.
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Learning analytics combines data from student models with data on learning behaviors, knowledge maps, and learning outcomes, and mines these data sets to identify patterns that associate student attributes and behaviors with successful outcomes.
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Learning analytics marks a significant departure from traditional data-driven instructional strategies. That’s because so much more data is available to mine, make sense of, and use.
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It is not enough to design cutting edge analytics to shape educational decision making if we do not understand how teachers can apply them to optimize student learning outcomes.