Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education

Authors

  • Steven Lonn University of Michigan
  • Benjamin Koester University of Michigan

DOI:

https://doi.org/10.18608/jla.2019.62.8

Keywords:

dataset, collaboration, higher education, administrative data

Abstract

This article presents the case of the Learning Analytics Architecture (LARC) dataset, a collaborative effort at the University of Michigan to develop a common and extensible tool using administrative data and designed primarily for learning analytics researchers to investigate enrolled students’ academic careers, demographics, and related teaching and learning outcomes. The institutional context prior to the creation of the dataset and the rationale, design, development, and maintenance involved in creating LARC are all detailed. Also discussed are the procedures for access, documentation, and ensuring the continued usability and relevance of the dataset for a growing learning analytics and data science research community. The authors conclude the case description with recommendations for institutions seeking to replicate this effort.

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Published

2019-07-23

How to Cite

Lonn, S., & Koester, B. (2019). Rearchitecting Data for Researchers: A Collaborative Model for Enabling Institutional Learning Analytics in Higher Education. Journal of Learning Analytics, 6(2), 107–119. https://doi.org/10.18608/jla.2019.62.8

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Section

Data and Tools Reports

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