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Class attendance, peer similarity, and academic performance in a large field study.

Authors
  • Kassarnig, Valentin1
  • Bjerre-Nielsen, Andreas2, 3
  • Mones, Enys4
  • Lehmann, Sune2, 4, 5
  • Lassen, David Dreyer2, 3
  • 1 Institute of Software Technology, Graz University of Technology, Graz, Austria. , (Austria)
  • 2 Center for Social Data Science, University of Copenhagen, Copenhagen, Denmark. , (Denmark)
  • 3 Department of Economics, University of Copenhagen, Copenhagen, Denmark. , (Denmark)
  • 4 Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark. , (Denmark)
  • 5 The Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark. , (Denmark)
Type
Published Article
Journal
PLoS ONE
Publisher
Public Library of Science
Publication Date
Jan 01, 2017
Volume
12
Issue
11
Identifiers
DOI: 10.1371/journal.pone.0187078
PMID: 29117190
Source
Medline
License
Unknown

Abstract

Identifying the factors that determine academic performance is an essential part of educational research. Existing research indicates that class attendance is a useful predictor of subsequent course achievements. The majority of the literature is, however, based on surveys and self-reports, methods which have well-known systematic biases that lead to limitations on conclusions and generalizability as well as being costly to implement. Here we propose a novel method for measuring class attendance that overcomes these limitations by using location and bluetooth data collected from smartphone sensors. Based on measured attendance data of nearly 1,000 undergraduate students, we demonstrate that early and consistent class attendance strongly correlates with academic performance. In addition, our novel dataset allows us to determine that attendance among social peers was substantially correlated (>0.5), suggesting either an important peer effect or homophily with respect to attendance.

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