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IDE-Based Learning Analytics for Computing Education: A Process Model, Critical Review, and Research Agenda

Published: 29 August 2017 Publication History

Abstract

In recent years, learning process data have become increasingly easy to collect through computer-based learning environments. This has led to increased interest in the field of learning analytics, which is concerned with leveraging learning process data in order to better understand, and ultimately to improve, teaching and learning. In computing education, the logical place to collect learning process data is through integrated development environments (IDEs), where computing students typically spend large amounts of time working on programming assignments. While the primary purpose of IDEs is to support computer programming, they might also be used as a mechanism for delivering learning interventions designed to enhance student learning. The possibility of using IDEs both to collect learning process data, and to strategically intervene in the learning process, suggests an exciting design space for computing education research: that of IDE-based learning analytics. In order to facilitate the systematic exploration of this design space, we present an IDE-based data analytics process model with four primary activities: (1) Collect data, (2) Analyze data, (3) Design intervention, and (4) Deliver intervention. For each activity, we identify key design dimensions and review relevant computing education literature. To provide guidance on designing effective interventions, we describe four relevant learning theories, and consider their implications for design. Based on our review, we present a call-to-action for future research into IDE-based learning analytics.

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cover image ACM Transactions on Computing Education
ACM Transactions on Computing Education  Volume 17, Issue 3
Special Issue on Learning Analytics
September 2017
116 pages
EISSN:1946-6226
DOI:10.1145/3135995
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 August 2017
Accepted: 01 June 2017
Revised: 01 May 2017
Received: 01 August 2016
Published in TOCE Volume 17, Issue 3

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  1. Learning analytics
  2. learning interventions
  3. learning process data

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