An International Comparison Study Exploring the Influential Variables Affecting Students’ Reading Literacy and Life Satisfaction

Authors

  • Hyewon Chung Chungnam National University
  • Jung-In Kim University of Colorado Denver
  • Eunjin (EJ) Jung University of San Francisco
  • Soyoung Park Chungnam National University

https://doi.org/10.17583/ijep.8924

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Abstract

The Program for International Student Assessment (PISA) aims to provide comparative data on 15-year-olds’ academic performance and well-being. The purpose of the current study is to explore and compare the variables that predict the reading literacy and life satisfaction of U.S. and South Korean students. The random forest algorithm, which is a machine learning approach, was applied to PISA 2018 data (4,677 U.S. students and 6,650 South Korean students) to explore and select the key variables among 305 variables that predict reading literacy and life satisfaction. In each random forest analysis, one for the U.S. and another for South Korea, 23 variables were derived as key variables in students’ reading literacy. In addition, 23 variables in the U.S. and 26 variables in South Korea were derived as important variables for students’ life satisfaction. The multilevel analysis revealed that various student-, teacher- or school-related key variables derived from the random forest were statistically related to either U.S. and/or South Korean students’ reading literacy and/or life satisfaction. The current study proposes to use a machine learning approach to examine international large-scale data for an international comparison. The implications of the current study and suggestions for future research are discussed.

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References

Barrett, N., & Toma, E. F. (2013). Reward or punishment? Class size and teacher quality. Economics of Education Review, 35, 41–52. https://doi.org/10.1016/j.econedurev.2013.03.001

Google Scholar Crossref

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Google Scholar Crossref

Breiman, L., Cutler, A., Liaw, A., & Wiener, M. (2018). The randomForest package. R Core Team. Retrieved from: https://CRAN.R-project.org/package=randomForest

Google Scholar Crossref

Chen, R. C., Dewi, C., Huang, S. W., & Caraka, R. E. (2020). Selecting critical features for data classification based on machine learning methods. Journal of Big Data, 7(1), 1-26. https://doi.org/10.1186/s40537-020-00327-4

Google Scholar Crossref

Cho, E. Y. N. (2019). A multilevel analysis of life satisfaction among secondary school students: Do school-level factors matter?. Children and Youth Services Review, 102, 231–242. https://doi.org/10.1016/j.childyouth.2019.05.002

Google Scholar Crossref

Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 113(31), 8664–8668. https://doi.org/10.1073/pnas.1608207113

Google Scholar Crossref

Dewi, C., & Chen, R. C. (2019). Random forest and support vector machine on features selection for regression analysis. International Journal of Innovative Computing, Information and Control, 15(6), 2027–2037. https://doi.org/10.24507/ijicic.15.06.2027

Google Scholar Crossref

Dong, X., & Hu, J. (2019). An exploration of impact factors influencing students’ reading literacy in Singapore with machine learning approaches. International Journal of English Linguistics, 9(5), 52–65. https://doi.org/10.5539/ijel.v9n5p52

Google Scholar Crossref

Elliot, A. J., Chirkov, V. I., Kim, Y., & Sheldon, K. M. (2001). A cross-cultural analysis of avoidance (relative to approach) personal goals. Psychological Science, 12(6), 505–510. https://doi.org/10.1111/1467-9280.00393

Google Scholar Crossref

Ertem, H. Y. (2020). Examination of Turkey’s PISA 2018 reading literacy scores within student-level and school-level variables. Participatory Educational Research, 8(1), 248–264. https://doi.org/10.17275/per.21.14.8.1

Google Scholar Crossref

Fuchs, T., & Wößmann, L. (2007). What accounts for international differences in student performance? A re-examination using PISA data. Empirical Economics, 32(2–3), 433–464. https://doi.org/10.1007/s00181-006-0087-0

Google Scholar Crossref

Gamazo, A., & Martínez-Abad, F. (2020). An exploration of factors linked to academic performance in PISA 2018 through data mining techniques. Frontiers in Psychology, 11(–), 1–17. https://doi.org/10.3389/fpsyg.2020.575167

Google Scholar Crossref

Gilman, R. (2001). The relationship between life satisfaction, social interest, and frequency of extracurricular activities among adolescent students. Journal of Youth and Adolescence, 30(6), 749–767. https://doi.org/10.1023/a:1012285729701

Google Scholar Crossref

Guess, P. E., & McCane-Bowling, S. J. (2016). Teacher support and life satisfaction: an investigation with urban, middle school students. Education and Urban Society, 48(1), 30–47. https://doi.org/10.1177/0013124513514604

Google Scholar Crossref

Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125(1), 1–13. https://doi.org/10.1016/j.compedu.2018.05.021

Google Scholar Crossref

Kang, D. J., & Yum, S. C. (2013). An analysis of school effects based on reading achievement data from PISA 2009. Journal of Research in Curriculum Instruction. 17(2), 323–345. https://doi.org/10.24231/rici.2013.17.2.323

Google Scholar Crossref

Kaplan, A., & Maehr, M. L. (1999). Achievement goals and student well-being. Contemporary Educational Psychology, 24(4), 330–358. https://doi.org/10.1006/ceps.1999.0993

Google Scholar Crossref

Kardefelt‐Winther, D., Rees, G., & Livingstone, S. (2020). Contextualizing the link between adolescents’ use of digital technology and their mental health: a multi‐country study of time spent online and life satisfaction. Journal of Child Psychology and Psychiatry, 61(8), 875–889. https://doi.org/10.1111/jcpp.13280

Google Scholar Crossref

Kim, H. S. (2012). The impact of ICT use on students' academic performance based on PISA 2009 Korean data. Asian Journal of Education, 13(1), 1–22. https://doi.org/10.15753/aje.2012.13.1.001

Google Scholar Crossref

Koyuncu, I. & Fırat, A. (2020). Investigating reading literacy in PISA 2018 assessment. International Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189

Google Scholar Crossref

Lee, I. W., & Ku, N. W. (2019). Analysis of PISA 2015 reading achievement characteristics of Korean students and influence of educational context variables. Journal of Reading Research, 50, 113–144. %20https:/doi.org/10.17095/JRR.2019.50.4

Google Scholar Crossref

Lim, H. J., & Jung, H. (2019). Factors related to digital reading achievement: A multilevel analysis using international large scale data. Computers & Education, 133, 82–93. https://doi.org/10.1016/j.compedu.2019.01.007

Google Scholar Crossref

Liu, Z., & Chen, H. (2017). A predictive performance comparison of machine learning models for judicial cases. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-6. https://doi.org/10.1109/SSCI.2017.8285436

Google Scholar Crossref

Muthén, L. K., & Muthén, B. O. (1998–2019). Mplus user’s guide (8th ed.). Los Angeles, CA: Muthén & Muthén.

Google Scholar Crossref

Nakhla, G. (2019). The relationship between fear of failure, academic motivation and student engagement in higher education: A general linear model (Unpublished doctoral dissertation). United Kingdom, Lancaster: Lancaster University.

Google Scholar Crossref

OECD. (2019a). PISA 2018 technical report. OECD Publishing.

Google Scholar Crossref

OECD. (2019b), PISA 2018 results (Volume I) what students know and can do. OECD Publishing.

Google Scholar Crossref

OECD. (2019c). PISA 2018 results (Volume III) what school life means for students' lives. OECD Publishing.

Google Scholar Crossref

Park, S. Y., & Chung, H. W. (2020). Classifying latent profiles in academic achievement and life satisfaction of adolescents. The Journal of Yeolin Education, 28(3), 47–72. https://doi.org/10.18230/tjye.2020.28.3.47

Google Scholar Crossref

Park, N., & Huebner, E. S. (2005). A cross-cultural study of the levels and correlates of life satisfaction among adolescents. Journal of Cross-Cultural Psychology, 36(4), 444-456. https://doi.org/10.1177/0022022105275961

Google Scholar Crossref

Perry, L. B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137–1162. https://doi.org/10.1177/016146811011200401

Google Scholar Crossref

Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models (2nd ed.). Newbury Park, CA: Sage.

Google Scholar Crossref

Reilly, D. (2012). Gender, culture, and sex-typed cognitive abilities. PLoS ONE, 7(7), 1–16. https://doi.org/10.1371/journal.pone.0039904.

Google Scholar Crossref

Rudolf, R. (2020). Life satisfaction among middle school students around the world cross-cultural evidence from PISA 2018. Retrieved April 28, 2021, from https://ssrn.com/abstract=3544001

Google Scholar Crossref

Shin, J., Lee, H., & Kim, Y. (2009). Student and school factors affecting mathematics achievement: International comparisons between Korea, Japan and the USA. School Psychology International, 30(5), 520–537. https://doi.org/10.1177/0143034309107070

Google Scholar Crossref

Shin, S. H., Slater, C. L., & Backhoff, E. (2013). Principal perceptions and student achievement in reading in Korea, Mexico, and the United States. Educational Administration Quarterly, 49(3), 489–527. https://doi.org/10.1177/0013161X12458796

Google Scholar Crossref

Sothe, C., De Almeida, C. M., Schimalski, M. B., La Rosa, L. E. C., Castro, J. D. B., Feitosa, R. Q., ... & Tommaselli, A. M. G. (2020). Comparative Performance of Convolutional Neural Network, Weighted and Conventional Support Vector Machine and Random Forest for Classifying Tree Species Using Hyperspectral and Photogrammetric Data. GIScience & Remote Sensing, 57(3), 369-394. https://doi.org/10.1080/15481603.2020.1712102

Google Scholar Crossref

Tang, Y. (2019). Immigration status and adolescent life satisfaction: an international comparative analysis based on PISA 2015. Journal of Happiness Studies, 20(5), 1499–1518. https://doi.org/10.1007/s10902-018-0010-3

Google Scholar Crossref

Topçu, M. S., Arıkan, S., & Erbilgin, E. (2015). Turkish students’ science performance and related factors in PISA 2006 and 2009. The Australian Educational Researcher, 42(1), 117–132. https://doi.org/10.1007/s13384-014-0157-9

Google Scholar Crossref

Won, S. J., & Han, S. (2010). Out-of-school activities and achievement among middle school students in the U.S. and South Korea. Journal of Advanced Academics, 21(4), 628–661. https://doi.org/10.1177/1932202X1002100404

Google Scholar Crossref

van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–68. https://doi.org/10.18637/jss.v045.i03

Google Scholar Crossref

Yoon, J., & Järvinen, T. (2016). Are model PISA pupils happy at school? Quality of school life of adolescents in Finland and Korea. Comparative Education, 52(4), 427–448. https://doi.org/10.1080/03050068.2016.1220128

Google Scholar Crossref

Xiao, Y., & Hu, J. (2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology, 10(–), 1–14. https://doi.org/10.3389/fpsyg.2019.01646

Google Scholar Crossref

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2022-10-24
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Chung, H., Kim, J.-I., Jung, E. (EJ), & Park, S. (2022). An International Comparison Study Exploring the Influential Variables Affecting Students’ Reading Literacy and Life Satisfaction. International Journal of Educational Psychology, 11(3), 261–292. https://doi.org/10.17583/ijep.8924

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