Vol. 4 No. 2 (2017): Shape of Educational Data

View Vol. 4 No. 2 (2017): Shape of Educational Data

Published: 2017-07-05

Editorial

Learning Analytics: Looking to the Future

Simon Knight, Alyssa F. Wise, Xavier Ochoa, Arnon Hershkovitz

1–5
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Special section: Shape of Educational Data

Shape of Educational Data: Interdisciplinary Perspectives

Colleen M. Ganley, Sara A. Hart

6–11
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Calculus Course Assessment Data

Matti J. Pauna

12–21
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Methods of Interoperability: Moodle and WeBWorK

Michael Gage

22–35
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Video-based Big Data Analytics in Cyberlearning

Shuangbao Paul Wang, William Kelly

36–46
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A User’s Guide to Topological Data Analysis

Elizabeth Munch

47–61
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Study Paths, Riemann Surfaces And Strebel Differentials

Peter Buser, Klaus-Dieter Semmler

62–75
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Shapes of Educational Data in an Online Calculus Course

Olga Caprotti

76–90
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How Flexible Is Your Data? A Comparative Analysis of Scoring Methodologies across Learning Platforms in the Context of Group Differentiation

Korinn S. Ostrow, Yan Wang, Neil T. Heffernan

91–112
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Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance

Dragan Gasevic, Jelena Jovanovic, Abelardo Pardo, Shane Dawson

113–128
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Individual Differences Related to College Students’ Course Performance in Calculus II

Sara Hart, Mia Daucourt, Colleen Ganley

129–153
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Is Learning Data in the Right Shape?

Anthony E. Kelly

154–159
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Research Papers

Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization

Ruth Elizabeth Deakin Crick, Simon Knight, Steven Barr

160–188
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Using Keystroke Analytics to Improve Pass-Fail Classifiers

Kevin Casey

189–211
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Automating the Detection of Reflection-on-Action

Jenny Saucerman, A. R. Ruis, David Williamson Shaffer

212–239
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How do Students Learn to See Concepts in Visualizations? Social Learning Mechanisms with Physical and Virtual Representations

Martina A. Rau

240–263
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