Orderliness of Campus Lifestyle Predicts Academic Performance: A Case Study in Chinese University

Different from the western education system, Chinese teachers and parents strongly encourage students to have a regular lifestyle. However, due to the lack of large-scale behavioral data, the relation between living patterns and academic performance remains poorly understood. In this chapter, we analyze large-scale behavioral records of 18,960 students within a Chinese university campus. In particular, we introduce orderliness, a novel entropy-based metric, to measure the regularity of campus lifestyle. Empirical analyses demonstrate that orderliness is significantly and positively correlated with academic performance, and it can improve the prediction accuracy on academic performance at the presence of diligence, another behavioral metric that estimates students’ studying hardness. This work supports the eastern pedagogy that emphasizes the value of regular lifestyle.

[1]  E. Kreyszig,et al.  Advanced Engineering Mathematics. , 1974 .

[2]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[3]  C. Spearman The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.

[4]  R. Fisher Social Desirability Bias and the Validity of Indirect Questioning , 1993 .

[5]  Yuri M. Suhov,et al.  Nonparametric Entropy Estimation for Stationary Processesand Random Fields, with Applications to English Text , 1998, IEEE Trans. Inf. Theory.

[6]  A. Furnham,et al.  Personality predicts academic performance: Evidence from two longitudinal university samples , 2003 .

[7]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[8]  Angela L. Duckworth,et al.  Self-Discipline Outdoes IQ in Predicting Academic Performance of Adolescents , 2005, Psychological science.

[9]  Maureen A. Conard,et al.  Aptitude is not enough: How personality and behavior predict academic performance. , 2006 .

[10]  Simine Vazire,et al.  The self-report method. , 2007 .

[11]  Barbara S. Grave The effect of student time allocation on academic achievement , 2010 .

[12]  Jin Young Park,et al.  The effects of Internet addiction on the lifestyle and dietary behavior of Korean adolescents , 2010, Nutrition research and practice.

[13]  M. Credé,et al.  Class Attendance in College , 2010 .

[14]  Soyeon Ahn,et al.  Student Academic Performance Outcomes of a Classroom Physical Activity Intervention: A Pilot Study. , 2012 .

[15]  J. Korkeila,et al.  Internet Addiction , 2020 .

[16]  Brandy M. Roane,et al.  The Role of Sleep in Predicting College Academic Performance: Is it a Unique Predictor? , 2013, Behavioral sleep medicine.

[17]  N. Akhter Relationship between Internet Addiction and Academic Performance among University Undergraduates. , 2013 .

[18]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[19]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[20]  Eitel J. M. Lauría,et al.  Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative , 2014, J. Learn. Anal..

[21]  A. Vedel,et al.  The Big Five and tertiary academic performance: A systematic review and meta-analysis. , 2014 .

[22]  A. Poropat Other-rated personality and academic performance: Evidence and implications , 2014 .

[23]  W. Van den Noortgate,et al.  The influence of classroom disciplinary climate of schools on reading achievement: a cross-country comparative study , 2015 .

[24]  Rayid Ghani,et al.  A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes , 2015, KDD.

[25]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[26]  Dan Pei,et al.  EDUM: classroom education measurements via large-scale WiFi networks , 2016, UbiComp.

[27]  S. Wolter,et al.  The more, the better? The impact of instructional time on student performance , 2016, SSRN Electronic Journal.

[28]  H. Vincent Poor,et al.  Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance , 2016, IEEE Transactions on Signal Processing.

[29]  Chris Baumann,et al.  School Discipline, School Uniforms and Academic Performance. , 2016 .

[30]  Christian Montag,et al.  Toward Psychoinformatics: Computer Science Meets Psychology , 2016, Comput. Math. Methods Medicine.

[31]  Jonathan P. Beauchamp,et al.  Genome-wide association study identifies 74 loci associated with educational attainment , 2016, Nature.

[32]  M. Brand,et al.  Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model , 2016, Neuroscience & Biobehavioral Reviews.

[33]  R. Plomin,et al.  Predicting educational achievement from DNA , 2016, Molecular psychiatry.

[34]  R. Plomin,et al.  Erratum: Predicting educational achievement from DNA , 2017, Molecular Psychiatry.

[35]  Kathrin U. Müller,et al.  Sleep habits, academic performance, and the adolescent brain structure , 2017, Scientific Reports.

[36]  J. Hill,et al.  The association between obesity and academic performance in youth: a systematic review , 2017, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[37]  Piotr Sapiezynski,et al.  Academic performance and behavioral patterns , 2017, EPJ Data Science.

[38]  M. Blettner,et al.  Chronic health conditions and school performance in first graders: A prospective cohort study , 2018, PloS one.

[39]  Zhihai Rong,et al.  Orderliness predicts academic performance: behavioural analysis on campus lifestyle , 2017, Journal of The Royal Society Interface.

[40]  C. Montag,et al.  The relationship between Internet Use Disorder, depression and burnout among Chinese and German college students. , 2019, Addictive behaviors.

[41]  Paiheng Xu,et al.  On predictability of time series , 2018, Physica A: Statistical Mechanics and its Applications.

[42]  Yicheng Zhang,et al.  Computational socioeconomics , 2019, Physics Reports.