- -

How students' friendship network affects their GPA ranking: A data-driven approach linking friendship with daily behaviour

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

How students' friendship network affects their GPA ranking: A data-driven approach linking friendship with daily behaviour

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Li, Yongli es_ES
dc.contributor.author Li, Sihan es_ES
dc.contributor.author Wei, Chuang es_ES
dc.contributor.author Liu, Jiaming es_ES
dc.date.accessioned 2021-02-06T04:33:49Z
dc.date.available 2021-02-06T04:33:49Z
dc.date.issued 2020 es_ES
dc.identifier.issn 0959-3845 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160836
dc.description.abstract [EN] Purpose Due to the unintentional or even the intentional mistakes arising from a survey, the purpose of this paper is to present a data-driven method for detecting students' friendship network based on their daily behaviour data. Based on the detected friendship network, this paper further aims to explore how the considered network effects (i.e. friend numbers (FNs), structural holes (SHs) and friendship homophily) influence students' GPA ranking. Design/methodology/approach The authors collected the campus smart card data of 8,917 sophomores registered in one Chinese university during one academic year, uncovered the inner relationship between the daily behaviour data with the friendship to infer the friendship network among students, and further adopted the ordered probit regression model to test the relationship between network effects with GPA rankings by controlling several influencing variables. Findings The data-driven approach of detecting friendship network is demonstrated to be useful and the empirical analysis illustrates that the relationship between GPA ranking and FN presents an inverted "U-shape", richness in SHs positively affects GPA ranking, and making more friends within the same department will benefit promoting GPA ranking. Originality/value The proposed approach can be regarded as a new information technology for detecting friendship network from the real behaviour data, which is potential to be widely used in many scopes. Moreover, the findings from the designed empirical analysis also shed light on how to improve GPA rankings from the angle of network effect and further guide how many friends should be made in order to achieve the highest GPA level, which contributes to the existing literature. es_ES
dc.description.sponsorship This research was supported by the research grants from the National Natural Science Foundation of China (71771041 and 71501032). es_ES
dc.language Inglés es_ES
dc.publisher Emerald es_ES
dc.relation.ispartof Information Technology & People es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Network analysis es_ES
dc.subject Education es_ES
dc.subject Social networking es_ES
dc.subject Knowledge discovery es_ES
dc.subject Information processing theory es_ES
dc.title How students' friendship network affects their GPA ranking: A data-driven approach linking friendship with daily behaviour es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1108/ITP-03-2018-0148 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//71771041/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//71501032/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Gestión de la Innovación y del Conocimiento - Institut de Gestió de la Innovació i del Coneixement es_ES
dc.description.bibliographicCitation Li, Y.; Li, S.; Wei, C.; Liu, J. (2020). How students' friendship network affects their GPA ranking: A data-driven approach linking friendship with daily behaviour. Information Technology & People. 33(2):535-553. https://doi.org/10.1108/ITP-03-2018-0148 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1108/ITP-03-2018-0148 es_ES
dc.description.upvformatpinicio 535 es_ES
dc.description.upvformatpfin 553 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 33 es_ES
dc.description.issue 2 es_ES
dc.relation.pasarela S\412758 es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES
dc.description.references Aral, S., & Walker, D. (2012). Identifying Influential and Susceptible Members of Social Networks. Science, 337(6092), 337-341. doi:10.1126/science.1215842 es_ES
dc.description.references Becker, W. E., & Kennedy, P. E. (1992). A Graphical Exposition of the Ordered Probit. Econometric Theory, 8(01), 127-131. doi:10.1017/s0266466600010781 es_ES
dc.description.references Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41-55. doi:10.1016/j.jeconom.2008.12.021 es_ES
dc.description.references Burt, R. S. (2004). Structural Holes and Good Ideas. American Journal of Sociology, 110(2), 349-399. doi:10.1086/421787 es_ES
dc.description.references Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492-511. doi:10.1037/pspp0000102 es_ES
dc.description.references An Economic Model of Friendship: Homophily, Minorities, and Segregation. (2009). Econometrica, 77(4), 1003-1045. doi:10.3982/ecta7528 es_ES
dc.description.references Currarini, S., Jackson, M. O., & Pin, P. (2010). Identifying the roles of race-based choice and chance in high school friendship network formation. Proceedings of the National Academy of Sciences, 107(11), 4857-4861. doi:10.1073/pnas.0911793107 es_ES
dc.description.references Duckworth, A. L., & Seligman, M. E. P. (2005). Self-Discipline Outdoes IQ in Predicting Academic Performance of Adolescents. Psychological Science, 16(12), 939-944. doi:10.1111/j.1467-9280.2005.01641.x es_ES
dc.description.references Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274-15278. doi:10.1073/pnas.0900282106 es_ES
dc.description.references Epple, D. and Romano, R.E. (2011), “Peer effects in education: a survey of the theory and evidence”, in Benhabib, J., Bisin, A. and Jackson, M. (Eds), Handbook of Social Economics, Vol. 1, North-Holland, pp. 1053-1163. es_ES
dc.description.references Farmer, T., Xie, H., Cairns, B. and Hutchins, B. (2007), “Social synchrony, peer networks, and aggression in school”, in Hawley, P., Little, T. and Rodkin, P. (Eds), Aggression and Adaptation: The Bright Side to Bad Behavior, Erlbaum, Mahwah, NJ, pp. 209-234. es_ES
dc.description.references Fricker, R. D., & Schonlau, M. (2002). Advantages and Disadvantages of Internet Research Surveys: Evidence from the Literature. Field Methods, 14(4), 347-367. doi:10.1177/152582202237725 es_ES
dc.description.references Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251. doi:10.2307/1913827 es_ES
dc.description.references Jain, T., & Kapoor, M. (2015). The Impact of Study Groups and Roommates on Academic Performance. Review of Economics and Statistics, 97(1), 44-54. doi:10.1162/rest_a_00454 es_ES
dc.description.references Jurbergs, N., Palcic, J., & Kelley, M. L. (2007). School-home notes with and without response cost: Increasing attention and academic performance in low-income children with attention-deficit/hyperactivity disorder. School Psychology Quarterly, 22(3), 358-379. doi:10.1037/1045-3830.22.3.358 es_ES
dc.description.references Lee, J., Lee, H., & Park, J.-G. (2014). Exploring the impact of empowering leadership on knowledge sharing, absorptive capacity and team performance in IT service. Information Technology & People, 27(3), 366-386. doi:10.1108/itp-10-2012-0115 es_ES
dc.description.references Li, Y., Zhang, D., Luo, P., & Jiang, J. (2017). Interpreting the formation of co-author networks via utility analysis. Information Processing & Management, 53(3), 624-639. doi:10.1016/j.ipm.2016.12.007 es_ES
dc.description.references Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28(2), 203-208. doi:10.3758/bf03204766 es_ES
dc.description.references McCabe, J. (2016), “How your college friendships help you or don’t”, Dartmouth News, 16 December, available at: https://news.dartmouth.edu/news/2016/12/how-your-college-friendships-help-you-or-dont es_ES
dc.description.references Murayama, K., & Elliot, A. J. (2012). The competition–performance relation: A meta-analytic review and test of the opposing processes model of competition and performance. Psychological Bulletin, 138(6), 1035-1070. doi:10.1037/a0028324 es_ES
dc.description.references Paluck, E. L., & Green, D. P. (2009). Prejudice Reduction: What Works? A Review and Assessment of Research and Practice. Annual Review of Psychology, 60(1), 339-367. doi:10.1146/annurev.psych.60.110707.163607 es_ES
dc.description.references Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353-387. doi:10.1037/a0026838 es_ES
dc.description.references Roseth, C. J., Johnson, D. W., & Johnson, R. T. (2008). Promoting early adolescents’ achievement and peer relationships: The effects of cooperative, competitive, and individualistic goal structures. Psychological Bulletin, 134(2), 223-246. doi:10.1037/0033-2909.134.2.223 es_ES
dc.description.references Sivo, S., Saunders, C., Chang, Q., … Jiang, J. (2006). How Low Should You Go? Low Response Rates and the Validity of Inference in IS Questionnaire Research. Journal of the Association for Information Systems, 7(6), 351-414. doi:10.17705/1jais.00093 es_ES
dc.description.references Smith, J. F., & Skrbiš, Z. (2017). A social inequality of motivation? The relationship between beliefs about academic success and young people’s educational attainment. British Educational Research Journal, 43(3), 441-465. doi:10.1002/berj.3272 es_ES
dc.description.references Steinberg, L., & Morris, A. S. (2001). Adolescent Development. Annual Review of Psychology, 52(1), 83-110. doi:10.1146/annurev.psych.52.1.83 es_ES
dc.description.references Stock, J. H., Wright, J. H., & Yogo, M. (2002). A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments. Journal of Business & Economic Statistics, 20(4), 518-529. doi:10.1198/073500102288618658 es_ES
dc.description.references Van der Aalst, W. M. P., Reijers, H. A., & Song, M. (2005). Discovering Social Networks from Event Logs. Computer Supported Cooperative Work (CSCW), 14(6), 549-593. doi:10.1007/s10606-005-9005-9 es_ES
dc.description.references Vasudeva, G., Zaheer, A., & Hernandez, E. (2013). The Embeddedness of Networks: Institutions, Structural Holes, and Innovativeness in the Fuel Cell Industry. Organization Science, 24(3), 645-663. doi:10.1287/orsc.1120.0780 es_ES
dc.description.references Vedel, A. (2014). The Big Five and tertiary academic performance: A systematic review and meta-analysis. Personality and Individual Differences, 71, 66-76. doi:10.1016/j.paid.2014.07.011 es_ES
dc.description.references ZHOU, M., & KIM, S. (2006). Community Forces, Social Capital, and Educational Achievement: The Case of Supplementary Education in the Chinese and Korean Immigrant Communities. Harvard Educational Review, 76(1), 1-29. doi:10.17763/haer.76.1.u08t548554882477 es_ES
dc.description.references Zimmerman, K. (2016), “Can having a best friend at work make you more productive?”, Forbes, 5 December, available at: www.forbes.com/sites/kaytiezimmerman/2016/12/05/can-having-a-best-friend-at-work-make-you-more-productive/#25f80bfc43bb es_ES
dc.description.references Heckman, J. J. (1978). Dummy Endogenous Variables in a Simultaneous Equation System. Econometrica, 46(4), 931. doi:10.2307/1909757 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem