Beating the odds: Identifying the top predictors of resilience among Hong Kong students

Springer Science and Business Media LLC - Tập 15 Số 5 - Trang 1921-1944 - 2022
Faming Wang1, Ronnel B. King2, Shing On Leung1
1Faculty of Education, University of Macau, Macao, China
2Faculty of Education, Centre for the Enhancement of Teaching and Learning, The University of Hong Kong, Hong Kong, China

Tóm tắt

Từ khóa


Tài liệu tham khảo

King, R. B. (2020). Mindsets are contagious: The social contagion of implicit theories of intelligence among classmates. British Journal of Educational Psychology, 90(2), 349-363. https://doi.org/10.1111/bjep.12285

King, R. B., & Mendoza, N. B. (2020). Achievement goal contagion: Mastery and performance goals spread among classmates. Social Psychology of Education, 23(3), 795-814. https://doi.org/10.1007/s11218-020-09559-x

Wang, H., King, R. B., & McInerney, D. M. (2021). Ability grouping and student performance: A longitudinal investigation of teacher support as a mediator and moderator. Research Papers in Education, 1-22. https://doi.org/10.1080/02671522.2021.1961293

Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2018). Academic Resilience: What schools and countries do to help disadvantaged students succeed in PISA. OECD Publishing. https://doi.org/10.1787/e22490ac-en

Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2021). What school factors are associated with the success of socioeconomically disadvantaged students? An empirical investigation using PISA data. Social Indicators Research, 1–33. https://doi.org/10.1007/s11205-021-02668-w

Aldridge, J. M., Fraser, B. J., Fozdar, F., Ala’i, K., Earnest, J., & Afari, E. (2016). Students’ perceptions of school climate as determinants of well-being, resilience and identity. Improving Schools, 19(1), 5–26. https://doi.org/10.1177/1365480215612616

Avvisati, F. (2020). The measure of socioeconomic status in PISA: A review and some suggested improvements. Large-Scale Assessments in Education, 8, 1–37. https://doi.org/10.1186/s40536-020-00086-x

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1

Barzilay, R., Moore, T. T., Greenberg, D. M., DiDomenico, G. E., Brown, L. A., White, L. K. … Gur, R. E. (2020). Resilience, COVID-19-related stress, anxiety and depression during the pandemic in a large population enriched for healthcare providers. Translational Psychiatry, 10(291), https://doi.org/10.1038/s41398-020-00982-4

Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7

Boden, J. M., Sanders, J., Munford, R., & Liebenberg, L. (2018). The same but different? Applicability of a general resilience model to understand a population of vulnerable youth. Child Indicators Research, 11(1), 79–96. https://doi.org/10.1007/s12187-016-9422-y

Borman, G. D., & Rachuba, L. T. (2000, April). The characteristics of schools and classrooms attended by successful minority students [Paper presentation]. New Orleans, LA: Annual meeting of the American Educational Research Association

Borman, G. D., & Overman, L. T. (2004). Academic resilience in mathematics among poor and minority students. The Elementary School Journal, 104(3), 177–195. https://doi.org/10.1086/499748

Breiman, L. (2001a). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215. https://doi.org/10.1214/ss/1009213726

Breiman, L. (2001b). Random forests. Machine Learning, 45(1), 5–32

https://doi.org/10.1023/A:1010933404324

Bzdok, D., Krzywinski, M., & Altman, N. (2017). Machine learning: a primer. Nature Methods, 14, 1119–1120. https://doi.org/10.1038/nmeth.4526

Cappella, E., & Weinstein, R. S. (2001). Turning around reading achievement: Predictors of high school students’ academic resilience. Journal of Educational Psychology, 93(4), 758–771. https://doi.org/10.1037/0022-0663.93.4.758

Caro, D. H., Lenkeit, J., & Kyriakides, L. (2016). Teaching strategies and differential effectiveness across learning contexts: Evidence from PISA 2012. Studies in Educational Evaluation, 49, 30–41. https://doi.org/10.1016/j.stueduc.2016.03.005

Cefai, C. (2007). Resilience for all: A study of classrooms as protective contexts. Emotional and Behavioural Difficulties, 12(2), 119–134. https://doi.org/10.1080/13632750701315516

Cheung, K. C. (2017). The effects of resilience in learning variables on mathematical literacy performance: A study of learning characteristics of the academic resilient and advantaged low achievers in Shanghai, Singapore, Hong Kong, Taiwan and Korea. Educational Psychology, 37(8), 965–982. https://doi.org/10.1080/01443410.2016.1194372

Cheung, K. C., Sit, P. S., Soh, K. C., Ieong, M. K., & Mak, S. K. (2014). Predicting academic resilience with reading engagement and demographic variables: Comparing Shanghai, Hong Kong, Korea, and Singapore from the PISA perspective. The Asia-Pacific Education Researcher, 23(4), 895–909. https://doi.org/10.1007/s40299-013-0143-4

Chiu, M. M., & Khoo, L. (2005). Effects of resources, inequality, and privilege bias on achievement: Country, school, and student level analyses. American Educational Research Journal, 42(4), 575–603. https://doi.org/10.3102/00028312042004575

Chiu, M. M., & Walker, A. (2007). Leadership for social justice in Hong Kong schools: Addressing mechanisms of inequality. Journal of Educational Administration, 45(6), 724–739. https://doi.org/10.1108/09578230710829900

Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, F., Mood, A. M., & Weinfeld, F. D. (1966). Equality of educational opportunity. U.S. Government Printing Office

Cordero, J. M., & Mateos-Romero, L. (2021). Exploring factors related with resilience in primary education: Evidence from European countries. Studies in Educational Evaluation, 70, Article 101045. https://doi.org/10.1016/j.stueduc.2021.101045

Couronné, R., Probst, P., & Boulesteix, A. L. (2018). Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics, 19(1), 1–14. https://doi.org/10.1186/s12859-018-2264-5

Cutuli, J. J., DesJardins, C. D., Herbes, J. E., Long, J. D., Heistad, D., Chan, C. … Masten, A. S. (2013). Academic achievement trajectories of homeless and highly mobile students: Resilience in the context of chronic and acute risk. Child Development, 84(3), 841–857. https://doi.org/10.1111/cdev.12013

Das, D. (2019). Academic resilience among children from disadvantaged social groups in India. Social Indicators Research, 145(2), 719–739. https://doi.org/10.1007/s11205-018-1899-y

Davydov, D. M., Stewart, R., Ritchie, K., & Chaudieu, I. (2010). Resilience and mental health. Clinical Psychology Review, 30(5), 479–495. https://doi.org/10.1016/j.cpr.2010.03.003

Depren, S. K., & Depren, Ö. (2021). Cross-Cultural Comparisons of the Factors Influencing the High Reading Achievement in Turkey and China: Evidence from PISA 2018. The Asia-Pacific Education Researcher, 1–11. https://doi.org/10.1007/s40299-021-00584-8

Destin, M., Hanselman, P., Buontempo, J., Tipton, E., & Yeager, D. S. (2019). Do student mindsets differ by socioeconomic status and explain disparities in academic achievement in the United States? AERA Open 5(3), 1–12. https://doi.org/10.1177/2332858419857706

Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure. Journal of Applied Psychology, 82(2), 221–234. https://doi.org/10.1037/0021-9010.82.2.221

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

Gjicali, K., & Lipnevich, A. A. (2021). Got math attitude?(In) direct effects of student mathematics attitudes on intentions, behavioral engagement, and mathematics performance in the US PISA. Contemporary Educational Psychology, 67, 102019. https://doi.org/10.1016/j.cedpsych.2021.102019

Greenberg, M. T. (2006). Promoting resilience in children and youth: Preventive interventions and their interface with neuroscience. Annals of the New York Academy of Sciences, 1094(1), 139–150. https://doi.org/10.1196/annals.1376.013

Grömping, U. (2009). Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4), 308–319. https://doi.org/10.1198/tast.2009.08199

Harwell, M., Maeda, Y., Bishop, K., & Xie, A. (2017). The surprisingly modest relationship between SES and educational achievement. Journal of Experimental Education, 85(2), 197–214. https://doi.org/10.1080/00220973.2015.1123668

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses related to

achievement. Routledge, Taylor and Francis Group

King, R. B., & Trinidad, J. E. (2021) Growth mindset predicts achievement only among rich students: Examining the interplay between mindset and socioeconomic status. Social Psychology of Education 24(3) 635-652. doi: 10.1007/s11218-021-09616-z

Klassen, R. M., & Usher, E. L. (2010). Self-efficacy in educational settings. Recent research

Urdan, T. C., & Karabenick, S. A. (Eds.). The decade

ahead: Theoretical perspectives on motivation and achievement (pp. 1–33). Emerald

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, 14(2), 1137–1145

Korpershoek, H., Canrinus, E. T., Fokkens-Bruinsma, M., & de Boer, H. (2020). The relationships between school belonging and students’ motivational, social-emotional, behavioural, and academic outcomes in secondary education: A meta-analytic review. Research Papers in Education, 35(6), 641–680. https://doi.org/10.1080/02671522.2019.1615116

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56(1), 16–26. https://doi.org/10.1037/0003-066X.56.1.16

Kumpfer, K. L. (1999). Factors and processes contributing to resilience: The resilience framework. In M. Glantz, & J. L. Johnson (Eds.), Resilience and development: Positive life adaptations (pp. 179–224). Plenum Press

Kwan, Y. W., & Wong, A. F. (2014). The constructivist classroom learning environment and its associations with critical thinking ability of secondary school students in Liberal Studies. Learning Environments Research, 17(2), 191–207. https://doi.org/10.1007/s10984-014-9158-x

Lavrijsen, J., Vansteenkiste, M., Boncquet, M., & Verschueren, K. (2021). Does motivation predict changes in academic achievement beyond intelligence and personality? A multitheoretical perspective. Journal of Educational Psychology. https://doi.org/10.1037/edu0000666. Advance online publication

Lee, J. (2016). Attitude toward school does not predict academic achievement. Learning and Individual Differences, 52, 1–9. https://doi.org/10.1016/j.lindif.2016.09.009

Lee, J., & Shute, V. J. (2010). Personal and social-contextual factors in K-12 academic performance: An integrative perspective on student learning. Educational Psychologist, 45, 185–202. https://doi.org/10.1080/00461520.2010.493471

Lee, W. O., & Manzon, M. (2014). The issue of equity and quality of education in Hong Kong. The Asia-Pacific Education Researcher, 23(4), 823–833. https://doi.org/10.1007/s40299-014-0214-1

Li, H. (2017). The ‘secrets’ of Chinese students’ academic success: Academic resilience among students from highly competitive academic environments. Educational Psychology, 37(8), 1001–1014. https://doi.org/10.1080/01443410.2017.1322179

Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18–22

Linnenluecke, M. K. (2017). Resilience in business and management research: A review of influential publications and a research agenda. International Journal of Management Reviews, 19(1), 4–30. https://doi.org/10.1111/ijmr.12076

Lui, H. K. (2018). Growing socioeconomic inequalities. In T. Lui, W. K. Chiu, & R. Yep (Eds.), Routledge handbook of contemporary Hong Kong (pp. 247–258). Routledge

Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71(3), 543–562. https://doi.org/10.1111/1467-8624.00164

Marcot, B. G., & Hanea, A. M. (2020). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics, 1–23. https://doi.org/10.1007/s00180-020-00999-9

Marquez, J., & Long, E. (2021). A global decline in adolescents’ subjective well-being: a comparative study exploring patterns of change in the life satisfaction of 15-year-old students in 46 countries. Child Indicators Research, 14(3), 1251–1292. https://doi.org/10.1007/s12187-020-09788-8

Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43(3), 267–281. https://doi.org/10.1002/pits.20149

Masten, A. S. (2001). Ordinary magic: Resilience processes in development. American Psychologist, 56(3), 227–238. https://doi.org/10.1037/0003-066X.56.3.227

Masten, A. S., Best, K. M., & Garmezy, N. (1990). Resilience and development: Contributions from the study of children who overcome adversity. Development and Psychopathology, 2(4), 425–444. https://doi.org/10.1017/S0954579400005812

Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments: Lessons from research on successful children. American Psychologist, 53(2), 205–220. https://doi.org/10.1037/0003-066X.53.2.205

McAlexander, R. J., & Mentch, L. (2020). Predictive inference with random forests: A new perspective on classical analyses. Research & Politics, 7(1), https://doi.org/10.1177/2053168020905487. Article 2053168020905487

Moreno-Maldonado, C., Jiménez-Iglesias, A., Rivera, F., & Moreno, C. (2020). Characterization of resilient adolescents in the context of parental unemployment. Child Indicators Research, 13(2), 681–702. https://doi.org/10.1007/s12187-019-09640-8

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. https://doi.org/10.1037/a0028324

OECD. (2011). Against the odds: Disadvantaged students who succeed in school. OECD Publishing. https://doi.org/10.1787/9789264090873-en

OECD. (2019a). PISA 2018 results (Volume II): Where all students can succeed. OECD Publishing. https://doi.org/10.1787/b5fd1b8f-en

OECD. (2019b). PISA 2018 Assessment and Analytical Framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en

OECD (2020). PISA 2018 technical report: Scaling procedures and construct validation of context questionnaire data. OECD Publishing. https://www.oecd.org/pisa/data/pisa2018technicalreport/PISA2018_Technical-Report-Chapter-16-Background-Questionnaires.pdf

Padron, Y. N., Waxman, H. C., & Lee, Y. H. (2014). Classroom learning environment differences between resilient, average, and non-resilient middle school students in reading. Education and Urban Society, 46(2), 264–283. https://doi.org/10.1177/0013124512446217

Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525–556. https://doi.org/10.3102/00346543074004525

Rutter, M. (2012). Resilience as a dynamic concept. Development and Psychopathology, 24(2), 335–344. https://doi.org/10.1017/S0954579412000028

Rudd, G., Meissel, K., & Meyer, F. (2021). Measuring academic resilience in quantitative research: A systematic review of the literature. Educational Research Review, Article, 100402, https://doi.org/10.1016/j.edurev.2021.100402

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020

Sandoval-Hernández, A., & Białowolski, P. (2016). Factors and conditions promoting academic resilience: a TIMSS-based analysis of five Asian education systems. Asia Pacific Education Review, 17(3), 511–520. https://doi.org/10.1007/s12564-016-9447-4

Schiefele, U., & Schaffner, E. (2015). Teacher interests, mastery goals, and self-efficacy as predictors of instructional practices and student motivation. Contemporary Educational Psychology, 42, 159–171. https://doi.org/10.1016/j.cedpsych.2015.06.005

Schunk, D. H., & Pajares, F. (2009). Self-efficacy theory. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp.35–54).Routledge

Šimundić, A. M. (2009). Measures of diagnostic accuracy: Basic definitions. Journal of the International Federation of Clinical Chemistry and Laboratory Medicine, 19(4), 203–211. https://www.ncbi.nlm.nih.gov/pubmed/27683318

Sinclair, J., Jang, E. E., & Rudzicz, F. (2021). Using machine learning to predict children’s reading comprehension from linguistic features extracted from speech and writing. Journal of Educational Psychology, 113(6), 1088–1106. https://doi.org/10.1037/edu0000658

Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417

Smith, P. F., Ganesh, S., & Liu, P. (2013). A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods, 220(1), 85–91. https://doi.org/10.1016/j.jneumeth.2013.08.024

Stockard, J., Wood, T. W., Coughlin, C., & Rasplica Khoury, C. (2018). The effectiveness of direct instruction curricula: A meta-analysis of a half century of research. Review of Educational Research, 88(4), 479–507. https://doi.org/10.3102/0034654317751919

Tennenhouse, L. G., Marrie, R. A., Bernstein, C. N., & Lix, L. M. (2020). Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease. Journal of Psychosomatic Research, 134, 110126. https://doi.org/10.1016/j.jpsychores.2020.110126

Thorsen, C., Yang Hansen, K., & Johansson, S. (2021). The mechanisms of interest and perseverance in predicting achievement among academically resilient and non-resilient students: Evidence from Swedish longitudinal data. British Journal of Educational Psychology, e12431. https://doi.org/10.1111/bjep.12431

Taylor, G., Jungert, T., Mageau, G. A., Schattke, K., Dedic, H., Rosenfield, S., & Koestner, R. (2014). A self-determination theory approach to predicting school achievement over time: The unique role of intrinsic motivation. Contemporary Educational Psychology, 39(4), 342–358. https://doi.org/10.1016/j.cedpsych.2014.08.002

UNICEF (2021). COVID-19 impacts on child poverty. Retrieved November 18, 2021, from https://www.unicef.org/social-policy/child-poverty/covid-19-socioeconomic-impacts

Vrugt, A., & Oort, F. J. (2008). Metacognition, achievement goals, study strategies and academic achievement: pathways to achievement. Metacognition and Learning, 3(2), 123–146. https://doi.org/10.1007/s11409-008-9022-4

Wang, M. C., Haertel, G. D., & Walberg, H. J. (1994). Educational resilience in inner cities. In M. C. Wang, & E. W. Gordon (Eds.), Educational resilience in Inner-City America: Challenges and Prospects (pp. 45–72). Erlbaum

Wang, K., & Kong, F. (2020). Linking trait mindfulness to life satisfaction in adolescents: The mediating role of resilience and self-esteem. Child Indicators Research, 13(1), 321–335. https://doi.org/10.1007/s12187-019-09698-4

Wang, F., King, R. B., & Leung, S. O. (2022). Why do East Asian students do so well in mathematics? A machine learning study. International Journal of Science and Mathematics Education, 1-21. https://doi.org/10.1007/s10763-022-10262-w

Wills, G., & Hofmeyr, H. (2019). Academic resilience in challenging contexts: Evidence from township and rural primary schools in South Africa. International Journal of Educational Research, 98, 192–205. https://doi.org/10.1016/j.ijer.2019.08.001

Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393

Yeung, S. S., King, R. B., Nalipay, M. J. N., & Cai, Y. (2022). Exploring the interplay between socioeconomic status and reading achievement: An expectancy‐value perspective. British Journal of Educational Psychology, e12495. https://doi.org/10.1111/bjep.12495