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      Orderliness predicts academic performance: behavioural analysis on campus lifestyle

      , , , , , , , ,
      Journal of The Royal Society Interface
      The Royal Society

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          Abstract

          <p class="first" id="d4894854e276">Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that were mainly based on questionnaire surveys, recent literature suggests that unobtrusive digital data bring us unprecedented opportunities to study students' lifestyles in the campus. In this paper, we collect behavioural records from undergraduate students' ( <i>N</i> = 18 960) smart cards and propose two high-level behavioural characters, orderliness and diligence. The former is a novel entropy-based metric that measures the regularity of campus daily life, which is estimated here based on temporal records of taking showers and having meals. Empirical analyses on such large-scale unobtrusive behavioural data demonstrate that academic performance (GPA) is significantly correlated with orderliness. Furthermore, we show that orderliness is an important feature to predict academic performance, which improves the prediction accuracy even in the presence of students' diligence. Based on these analyses, education administrators could quantitatively understand the major factors leading to excellent or poor performance, detect undesirable abnormal behaviours in time and thus implement effective interventions to better guide students' campus lives at an early stage when necessary. </p>

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          Author and article information

          Journal
          Journal of The Royal Society Interface
          J. R. Soc. Interface
          The Royal Society
          1742-5689
          1742-5662
          September 19 2018
          September 2018
          September 19 2018
          September 2018
          : 15
          : 146
          : 20180210
          Article
          10.1098/rsif.2018.0210
          6170765
          30232241
          6ff3ba16-2e48-4150-b36a-25d7d422b411
          © 2018

          http://royalsocietypublishing.org/licence

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