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.
Highlights
An increasing number of individuals seek a high level of education to secure their future and improve their economic possibilities [1]
We introduced a novel high precision method to measure class attendance in an academic setting
Applying our method to a population of nearly 1,000 undergraduate students, we have shown that in this population attendance is weakly correlated (< 0.3) with academic success but it is reflected in the social interactions, which show that students of similar performance tend to be clustered
Summary
An increasing number of individuals seek a high level of education to secure their future and improve their economic possibilities [1]. Academic performance is an essential factor in the success of the post-education period with respect to employment [2]. For this reason, the ability to predict students’ academic success has been the subject of increasing interest. The knowledge regarding expected academic performance is a valuable input for educators and school administrators, as this information can be used to identify and target vulnerable students at risk of dropping out or in need of additional attention. Gathering information about attendance levels using conventional methods (surveys or self-reports) is subject to inherent biases [3] and can be costly to gather at the scale of schools or universities. Our method leverages data collected via smartphone sensors to identify class locations from clusters of students following
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