What are Learning Analytics?


Learning analytics refers to the interpretation of a wide range of data produced by and gathered on behalf of students to assess academic progress, predict future performance, and spot potential issues. Data are collected from explicit student actions, such as completing assignments and taking exams, and from tacit actions, including online social interactions, extracurricular activities, posts on discussion forums, and other activities that are not typically viewed as part of a student’s work. The goal of learning analytics is to enable teachers and [[#|schools]] to tailor educational opportunities to each student’s level of need and ability. Learning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understanding of teaching and learning, and to tailor education to individual students more effectively. Still in its very early stages, and not yet at the stage where it is practical to talk about implementation in schools, learning analytics is an emerging scientific practice that hopes to redefine what we know about learning by mining the vast amount of data produced by students in academic activities.


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(1) How might this technology be relevant to the educational sector you know best?

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(2) What themes are missing from the above description that you think are important?

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(3) What do you see as the potential impact of this technology on Singapore K12 education?

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(4) Do you have or know of a project working in this area?

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