Shape of Educational Data: Interdisciplinary Perspectives

Colleen M. Ganley
Sara A. Hart

Abstract


This paper is a guest editorial for a special section that forms the proceedings of the Shape of Educational Data meeting. This special section features papers that apply methods from multiple fields — including mathematics, computer science, educational psychology, and learning analytics — to describe and predict student learning in online platforms. The special section is organized such that the first set of articles discusses different online learning systems (WEPS, WeBWorK, and inVideo) and data that can be analyzed from these systems. The second set of articles involves descriptions of topological data analyses that can be helpful to researchers in learning analytics and educational psychology to better model student learning in online courses. The third set of articles uses data obtained from online systems to study factors related to student learning. Due to these multiple approaches, we can gain insight into the types of data available, the ways in which we can measure particular constructs related to learning using these data, and the ways we can analyze these data, including statistical approaches and visualizations.

Keywords


topology, geometry, learning analytics, educational psychology, data visualization

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References


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DOI: http://dx.doi.org/10.18608/jla.2017.42.2

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