10.6084/m9.figshare.7043471.v1
Yi Cao
Yi
Cao
Jian Gao
Jian
Gao
Defu Lian
Defu
Lian
Zhihai Rong
Zhihai
Rong
Jiatu Shi
Jiatu
Shi
Qing Wang
Qing
Wang
Yifan Wu
Yifan
Wu
Huaxiu Yao
Huaxiu
Yao
Tao Zhou
Tao
Zhou
Supplementary Dataset from Orderliness predicts academic performance: behavioural analysis on campus lifestyle
The Royal Society
2018
computational social science
campus behaviour
academic performance
data science
orderliness
human behaviour
2018-09-04 16:46:30
Dataset
https://rs.figshare.com/articles/dataset/Supplementary_Dataset_from_Orderliness_predicts_academic_performance_behavioural_analysis_on_campus_lifestyle/7043471
Quantitative understanding of relationships between students' behavioural patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that 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, say 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 in an early stage when necessary.