Unlocking the potential of learning analytics in computing education

Shuchi Grover, Ari Korhonen

Research output: Contribution to journalEditorialScientificpeer-review

3 Citations (Scopus)

Abstract

Big data and online learning have been among the main drivers pushing forward the field of learning analytics (LA). As with work being done in other disciplines, LA in computing education is closely intertwined with the sister field of educational data mining (EDM), with the latter focused more on the technical challenges of extracting meaning from data using data-driven techniques. Learning analytics can be put to use to understand aspects of computing education that are important and yet too difficult, time-consuming, expensive, or not possible otherwise. These include understanding and measuring learning during the process of programming. In addition, some of the papers also emphasize the socio-cognitive aspects of learning computing, including understanding learners as part of a participatory culture that includes social aspects such as how they interact with their peers during the programming process.

Original languageEnglish
Article number11e
JournalACM Transactions on Computing Education
Volume17
Issue number3
DOIs
Publication statusPublished - 1 Aug 2017
MoE publication typeA1 Journal article-refereed

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