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The Next Step for Learning Analytics

Jinan Fiaidhi

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Abstract

Use of learning analytics in real-world educational applications is growing as academic institutions realize its potential, especially in dealing with the exponential growth of unstructured data. Adding text analytics to learning analytics applications is particularly appealing, because most of the unstructured data dealt with in academia involves text. This article discusses the importance of using textual analytics within the paradigm of learning analytics.

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Use of learning analytics in real-world educational applications is growing as academic institutions realize its potential, especially in dealing with the exponential growth of unstructured data. Adding text analytics to learning analytics applications is particularly appealing, because most of the unstructured data dealt with in academia involves text. This article discusses the importance of using textual analytics within the paradigm of learning analytics.

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Available abstract

Use of learning analytics in real-world educational applications is growing as academic institutions realize its potential, especially in dealing with the exponential growth of unstructured data. Adding text analytics to learning analytics applications is particularly appealing, because most of the unstructured data dealt with in academia involves text. This article discusses the importance of using textual analytics within the paradigm of learning analytics.

Key concepts: Learning analytics, Analytics, Computer science, Data science, Software analytics, Data analysis, Cultural analytics, Big data

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