Abstract
This study examined the combined effects of teachers’ instructional practices and students’ reading-related affective engagement on predicting the high and low levels of elementary reading literacy from a linguistically and culturally comparative perspective. Data were based on 9748 students from 4 English-speaking and 3 Chinese-speaking education systems participating in the Progress in International Reading Literacy Study 2016. A mixed theory-based and data-driven approach was adopted. Four machine learning algorithms, specifically logistic regression, support vector machine, decision tree, and extreme gradient boosting, were simultaneously used to classify and predict high- and low-proficiency readers and to identify the most important factors for the ability separation. The findings showed that for both system groups, those factors together were sufficiently powerful to discriminate the readers and that the affective constructs, particularly students’ self-concepts, played a predominant role. The Chinese-speaking systems, compared with their English counterparts, applied more effective pedagogies to nurture sophisticated readers.
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References
Alivernini, F. (2013). An exploration of the gap between highest and lowest ability readers across 20 countries. Educational Studies, 39, 399–417. https://doi.org/10.1080/03055698.2013.767187.
Alivernini, F., & Manganelli, S. (2015). Country, school and students factors associated with extreme levels of science literacy across 25 countries. International Journal of Science Education, 37, 1992–2012. https://doi.org/10.1080/09500693.2015.1060648.
Başol, G., & Johanson, G. (2009). Effectiveness of frequent testing over achievement: A meta analysis study. Journal of Human Sciences, 6, 99–121.
Chen, T., & Guestrin, C. (2016). XGboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, USA (pp. 785–794). New York, NY: ACM. https://doi.org/10.1145/2939672.2939785.
Chen, J., Zhang, Y., Wei, Y., & Hu, J. (2019). Discrimination of the contextual features of top performers in scientific literacy using a machine learning approach. Research in Science Education. Advance online publication. https://doi.org/10.1007/s11165-019-9835-y. Retrieved from https://rdcu.be/btN56
Cheung, W., Tse, S., Lam, J., & Loh, K. (2009). Progress in International Reading Literacy Study 2006 (PIRLS): Pedagogical correlates of fourth-grade students in Hong Kong. Journal of Research in Reading, 32, 293–308. https://doi.org/10.1111/j.1467-9817.2009.01395.x.
Cho, M., & Yoo, J. (2017). Exploring online students’ self-regulated learning with self-reported surveys and log files: A data mining approach. Interactive Learning Environments, 25, 970–982. https://doi.org/10.1080/10494820.2016.1232278.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1023/A:1022627411411.
Farzan, I. A. (2018). The interplay of culture, motivation, and self: An investigation of math achievement gap between middle school students in the us and those in the top-performing countries in east Asia (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 394127191).
Fraser, B., Walberg, H., Welch, W., & Hattie, J. (1987). Syntheses of educational productivity research. International Journal of Educational Research, 11, 147–252. https://doi.org/10.1016/0883-0355(87)90035-8.
Fredricks, J., Blumenfeld, P., & Paris, A. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. https://doi.org/10.3102/00346543074001059.
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 & Method in Education, 41, 306–327. https://doi.org/10.1080/1743727x.2017.1301916.
Gorostiaga, A., & Rojo-Álvarez, J. (2016). On the use of conventional and statistical-learning techniques for the analysis of PISA results in Spain. Neurocomputing, 171, 625–637. https://doi.org/10.1016/j.neucom.2015.07.001.
Guo, J., Marsh, H., Parker, P., & Dicke, T. (2018). Cross-cultural generalizability of social and dimensional comparison effects on reading, math, and science self-concepts for primary school students using the combined PIRLS and TIMSS data. Learning and Instruction, 58, 210–219. https://doi.org/10.1016/j.learninstruc.2018.07.007.
Guthrie, J., Schafer, W., Von Secker, C., & Alban, T. (2000). Contributions of instructional practices to reading achievement in a statewide improvement program. The Journal of Educational Research, 93, 211–225. https://doi.org/10.1080/00220670009598710.
Hebbecker, K., Förster, N., & Souvignier, E. (2019). Reciprocal effects between reading achievement and intrinsic and extrinsic reading motivation. Scientific Studies of Reading, 23, 419–436. https://doi.org/10.1080/10888438.2019.1598413.
Lafontaine, D., Dupont, V., & Schillings, P. (2018). Does reading literacy instruction vary according to language or culture? Similarities and differences in English-, German-and French-speaking education systems in PIRLS 2016. IEA: Compass Briefs in Education, 4, 1–12.
Larose, D., & Larose, C. (2014). Discovering knowledge in data: An introduction to data mining. Hoboken, NJ: Wiley.
Liou, P. (2017). Profiles of adolescents’ motivational beliefs in science learning and science achievement in 26 countries: Results from TIMSS 2011 data. International Journal of Educational Research, 81, 83–96. https://doi.org/10.1016/j.ijer.2016.11.006.
Loh, E., & Tse, S. (2009). The relationship between motivation to read Chinese and English and its impact on the Chinese and English reading performance of Chinese students. Chinese Education & Society, 42, 66–90. https://doi.org/10.2753/ced1061-1932420304.
McGeown, S., Johnston, R., Walker, J., Howatson, K., Stockburn, A., & Dufton, P. (2015). The relationship between young children’s enjoyment of learning to read, reading attitudes, confidence and attainment. Educational Research, 57, 389–402. https://doi.org/10.1080/00131881.2015.1091234.
McLaughlin, M. (2012). Reading comprehension: What every teacher needs to know. The Reading Teacher, 65, 432–440. https://doi.org/10.1002/trtr.01064.
Müller, A., & Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. Sebastopol, CA: O’Reilly Media.
Mullis, I., & Martin, M. (2015). PIRLS 2016 assessment framework (2nd ed.). Retrieved from http://timssandpirls.bc.edu/pirls2016/framework.html/.
Mullis, I., Martin, M., Foy, P., & Hooper, M. (2017). PIRLS 2016 international results in reading. Retrieved from http://timssandpirls.bc.edu/pirls2016/international-results/.
Park, Y., Brownell, M., Bettini, E., & Benedict, A. (2017). Multiple dimensions of instructional effectiveness in reading: A review of classroom observation studies and implications for special education classrooms. Exceptionality, 27, 1–17. https://doi.org/10.1080/09362835.2017.1283628.
Petscher, Y., & Logan, J. (2014). Quantile regression in the study of developmental sciences. Child Development, 85, 861–881. https://doi.org/10.1111/cdev.12190.
Pikulski, J., & Chard, D. (2005). Fluency: Bridge between decoding and reading comprehension. The Reading Teacher, 58, 510–519. https://doi.org/10.1598/rt.58.6.2.
Qiao, X., & Jiao, H. (2018). Data mining techniques in analyzing process data: A didactic. Frontiers in Psychology, 9, 2231. https://doi.org/10.3389/fpsyg.2018.02231.
Schütte, K. (2015). Science self-concept and valuing science: A cross-cultural analysis of their relation among students from Western and East Asian countries. Social Psychology of Education, 18, 635–652. https://doi.org/10.1007/s11218-015-9311-0.
Stevens, E., Park, S., & Vaughn, S. (2019). A review of summarizing and main idea interventions for struggling readers in grades 3 through 12: 1978–2016. Remedial and Special Education, 40, 131–149. https://doi.org/10.1177/0741932517749940.
Susperreguy, M., Davis-Kean, P., Duckworth, K., & Chen, M. (2017). Self-concept predicts academic achievement across levels of the achievement distribution: Domain specificity for math and reading. Child Development, 89, 2196–2214. https://doi.org/10.1111/cdev.12924.
Top**, K. (2015). Fiction and non-fiction reading and comprehension in preferred books. Reading Psychology, 36, 350–387. https://doi.org/10.1080/02702711.2013.865692.
Troyer, M., Kim, J. S., Hale, E., Wantchekon, K. A., & Armstrong, C. (2019). Relations among intrinsic and extrinsic reading motivation, reading amount, and comprehension: A conceptual replication. Reading and Writing: An Interdisciplinary Journal, 32, 1197–1218. https://doi.org/10.1007/s11145-018-9907-9.
Tse, S., Hui, S., Ng, H., & Lam, W. (2015). The reading attainment of Hong Kong Primary Four students: A study of literacy achievements and its implications. Education Journal, 43, 35–58.
Tse, S., Lam, J., Lam, R., Loh, K., & Westwood, P. (2007). Pedagogical correlates of reading comprehension in English and Chinese. L1 Educational Studies in Language and Literature, 7, 71–91. https://doi.org/10.17239/l1esll-2007.07.02.06.
Tse, S., & **ao, X. (2014). Differential influences of affective factors and contextual factors on high-proficiency readers and low-proficiency readers: A multilevel analysis of PIRLS data from Hong Kong. Large-Scale Assessments in Education, 2, 6. https://doi.org/10.1186/s40536-014-0006-3.
Tse, S., **ao, X., Ko, H., Lam, J., Hui, S., & Ng, H. (2016). Do reading practices make a difference? Evidence from PIRLS data for Hong Kong and Taiwan primary school Grade 4 students. Compare: A Journal of Comparative and International Education, 46, 369–393. https://doi.org/10.1080/03057925.2014.927732.
Walberg, H. (1981). A psychological theory of educational productivity. In F. H. Farley & N. Gordon (Eds.), Psychology and education (pp. 81–110). Berkeley, CA: McCutchan.
Wei, X., & Li, K. (2010). Exploring the within- and between-class correlation distributions for tumor classification. Proceedings of the National Academy of Sciences of the United States of America, 107, 6737–6742. https://doi.org/10.1073/pnas.0910140107.
**a, J., Broadhurst, D., Wilson, M., & Wishart, D. (2013). Translational biomarker discovery in clinical metabolomics: An introductory tutorial. Metabolomics, 9, 280–299. https://doi.org/10.1007/s11306-012-0482-9.
**ao, Y., & Hu, J. (2019). Assessment of optimal pedagogical factors for Canadian ESL learners' reading literacy through artificial intelligence algorithms. International Journal of English Linguistics, 9(4), 1–14. https://doi.org/10.5539/ijel.v9n4p1.
**ao, Y., Liu, Y., & Hu, J. (2019). Regression analysis of ICT impact factors on early adolescents’ reading proficiency in five high-performing countries. Frontiers in Psychology, 10, 1646. https://doi.org/10.3389/fpsyg.2019.01646.
**ao, Z., Wang, Y., Fu, K., & Wu, F. (2017). Identifying different transportation models from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of Geo-Information, 6, 57. https://doi.org/10.3390/ijgi6020057.
Zhang, D., Qian, L., Mao, B., Huang, C., Huang, B., & Si, Y. (2018). A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access, 6, 21020–21031. https://doi.org/10.1109/ACCESS.2018.2818678.
Acknowledgements
This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 [Grant Number 20NDJC01Z], the Fundamental Research Funds for the Central Universities and the Teaching Reform Research Projects in the 13th Five Year Plan of Higher Education of Zhejiang University.
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Chen, J., Zhang, Y. & Hu, J. Synergistic effects of instruction and affect factors on high- and low-ability disparities in elementary students’ reading literacy. Read Writ 34, 199–230 (2021). https://doi.org/10.1007/s11145-020-10070-0
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DOI: https://doi.org/10.1007/s11145-020-10070-0