Abstract
Much of the existing educational research has focused on affluent Western societies. Despite comprising a broad swathe of the world population, less work has focused on lower middle-income economies such as the Philippines. Perhaps part of this reason is the lack of high-quality data in such contexts. PISA provides a potential solution to this problem. The Philippines participated in PISA in 2018 for the first time and scored lowest in reading out of 78 participating economies. This study was rooted in the bioecological model and simultaneously considered the roles of personal characteristics, proximal processes, and contextual factors to identify the most important predictors of reading achievement in the Philippine context. We used the 2018 PISA Philippine data, which consisted of 7233 15-year-old adolescent participants. Machine learning was used to select important variables. Results revealed the 26 top predictors of reading achievement out of 52 variables. The most critical predictors in order of importance included reading difficulty self-concept, socioeconomic status, grade repetition, school belonging, and fixed mindset among others. Hierarchical linear modelling broadly indicated similar results with small to moderate effect sizes. Theoretical and practical implications were discussed.
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References
Acar-Erdol, T., & Akin-Arikan, Ç. (2022). Gender gap in reading achievement: The mediating role of metacognitive strategies and reading-related attitudes. Social Psychology of Education, 25(2–3), 537–566. https://doi.org/10.1007/s11218-022-09692-9
Ahmadi, S., Hassani, M., & Ahmadi, F. (2020). Student- and school-level factors related to school belongingness among high school students. International Journal of Adolescence and Youth, 25(1), 741–752. https://doi.org/10.1080/02673843.2020.1730200
Allen, K. A., Fortune, K. C., & Arslan, G. (2021). Testing the social-ecological factors of school belonging in native-born, first-generation, and second-generation Australian students: A comparison study. Social Psychology of Education, 24(3), 835–856. https://doi.org/10.1007/s11218-021-09634-x
Anderson, J. O., Chiu, M.-H., & Yore, L. D. (2010). First cycle of PISA (2000–2006)-International perspectives on successes and challenges: Research and policy directions. International Journal of Science and Mathematics Education, 8(3), 373–388. https://doi.org/10.1007/s10763-010-9210-y
Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369–386. https://doi.org/10.1002/pits.20303
Araujo, L., Saltelli, A., & Schnepf, S. V. (2017). Do PISA data justify PISA-based education policy? International Journal of Comparative Education and Development, 19(1), 20–34. https://doi.org/10.1108/ijced-12-2016-0023
Archer, K. J., & Kimes, R. V. (2008). Empirical characterization of random forest variable importance measures. Computational Statistics and Data Analysis, 52(4), 2249–2260. https://doi.org/10.1016/j.csda.2007.08.015
Areepattamannil, S. (2014). International Note: What factors are associated with reading, mathematics, and science literacy of Indian adolescents? A Multilevel Examination. Journal of Adolescence, 37(4), 367–372. https://doi.org/10.1016/j.adolescence.2014.02.007
Artelt, C., & Schneider, W. (2015). Cross-country generalizability of the role of metacognitive knowledge in students’ strategy use and reading competence. Teachers College Record, 117(1), 1–32.
Ashiabi, G. S., & O’Neal, K. K. (2015). Child social development in context: An examination of some propositions in Bronfenbrenner’s Bioecological Theory. SAGE Open. https://doi.org/10.1177/2158244015590840
Auld, E., & Morris, P. (2016). PISA, policy and persuasion: Translating complex conditions into education ‘best practice.’ Comparative Education, 52(2), 202–229. https://doi.org/10.1080/03050068.2016.1143278
Bandura, A. (1994). Self-Efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of Human Behavior (Vol. 4, pp. 77–81). Academic Press. https://link.gale.com/apps/doc/CX1942000851/GVRL?u=hkioel&sid=bookmark-GVRL&xid=3ef4f3aa
Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology, 41(3), 586–598. https://doi.org/10.1037/0022-3514.41.3.586
Berger, N., & Archer, J. (2018). Qualitative insights into the relationship between socioeconomic status and students’ academic achievement goals. Social Psychology of Education, 21(4), 787–803. https://doi.org/10.1007/s11218-018-9442-1
Berkowitz, R., Moore, H., Astor, R. A., & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review of Educational Research, 87(2), 425–469. https://doi.org/10.3102/0034654316669821
Bernardo, A. B. (2022). Growth mindset and reading proficiency of ESL learners: Examining the role of students’ socioeconomic status using PISA 2018 Philippine data. European Journal of Psychology of Education. https://doi.org/10.1007/s10212-022-00629-6
Bernardo, A. B., Cordel, M. O., II., Irene Lucas, R. G., Michael Teves, J. M., Yap, S. A., & Chua, U. C. (2021). Using machine learning approaches to explore non-cognitive variables influencing reading proficiency in English among Filipino learners. Education Sciences. https://doi.org/10.3390/educsci11100628
Bernardo, A. B., Cordel, M. O., Lapinid, M. R. C., Teves, J. M. M., Yap, S. A., & Chua, U. C. (2022). Contrasting profiles of low-performing mathematics students in public and private schools in the Philippines: Insights from machine learning. Journal of Intelligence. https://doi.org/10.3390/jintelligence10030061
Bernardo, A. B., & Garcia, J. A. S. (2006). School improvement in a centralized educational system: The case of Philippine basic education. In J. C. Lee & M. Williams (Eds.), School Improvement: International Perspectives (pp. 227–224). Nova Science Publishers, Inc.
Bernardo, A. B. I. (2021). Socioeconomic status moderates the relationship between growth mindset and learning in mathematics and science: Evidence from PISA 2018 Philippine data. International Journal of School & Educational Psychology, 9, 208–222. https://doi.org/10.1080/21683603.2020.1832635
Bernardo, A. B. I. (2022). Growth mindset and reading proficiency of ESL learners: Examining the role of students’ socioeconomic status using PISA 2018 Philippine data. European Journal of Psychology of Education . https://doi.org/10.1007/s10212-022-00629-6
Bernardo, A. B., Limjap, A. A., Prudente, M. S., & Roleda, L. S. (2008). Students’ perceptions of science classes in the Philippines. Asia Pacific Education Review, 9(1), 80–81. https://doi.org/10.1007/bf03025828
Bernardo, J. (2020). DepEd says preparing for 2022 global learning assessment after dismal PH ranking. ABS-CBN News. https://news.abs-cbn.com/news/11/26/20/deped-says-preparing-for-2022-global-learning-assessment-after-dismal-ph-ranking
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7
Breiman, L. (2001a). Random forests. Machine Learning, 45, 5–32.
Breiman, L. (2001b). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–215. https://doi.org/10.1214/ss/1009213726
Breiman, L. (2003). Setting up, using, and understanding Random Forests V4.0. https://www.stat.berkeley.edu/~breiman/Using_random_forests_v4.0.pdf
Bronfenbrenner, U. (2005). Making human beings human: Bioecological perspectives on human development. sage.
Bronfenbrenner, U., & Morris, P. A. (2007). The Bioecological Model of Human Development. In Handbook of Child Psychology (Vol. 1, pp. 793–827). Wiley.
Brunner, M., Keller, U., Wenger, M., Fischbach, A., & Lüdtke, O. (2018). Between-school variation in students’ achievement, motivation, affect, and learning strategies: Results from 81 countries for planning group-randomized trials in education. Journal of Research on Educational Effectiveness, 11(3), 452–478. https://doi.org/10.1080/19345747.2017.1375584
Bzdok, D. (2017). Classical statistics and statistical learning in imaging neuroscience. Frontiers in Neuroscience, 11, 543. https://doi.org/10.3389/fnins.2017.00543
Cai, Y., King, R. B., Law, W., & McInerney, D. M. (2019). Which comes first? Modeling the relationships among future goals, metacognitive strategies and academic achievement using multilevel cross-lagged SEM. Learning and Individual Differences, 74, 101750. https://doi.org/10.1016/j.lindif.2019.06.004
Chen, F., Sakyi, A., & Cui, Y. (2021). Linking student, home, and school factors to reading achievement: The mediating role of reading self-efficacy. Educational Psychology. https://doi.org/10.1080/01443410.2021.1953445
Chiu, M. M., & Chow, B. W. Y. (2015). Classmate characteristics and student achievement in 33 countries: Classmates’ past achievement, family socioeconomic status, educational resources, and attitudes toward reading. Journal of Educational Psychology, 107(1), 152–169. https://doi.org/10.1037/a0036897
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., & McBride-Chang, C. (2006). Gender, context, and reading: A comparison of students in 43 countries. Scientific Studies of Reading, 10(4), 331–362. https://doi.org/10.1207/s1532799xssr1004_1
Cho, E., Toste, J. R., Lee, M., & Ju, U. (2019). Motivational predictors of struggling readers’ reading comprehension: The effects of mindset, achievement goals, and engagement. Reading and Writing, 32(5), 1219–1242. https://doi.org/10.1007/s11145-018-9908-8
Choi, N. (2005). Self-efficacy and self-concept as predictors of college students’ academic performance. Psychology in the Schools, 42(2), 197–205. https://doi.org/10.1002/pits.20048
Chung, H., Park, S., Kim, J.-I., & Kim, A. (2021). Exploring variables affecting adolescents’ reading literacy and life satisfaction: PISA 2018 international comparison of Korea and Finland. Journal of Curriculum and Evaluation, 24(1), 123–152. https://doi.org/10.29221/jce.2021.24.1.123
Cordon, J. M., & Polong, J. D. B. (2020). Behind the science literacy of Filipino students at PISA 2018: A Case study in the Philippines’ educational system. Integrated Science Education Journal, 1(2), 72–78. https://doi.org/10.37251/isej.v1i2.59
Craven, R. G., Marsh, H. W., & Debus, R. L. (1991). Effects of internally focused feedback and attributional feedback on enhancement of academic self-concept. Journal of Educational Psychology, 83(1), 17–27.
Daily, S. M., Mann, M. J., Kristjansson, A. L., Smith, M. L., & Zullig, K. J. (2019). School climate and academic achievement in middle and high school students. Journal of School Health, 89(3), 173–180. https://doi.org/10.1111/josh.12726
Davis, J. R., & Warner, N. (2018). Schools matter: The positive relationship between New York City High Schools’ student academic progress and school climate. Urban Education, 53(8), 959–980. https://doi.org/10.1177/0042085915613544
de Guzman, A. B. (2007). Chronicling decentralization initiatives in the Philippine basic education sector. International Journal of Educational Development, 27(6), 613–624. https://doi.org/10.1016/j.ijedudev.2006.06.014
Delprato, M., Frola, A., & Antequera, G. (2022). Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach. International Journal of Educational Development. https://doi.org/10.1016/j.ijedudev.2022.102631
Department of Education. (2012a). DepEd child protection policy. https://www.deped.gov.ph/2012a/05/14/do-40-s-2012a-deped-child-protection-policy/
Department of Education. (2012b). Guidelines on the assessment and rating of learning outcomes under the K to 12 basic education curriculum. In The Official Gazette of the republic of the Philippines (DepEd Order No. 73 s. 2012b). https://www.officialgazette.gov.ph/2012b/09/05/deped-order-no-73-s-2012b/
Department of Education. (2019). PISA 2018 National Report of the Philippines. Department of Education. https://www.deped.gov.ph/wp-content/uploads/2019/12/PISA-2018-Philippine-National-Report.pdf
Department of Education. (2021). DepEd posts 4% increase in enrollment for basic education in SY 2021–2022. https://www.deped.gov.ph/category/media/press-releases/
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. https://doi.org/10.5539/ijel.v9n5p52
Elliott, J., Stankov, L., Lee, J., & Beckmann, J. F. (2019). What did PISA and TIMSS ever do for us?: The potential of large scale datasets for understanding and improving educational practice. Comparative Education, 55(1), 133–155. https://doi.org/10.1080/03050068.2018.1545386
Ferla, J., Valcke, M., & Cai, Y. (2009). Academic self-efficacy and academic self-concept: Reconsidering structural relationships. Learning and Individual Differences, 19(4), 499–505. https://doi.org/10.1016/j.lindif.2009.05.004
Gabriel, F., Signolet, J., & Westwell, M. (2018). A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy. International Journal of Research and Method in Education, 41(3), 306–327. https://doi.org/10.1080/1743727X.2017.1301916
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, 3365. https://doi.org/10.3389/FPSYG.2020.575167
Ganimian, A. J., & Murnane, R. J. (2016). Improving education in developing countries: Lessons from rigorous impact evaluations. Review of Educational Research, 86(3), 719–755. https://doi.org/10.3102/0034654315627499
Giambona, F., & Porcu, M. (2015). Student background determinants of reading achievement in Italy. A quantile regression analysis. International Journal of Educational Development, 44, 95–107. https://doi.org/10.1016/J.IJEDUDEV.2015.07.005
Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. https://doi.org/10.1016/j.paid.2016.06.069
Glewwe, P., & Muralidharan, K. (2016). Improving education outcomes in developing countries: Evidence, knowledge gaps, and policy implications. In Handbook of the Economics of Education (Vol. 5, pp. 653–743). Elsevier. https://doi.org/10.1016/B978-0-444-63459-7.00010-5
Gregorutti, B., Michel, B., & Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27(3), 659–678. https://doi.org/10.1007/s11222-016-9646-1
Grund, S., Lüdtke, O., & Robitzsch, A. (2020). On the treatment of missing data in background questionnaires in educational large-scale assessments: An evaluation of different procedures. Journal of Educational and Behavioral Statistics 46(4), 430–465. https://doi.org/10.3102/1076998620959058
Guo, J., Marsh, H. W., Parker, P. D., Morin, A. J. S., & Yeung, A. S. (2015). Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations: A multi-cohort study. Learning and Individual Differences, 37, 161–168. https://doi.org/10.1016/j.lindif.2015.01.008
Guthrie, J. T., & Klauda, S. L. (2015). Engagement and motivational processes in reading. In Handbook of individual differences in reading: Reader, text and context (pp. 41–53). Routledge.
Habók, A., Magyar, A., Németh, M. B., & Csapó, B. (2020). Motivation and self-related beliefs as predictors of academic achievement in reading and mathematics: Structural equation models of longitudinal data. International Journal of Educational Research, 103, 101634. https://doi.org/10.1016/j.ijer.2020.101634
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analysis relating to achievement. Routledge. https://doi-org.ezproxy.eduhk.hk/https://doi.org/10.4324/9780203887332
Hattie, J. (2023). Visible Learning: The Sequel: A Synthesis of Over 2,100 Meta-Analyses Relating to Achievement. Taylor & Francis.
Haw, J. Y., & King, R. B. (2022). Need-supportive teaching is associated with reading achievement via intrinsic motivation across eight cultures. Learning and Individual Differences, 97, 102161. https://doi.org/10.1016/j.lindif.2022.102161
Haw, J. Y., King, R. B., Eos, J., & Trinidad, R. (2021). Need supportive teaching is associated with greater reading achievement: What the Philippines can learn from PISA 2018. International Journal of Educational Research, 110, 101864. https://doi.org/10.1016/j.ijer.2021.101864
Hayes, N., O’Toole, L., & Halpenny, A. M. (2017). The bioecological model of human development. Routledge.
He, J., Barrera-Pedemonte, F., & Buchholz, J. (2019). Cross-cultural comparability of noncognitive constructs in TIMSS and PISA. Assessment in Education: Principles, Policy & Practice, 26(4), 369–385. https://doi.org/10.1080/0969594X.2018.1469467
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29. https://doi.org/10.1038/466029a
Hong, M., Jacobucci, R., & Lubke, G. (2020). Deductive data mining. Psychological Methods, 25(6), 691–707. https://doi.org/10.1037/met0000252
Hopfenbeck, T. N., Lenkeit, J., el Masri, Y., Cantrell, K., Ryan, J., & Baird, J. A. (2018). Lessons learned from PISA: A systematic review of peer-reviewed articles on the programme for international student assessment. Scandinavian Journal of Educational Research, 62(3), 333–353. https://doi.org/10.1080/00313831.2016.1258726
Hopson, L. M., & Lee, E. (2011). Mitigating the effect of family poverty on academic and behavioral outcomes: The role of school climate in middle and high school. Children and Youth Services Review, 33(11), 2221–2229. https://doi.org/10.1016/j.childyouth.2011.07.006
Hornery, S., Seaton, M., Tracey, D., Craven, R. G., & Yeung, A. S. (2014). Enhancing reading skills and reading self-concept of children with reading difficulties: Adopting a dual approach intervention. Australian Journal of Educational & Developmental Psychology, 14, 131–143.
Hu, J., & Yu, R. (2021). The effects of ICT-based social media on adolescents’ digital reading performance: A longitudinal study of PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Computers and Education, 175, 104342. https://doi.org/10.1016/j.compedu.2021.104342
Huang, F. L. (2010). The role of socioeconomic status and school quality in the Philippines: Revisiting the Heyneman-Loxley effect. International Journal of Educational Development, 30(3), 288–296. https://doi.org/10.1016/j.ijedudev.2009.10.001
Jacobucci, R., & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science, 15(3), 809–816. https://doi.org/10.1177/1745691620902467
Jaeger, E. L. (2017). Negotiating complexity: A bioecological systems perspective on literacy development. Human Development, 59(4), 163–187. https://doi.org/10.1159/000448743
Jakubowski, M., & Jakubowski, M. (2021). Poland: Polish education reforms and evidence from international assessments. In N. Crato (Ed.), Improving a Country’s Education: PISA 2018 Results in 10 Countries (pp. 137–158). Springer, Cham. https://doi.org/10.1007/978-3-030-59031-4_7
King, R. B., & McInerney, D. M. (2014). Culture's consequences on student motivation: Capturing cross-cultural universality and variability through personal investment theory. Educational Psychologist, 49(3), 175–198. https://doi.org/10.1080/00461520.2014.926813
King, R. B., & McInerney, D. M. (2016). Do goals lead to outcomes or can it be the other way around?: Causal ordering of mastery goals, metacognitive strategies, and achievement. British Journal of Educational Psychology, 86(2), 296–312. https://doi.org/10.1111/bjep.12107
King, R. B., McInerney, D. M., & Pitliya, R. J. (2018). Envisioning a culturally imaginative educational psychology. Educational Psychology Review, 30(3), 1031–1065. https://doi.org/10.1007/s10648-018-9440-z
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. https://doi.org/10.1007/s11218-021-09616-z
King, R. B., Chiu, M. M., & Du, H. (2022). Greater income inequality, lower school belonging: Multilevel and cross-temporal analyses of 65 countries. Journal of Educational Psychology, 114(5), 1101–1120. https://doi.org/10.1037/edu0000736
King, R. B. (2022). Sociocultural and ecological perspectives on achievement motivation. Asian Journal of Social Psychology, 25(3), 433–448. https://doi.org/10.1111/ajsp.12507
Lazowski, R. A., & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational Research, 86(2), 602–640.
Lee, J. (2014). Universal factors of student achievement in high-performing eastern and western countries. Journal of Educational Psychology, 106(2), 364–374. https://doi.org/10.1037/a0035609
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(3), 185–202. https://doi.org/10.1080/00461520.2010.493471
Lee, J., & Stankov, L. (2013). Higher-order structure of noncognitive constructs and prediction of PISA 2003 mathematics achievement. Learning and Individual Differences, 26, 119–130. https://doi.org/10.1016/j.lindif.2013.05.004
Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50–64. https://doi.org/10.1016/j.lindif.2018.05.009
Lezhnina, O., & Kismihók, G. (2022). Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA. International Journal of Research and Method in Education, 45(2), 180–199. https://doi.org/10.1080/1743727X.2021.1963226
Liaw, A., & Wiener, M. (2022). Package “randomForest”: Breiman and Cutler’s random forests for classification and regression (4.7–1). The Comprehensive R Archive Network . https://www.stat.berkeley.edu/~breiman/RandomForests/
Liu, J., Peng, P., & Luo, L. (2020). The relation between family socioeconomic status and academic achievement in China: A meta-analysis. In Educational Psychology Review (Vol. 32, Issue 1, pp. 49–76). Springer. https://doi.org/10.1007/s10648-019-09494-0
Long, J. A. (2021). Jtools: Analysis and presentation of social scientific data (2.2.0). The Comprehensive R Archive Network. https://jtools.jacob-long.com/
Lounkaew, K. (2013). Explaining urban-rural differences in educational achievement in Thailand: Evidence from PISA literacy data. Economics of Education Review, 37, 213–225. https://doi.org/10.1016/j.econedurev.2013.09.003
Lu, M., & Ishwaran, H. (2018). A prediction-based alternative to P values in regression models. The Journal of Thoracic and Cardiovascular Surgery, 155(3), 1130-1136.e4. https://doi.org/10.1016/J.JTCVS.2017.08.056
Lumley, T. (2021). Analysis of complex survey samples (4.1–1). The Comprehensive R Archive Network. http://r-survey.r-forge.r-project.org/survey/
Ma, L., Luo, H., & Xiao, L. (2021). Perceived teacher support, self-concept, enjoyment and achievement in reading: A multilevel mediation model based on PISA 2018. Learning and Individual Differences, 85, 101947. https://doi.org/10.1016/j.lindif.2020.101947
Marôco, J. (2021). Portugal: The PISA Effects on Education. In N. Crato (Ed.), Improving a country’s education: PISA 2018 results in 10 countries (pp. 159–174). Springer. https://doi.org/10.1007/978-3-030-59031-4_8
Marsh, H. W. (1984). Relations among dimensions of self-attribution, dimensions of self-concept, and academic achievements. Journal of Educational Psychology, 76(6), 1291–1308. https://doi.org/10.1037/0022-0663.76.6.1291
Marsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59–77. https://doi.org/10.1348/000709910X503501
Martínez-Abad, F., Gamazo, A., & Rodríguez-Conde, M. J. (2020). Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment. Studies in Educational Evaluation, 66, 100875. https://doi.org/10.1016/j.stueduc.2020.100875
Martins, L., & Veiga, P. (2010). Do inequalities in parents’ education play an important role in PISA students’ mathematics achievement test score disparities? Economics of Education Review, 29(6), 1016–1033. https://doi.org/10.1016/j.econedurev.2010.05.001
McInerney, D. M., Cheng, R. W. Y., Mok, M. M. C., & Lam, A. K. H. (2012). Academic self-concept and learning strategies: Direction of effect on student academic achievement. Journal of Advanced Academics, 23(3), 249–269. https://doi.org/10.1177/1932202X12451020
Meng, L., Muñoz, M., King Hess, K., & Liu, S. (2016). Effective teaching factors and student reading strategies as predictors of student achievement in PISA 2009: The case of China and the United States. Educational Review, 69(1), 68–84. https://doi.org/10.1080/00131911.2016.1155537
Miyamoto, A., Pfost, M., & Artelt, C. (2019). The relationship between intrinsic motivation and reading comprehension: Mediating effects of reading amount and metacognitive knowledge of strategy use. Scientific Studies of Reading, 23(6), 445–460. https://doi.org/10.1080/10888438.2019.1602836
Morsy, L., Khavenson, T., & Carnoy, M. (2018). How international tests fail to inform policy: The unsolved mystery of Australia’s steady decline in PISA scores. International Journal of Educational Development, 60, 60–79. https://doi.org/10.1016/J.IJEDUDEV.2017.10.018
Musso, M. F., Cascallar, E. C., Bostani, N., & Crawford, M. (2020). Identifying reliable predictors of educational outcomes through machine-learning predictive modeling. Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.00104
OECD. (2009). PISA data analysis manual SPSS second edition. OECD Publishing. https://doi.org/10.1787/9789264056275-en
OECD. (2018). PISA 2018 technical report. https://www.oecd.org/pisa/data/pisa2018technicalreport/
OECD. (2019a). PISA 2018 Insights and interpretations. OECD Publishing. https://doi.org/10.1787/b25efab8-en
OECD. (2019b). PISA 2018 Assessment and analytical framework. OECD Publishing. https://doi.org/10.1787/b25efab8-en
OECD. (2020). Teachers’ support and teaching practices. In PISA 2018 Results (Volume III): What school life means for students’ lives. OECD Publishing. https://doi.org/10.1787/617837e6-en
Parker, P. D., Marsh, H. W., Ciarrochi, J., Marshall, S., & Abduljabbar, A. S. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology, 34(1), 29–48. https://doi.org/10.1080/01443410.2013.797339
Patel, H., & Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computer Applications, 95(9), 6–8. https://doi.org/10.5120/16620-6472
Perera, L. D. H., & Asadullah, M. N. (2019). Mind the gap: What explains Malaysia’s underperformance in Pisa? International Journal of Educational Development, 65, 254–263. https://doi.org/10.1016/J.IJEDUDEV.2018.08.010
Pitsia, V. (2022). Examining high achievement in mathematics and science among post-primary students in Ireland: A multilevel binary logistic regression analysis of PISA data. Large-Scale Assessments in Education. https://doi.org/10.1186/s40536-022-00131-x
Pons, X. (2012). Going beyond the “PISA Shock” discourse: An analysis of the cognitive reception of PISA in six European countries, 2001–2008. European Educational Research Journal, 11(2), 206–226. https://doi.org/10.2304/eerj.2012.11.2.206
Pons, X. (2017). Fifteen years of research on PISA effects on education governance: A critical review. European Journal of Education, 52(2), 131–144. https://doi.org/10.1111/ejed.12213
Reparaz, C., & Sotés-Elizalde, M. A. (2019). Parental involvement in schools in Spain and Germany: Evidence from PISA 2015. International Journal of Educational Research, 93, 33–52. https://doi.org/10.1016/j.ijer.2018.10.001
Retelsdorf, J., Köller, O., & Möller, J. (2014). Reading achievement and reading self-concept - Testing the reciprocal effects model. Learning and Instruction, 29, 21–30. https://doi.org/10.1016/j.learninstruc.2013.07.004
Rhew, E., Piro, J. S., Goolkasian, P., & Cosentino, P. (2018). The effects of a growth mindset on self-efficacy and motivation. Cogent Education, 5(1), 1–16. https://doi.org/10.1080/2331186X.2018.1492337
Robitzsch, A., & Oberwimmer, K. (2019). Package “BIFIEsurvey”: Tools for survey statistics in educational assessment (3.3–12). The Comprehensive R Archive Network. https://www.bifie.at/bildungsforschung/forschungsdatenbibliothek
Roebers, C. M., Cimeli, P., Röthlisberger, M., & Neuenschwander, R. (2012). Executive functioning, metacognition, and self-perceived competence in elementary school children: An explorative study on their interrelations and their role for school achievement. Metacognition and Learning, 7(3), 151–173. https://doi.org/10.1007/s11409-012-9089-9
Rosa, E. M., & Tudge, J. (2013). Urie Bronfenbrenner’s Theory of Human Development: Its evolution from ecology to bioecology. Journal of Family Theory & Review, 5(4), 243–258. https://doi.org/10.1111/jftr.12022
Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational Researcher, 39(2), 142–151. https://doi.org/10.3102/0013189X10363170
Ryo, M., & Rillig, M. C. (2017). Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere. https://doi.org/10.1002/ecs2.1976
San Antonio, D. M., Morales, N. S., & Moral, L. S. (2011). Module-based professional development for teachers: A cost-effective Philippine experiment of the Philippines. Teacher Development, 15(2), 157–169. https://doi.org/10.1080/13664530.2011.571496
San Juan, R. (2019). DepEd welcomes PISA results, recognizes “gaps” in education quality. Philstar Global. https://www.philstar.com/headlines/2019/12/04/1974229/deped-welcomes-pisa-results-recognizes-gaps-education-quality
Shin, H. W., & So, Y. (2018). The moderating role of socioeconomic status on motivation of adolescents’ foreign language learning strategy use. System, 73, 71–79. https://doi.org/10.1016/j.system.2017.11.007
Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453.
Skinner, E. A., Rickert, N. P., Vollet, J. W., & Kindermann, T. A. (2022) The complex social ecology of academic development: A bioecological framework and illustration examining the collective effects of parents teachers and peers on student engagement. Educational Psychologist, 57(2), 87–113. https://doi.org/10.1080/00461520.2022.2038603
Tan, C. Y., & Hew, K. F. (2018). The impact of digital divides on student mathematics achievement in Confucian heritage cultures: A critical examination using PISA 2012 data. International Journal of Science and Mathematics Education, 17(6), 1213–1232. https://doi.org/10.1007/S10763-018-9917-8
Thien, L. M., Darmawan, I. G. N., & Ong, M. Y. (2015). Affective characteristics and mathematics performance in Indonesia, Malaysia, and Thailand: What can PISA 2012 data tell us? Large-Scale Assessments in Education. https://doi.org/10.1186/s40536-015-0013-z
Tire, G. (2021). Estonia: A positive PISA experience. In N. Crato (Ed.), Improving a Country’s Education: PISA 2018 Results in 10 Countries (pp. 101–120). Springer, Cham. https://doi.org/10.1007/978-3-030-59031-4_5
Trinidad, J. E. (2020). Material resources, school climate, and achievement variations in the Philippines: Insights from PISA 2018. International Journal of Educational Development, 75, 102174. https://doi.org/10.1016/j.ijedudev.2020.102174
Trinidad, J. E., & King, R. B. (2022). Ability grouping predicts inequality not achievement gains in Philippine schools: Findings from PISA 2018. Educational Research for Policy and Practice, 21(2), 305–322. https://doi.org/10.1007/s10671-021-09307-8
Tudge, J. R. H., Mokroca, I., Hatfield, B. E., & Karnik, R. B. (2009). Uses and misuses of Bronfenbrenner’s bioecological theory of human development. Journal of Family Theory & Review, 1(4), 198–210. https://doi.org/10.1111/j.1756-2589.2009.00026.x
Tudge, J. R. H., Payir, A., Merçon-Vargas, E., Cao, H., Liang, Y., Li, J., & O’Brien, L. (2016). Still misused after all these years? A reevaluation of the uses of Bronfenbrenner’s bioecological theory of human development. Journal of Family Theory and Review, 8(4), 427–445. https://doi.org/10.1111/jftr.12165
Unrau, N. J., Rueda, R., Son, E., Polanin, J. R., Lundeen, R. J., & Muraszewski, A. K. (2018). Can reading self-efficacy be modified? A meta-analysis of the impact of interventions on reading self-efficacy. Review of Educational Research, 88(2), 167–204. https://doi.org/10.3102/0034654317743199
van Buuren, S., & Groothuis-Oudshorn, K. (2011). MICE: Multivariate imputation by chained equation. Journal of Statistical Software, 45, 1–67. https://doi.org/10.18637/jss.v045.i03
Voyer, D., & Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174–1204. https://doi.org/10.1037/a0036620
Wang, F., King, R. B., & Leung, S. O. (2022). Beating the odds: Identifying the top predictors of resilience among Hong Kong students. Child Indicators Research, 15(5), 1921–1944. https://doi.org/10.1007/s12187-022-09939-z
Wang, F., King, R. B., & Leung, S. O. (2023a). Why do East Asian students do so well in mathematics? A machine learning study. International Journal of Science and Mathematics Education, 21(3), 691–711. https://doi.org/10.1007/s10763-022-10262-w
Wang. H., King. R. B., & McInerney, D. M. (2023b). Ability grouping and student performance: A longitudinal investigation of teacher support as a mediator and moderator. Research Papers in Education 38(2), 121–142. https://doi.org/10.1080/02671522.2021.1961293
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
World Bank Group. (2016). Developing a proficient and motivated teacher workforce in the Philippines. In Philippines Education Note (Issue 3). World Bank. https://doi.org/10.1596/24746
World Bank Group. (2020). Philippines - Programme for International Student Assessment 2018 Country Report (Issue June). http://documents.worldbank.org/curated/en/184251593328815913/Main-Report
Wright, M. N., Wager, S., & Probst, P. (2021). Ranger: A fast implementation of random forests (0.13.1). The Comprehensive R Archive Network. https://doi.org/10.1080/10618600.2014.983641
Yan, Z., King, R. B., & Haw, J. Y. (2021). Formative assessment, growth mindset, and achievement: Examining their relations in the East and the West. Assessment in Education: Principles, Policy and Practice, 28(5–6), 676–702. https://doi.org/10.1080/0969594X.2021.1988510
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. 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, 92(3), 1196–1214. https://doi.org/10.1111/bjep.12495
Yu, C. H., Wu, S. F., & Mangan, C. (2015). Identifying crucial and malleable factors of successful science learning from the 2012 PISA. In M. Swe Khine (Ed.), Science Education in East Asia: Pedagogical Innovations and Research-informed Practices (pp. 567–590). Springer. https://doi.org/10.1007/978-3-319-16390-1
Yu, S., & Zhao, X. (2021). The negative impact of bullying victimization on academic literacy and social integration: Evidence from 51 countries in PISA. Social Sciences & Humanities Open, 4(1), 100151. https://doi.org/10.1016/j.ssaho.2021.100151
Zhao, Y. (2020). Two decades of havoc: A synthesis of criticism against PISA. Journal of Educational Change, 21(2), 245–266. https://doi.org/10.1007/s10833-019-09367-x
Zuze, T. L., & Reddy, V. (2014). School resources and the gender reading literacy gap in South African schools. International Journal of Educational Development, 36, 100–107. https://doi.org/10.1016/J.IJEDUDEV.2013.10.002
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Haw, J.Y., King, R.B. Understanding Filipino students’ achievement in PISA: The roles of personal characteristics, proximal processes, and social contexts. Soc Psychol Educ 26, 1089–1126 (2023). https://doi.org/10.1007/s11218-023-09773-3
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DOI: https://doi.org/10.1007/s11218-023-09773-3