References
- Agirdag, O., & Vanlaar, G. (2018). Does more exposure to the language of instruction lead to higher academic achievement? A cross-national examination. International Journal of Bilingualism, 22(1), 123–127. https://doi.org/https://doi.org/10.1177/1367006916658711
- Ainley, M., & Ainley, J. (2011). Student engagement with science in early adolescence: The contribution of enjoyment to students’ continuing interest in learning about science. Contemporary Educational Psychology, 36(1), 4–12. https://doi.org/https://doi.org/10.1016/j.cedpsych.2010.08.001
- Aitkin, M., & Longford, N. (1986). Statistical modelling issues in school effectiveness studies. Journal of the Royal Statistical Society. Series A (General), 149(1), 1–43. https://doi.org/https://doi.org/10.2307/2981882
- 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/https://doi.org/10.1002/pits.20303
- Archer, L., Dawson, E., DeWitt, J., Seakins, A., & Wong, B. (2015). “Science capital”: A conceptual, methodological, and empirical argument for extending bourdieusian notions of capital beyond the arts. Journal of Research in Science Teaching, 52(7), 922–948. https://doi.org/https://doi.org/10.1002/tea.21227
- Buehl, M. M., & Alexander, P. A. (2005). Motivation and performance differences in students’ domain-specific epistemological belief profiles. American Educational Research Journal, 42(4), 697–726. https://doi.org/https://doi.org/10.3102/00028312042004697
- 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/https://doi.org/10.1016/j.stueduc.2016.03.005
- Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. https://doi.org/https://doi.org/10.1145/1961189.1961199
- Chen, F., & Cui, Y. (2020). Investigating the relation of perceived teacher unfairness to science achievement by hierarchical linear modeling in 52 countries and economies. Educational Psychology, 40(3), 273–295. https://doi.org/https://doi.org/10.1080/01443410.2019.1652248
- Chen, J., Zhang, Y., & Hu, J. (2021). Synergistic effects of instruction and affect factors on high- and low-ability disparities in elementary students’ reading literacy. Reading and Writing, 34(1), 199–230. https://doi.org/https://doi.org/10.1007/s11145-020-10070-0
- 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/https://doi.org/10.1007/s11165-019-9835-y
- Chen, X., & Hu, J. (2020). ICT-related behavioral factors mediate the relationship between adolescents’ ICT interest and their ICT self-efficacy: Evidence from 30 countries. Computers & Education, 159, Article 104004. https://doi.org/https://doi.org/10.1016/j.compedu.2020.104004
- Chudgar, A., & Luschei, T. F. (2009). National income, income inequality, and the importance of schools: A hierarchical cross-national comparison. American Educational Research Journal, 46(3), 626–658. https://doi.org/https://doi.org/10.3102/0002831209340043
- Connor, C. M., Morrison, F. J., & Katch, L. E. (2004). Beyond the reading wars: Exploring the effect of child-instruction interactions on growth in early reading. Scientific Studies of Reading, 8(4), 305–336. https://doi.org/https://doi.org/10.1207/s1532799xssr0804_1
- Cools, W., De Fraine, B., Van den Noortgate, W., & Onghena, P. (2009). Multilevel design efficiency in educational effectiveness research. School Effectiveness and School Improvement, 20(3), 357–373. https://doi.org/https://doi.org/10.1080/09243450902850176
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/https://doi.org/10.1007/BF00994018
- Creemers, B. P. M. (2006). The importance and perspectives of international studies in educational effectiveness. Educational Research and Evaluation, 12(6), 499–511. https://doi.org/https://doi.org/10.1080/13803610600873978
- Creemers, B. P. M., & Kyriakides, L. (2008). The dynamics of educational effectiveness: A contribution to policy, practice and theory in contemporary schools. Routledge.
- Creemers, B. P. M., & Kyriakides, L. (2009). Situational effects of the school factors included in the dynamic model of educational effectiveness. South African Journal of Education, 29(3), 293–315. https://doi.org/https://doi.org/10.15700/saje.v29n3a270
- Creemers, B. P. M. & Kyriakides, L. (2010a). Explaining stability and changes in school effectiveness by looking at changes in the functioning of school factors. School Effectiveness and School Improvement, 21(4), 409–427. https://doi.org/https://doi.org/10.1080/09243453.2010.512795
- Creemers, B. P. M., & Kyriakides, L. (2010b). School factors explaining achievement on cognitive and affective outcomes: Establishing a dynamic model of educational effectiveness. Scandinavian Journal of Educational Research, 54(3), 263–294. https://doi.org/https://doi.org/10.1080/00313831003764529
- Creemers, B. P. M., & Kyriakides, L. (2015). Developing, testing and using theoretical models of educational effectiveness for promoting quality in education. School Effectiveness and School Improvement, 26(1), 102–119. https://doi.org/https://doi.org/10.1080/09243453.2013.869233
- Creemers, B. P. M., Kyriakides, L., & Sammons, P. (2010). Methodological advances in school effectiveness research. Routledge.
- de Leeuw, J., & Kreft, I. (1986). Random coefficient models for multilevel analysis. Journal of Educational Statistics, 11(1), 57–85. https://doi.org/https://doi.org/10.2307/1164848
- Destin, M., Hanselman, P., Buontempo, J., Tipton, E., & Yeager, D. (2019). Do student mindsets differ by socioeconomic status and explain disparities in academic achievement in the United States? AERA Open, 5(3), Article 2332858419857706. https://doi.org/https://doi.org/10.1177/2332858419857706
- Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R., Jr., Barch, D. M., Petersen, S. E., & Schlaggar, B. L. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361. https://doi.org/https://doi.org/10.1126/science.1194144
- Epstein, J. L. (2001). School, family, and community partnerships: Preparing educators and improving schools. Westview Press.
- Essuman, A. (2019). Improving education delivery through community – school partnership: Is the “social contract” being weakened? – A study of two rural schools. International Journal of Educational Management, 33(6), 1336–1351. https://doi.org/https://doi.org/10.1108/IJEM-06-2018-0175
- European Commission. (2014). The Teaching and Learning International Survey (TALIS) 2013: Main findings from the survey and implications for education and training policies in Europe. https://ec.europa.eu/assets/eac/education/library/reports/2014/talis_en.pdf
- Fan, R.-E., Chen, P.-H., & Lin, C.-J. (2005). Working set selection using second order information for training support vector machines. The Journal of Machine Learning Research, 6, 1889–1918. https://doi.org/https://doi.org/10.5555/1046920.1194907
- Fariña, P., Martín, S. E., Preiss, D. D., Claro, M., & Jara, I. (2015). Measuring the relation between computer use and reading literacy in the presence of endogeneity. Computers & Education, 80, 176–186. https://doi.org/https://doi.org/10.1016/j.compedu.2014.08.010
- Feliciano, C., & Rumbaut, R. G. (2005). Gendered paths: Educational and occupational expectations and outcomes among adult children of immigrants. Ethnic and Racial Studies, 28(6), 1087–1118. https://doi.org/https://doi.org/10.1080/01419870500224406
- Filiz, E., & Öz, E. (2019). Finding the best algorithms and effective factors in classification of Turkish science student success. Journal of Baltic Science Education, 18(2), 239–253. https://doi.org/https://doi.org/10.33225/jbse/19.18.239
- Flannery, K. B., Frank, J. L., & Kato, M. M. (2012). School disciplinary responses to truancy: Current practice and future directions. Journal of School Violence, 11(2), 118–137. https://doi.org/https://doi.org/10.1080/15388220.2011.653433
- Gilleece, L., Cosgrove, J., & Sofroniou, N. (2010). Equity in mathematics and science outcomes: Characteristics associated with high and low achievement on PISA 2006 in Ireland. International Journal of Science and Mathematics Education, 8(3), 475–496. https://doi.org/https://doi.org/10.1007/s10763-010-9199-2
- Goldstein, H. (2003). Multilevel statistical models (3rd ed.). Edward Arnold.
- González, R. L., & Jackson, C. L. (2013). Engaging with parents: The relationship between school engagement efforts, social class, and learning. School Effectiveness and School Improvement, 24(3), 316–335. https://doi.org/https://doi.org/10.1080/09243453.2012.680893
- Gorostiaga, A., & Rojo-Álvarez, J. L. (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/https://doi.org/10.1016/j.neucom.2015.07.001
- Harris, A. (2001). Building the capacity for school improvement. School Leadership & Management, 21(3), 261–270. https://doi.org/https://doi.org/10.1080/13632430120074419
- Hill, P. W., & Rowe, K. J. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement, 7(1), 1–34. https://doi.org/https://doi.org/10.1080/0924345960070101
- Holzinger, A. (2018). From machine learning to explainable AI. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), 55–66. https://doi.org/https://doi.org/10.1109/DISA.2018.8490530
- Hu, J. (2014). An analysis of the design process of a language learning management system. Control and Intelligent Systems, 42(1), 80–86. https://doi.org/https://doi.org/10.2316/Journal.201.2014.1.201-2534
- Hu, J., Chen, K., & Liu, D. (2020). Chinese university faculty members’ visiting experience and professional growth in American universities. Social Behavior and Personality: An International Journal, 48(5), Article e7898. https://doi.org/https://doi.org/10.2224/sbp.7898
- Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 299–310. https://doi.org/https://doi.org/10.1109/TKDE.2005.50
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer Verlag. https://doi.org/https://doi.org/10.1007/978-1-4614-7138-7
- Jennings, J. L., Deming, D., Jencks, C., Lopuch, M., & Schueler, B. E. (2015). Do differences in school quality matter more than we thought? New evidence on educational opportunity in the twenty-first century. Sociology of Education, 88(1), 56–82. https://doi.org/https://doi.org/10.1177/0038040714562006
- Jing, Y., Li, B., Chen, N., Li, X., & Hu, J. (2015). The discrimination of learning styles by Bayes-based statistics: An extended study on ILS system. Control and Intelligent Systems, 43(2), 68–75. https://doi.org/https://doi.org/10.2316/Journal.201.2015.2.201-2666
- Kao, G., & Tienda, M. (1995). Optimism and achievement: The educational performance of immigrant youth. Social Science Quarterly, 76(1), 1–19. https://www.jstor.org/stable/44072586
- Kearney, C. A. (2008). School absenteeism and school refusal behavior in youth: A contemporary review. Clinical Psychology Review, 28(3), 451–471. https://doi.org/https://doi.org/10.1016/j.cpr.2007.07.012
- Kelcey, B., & Shen, Z. (2016). Multilevel design of school effectiveness studies in sub-Saharan Africa. School Effectiveness and School Improvement, 27(4), 492–510. https://doi.org/https://doi.org/10.1080/09243453.2016.1168855
- Kilic Depren, S. (2018). Prediction of students’ science achievement: An application of multivariate adaptive regression splines and regression trees. Journal of Baltic Science Education, 17(5), 887–890. https://doi.org/https://doi.org/10.33225/jbse/18.17.887
- Kyriakides, L. (2008). Testing the validity of the comprehensive model of educational effectiveness: A step towards the development of a dynamic model of effectiveness. School Effectiveness and School Improvement, 19(4), 429–446. https://doi.org/https://doi.org/10.1080/09243450802535208
- Kyriakides, L., Charalambous, E., Creemers, B. P. M., Antoniou, P., Devine, D., Papastylianou, D., & Fahie, D. (2019). Using the dynamic approach to school improvement to promote quality and equity in education: A European study. Educational Assessment, Evaluation and Accountability, 31(1), 121–149. https://doi.org/https://doi.org/10.1007/s11092-018-9289-1
- Kyriakides, L., Charalambous, E., Creemers, B. P. M., & Dimosthenous, A. (2019). Improving quality and equity in schools in socially disadvantaged areas. Educational Research, 61(3), 274–301. https://doi.org/https://doi.org/10.1080/00131881.2019.1642121
- Kyriakides, L., & Creemers, B. P. M. (2008). Using a multidimensional approach to measure the impact of classroom-level factors upon student achievement: A study testing the validity of the dynamic model. School Effectiveness and School Improvement, 19(2), 183-205. https://doi.org/https://doi.org/10.1080/09243450802047873
- Kyriakides, L., Creemers, B. P. M., & Charalambous, E. (2019). Searching for differential teacher and school effectiveness in terms of student socioeconomic status and gender: Implications for promoting equity. School Effectiveness and School Improvement, 30(3), 286–308. https://doi.org/https://doi.org/10.1080/09243453.2018.1511603
- Kyriakides, L., & Luyten, H. (2009). The contribution of schooling to the cognitive development of secondary education students in Cyprus: An application of regression discontinuity with multiple cut-off points. School Effectiveness and School Improvement, 20(2), 167–186. https://doi.org/https://doi.org/10.1080/09243450902883870
- Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: An introduction to data mining (2nd ed.). John Wiley & Sons.
- Li, Q., Salman, R., Test, E., Strack, R., & Kecman, V. (2013). Parallel multitask cross validation for Support Vector Machine using GPU. Journal of Parallel and Distributed Computing, 73(3), 293–302. https://doi.org/https://doi.org/10.1016/j.jpdc.2012.02.011
- Linnakyla, P., Malin, A., & Taube, K. (2004). Factors behind low reading literacy achievement. Scandinavian Journal of Educational Research, 48(3), 231–249. https://doi.org/https://doi.org/10.1080/00313830410001695718
- Liu, X., & Whitford, M. (2011). Opportunities-to-learn at home: Profiles of students with and without reaching science proficiency. Journal of Science Education and Technology, 20(4), 375–387. https://doi.org/https://doi.org/10.1007/s10956-010-9259-y
- Ma, J., Pender, M., & Welch, M. (2016). Education Pays 2016: The benefits of higher education for individuals and society. College Board.
- Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: A cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instructional Science, 41(1), 49–79. https://doi.org/https://doi.org/10.1007/s11251-012-9210-0
- Mills, M., Howell, A., Lynch, D., & Dungan, J. (2019). Approaches to improving school attendance: Insights from Australian principals. Leadership and Policy in Schools. Advance online publication. https://doi.org/https://doi.org/10.1080/15700763.2019.1695847
- Molinari, L., Speltini, G., & Passini, S. (2013). Do perceptions of being treated fairly increase students’ outcomes? Teacher–student interactions and classroom justice in Italian adolescents. Educational Research and Evaluation, 19(1), 58–76. https://doi.org/https://doi.org/10.1080/13803611.2012.748254
- Mou, W., Liu, Z., Luo, Y., Zou, M., Ren, C., Zhang, C., Wen, X., Wang, Y., & Tian, Y. P. (2014). Development and cross-validation of prognostic models to assess the treatment effect of cisplatin/pemetrexed chemotherapy in lung adenocarcinoma patients. Medical Oncology, 31(9), Article 59. https://doi.org/https://doi.org/10.1007/s12032-014-0059-8
- Mugendawala, H., & Muijs, D. (2020). Educational process factors for effective education in resource-constrained countries: A multilevel analysis. School Effectiveness and School Improvement, 31(3), 445–467. https://doi.org/https://doi.org/10.1080/09243453.2019.1702562
- Nash, R. (2003). Is the school composition effect real?: A discussion with evidence from the UK PISA data. School Effectiveness and School Improvement, 14(4), 441–457. https://doi.org/https://doi.org/10.1076/sesi.14.4.441.17153
- Ning, B. (2019). Examining the importance of discipline in Chinese schooling: An exploration in Shanghai, Hong Kong, Macao, and Taipei. Asia Pacific Education Review, 20(3), 489–501. https://doi.org/https://doi.org/10.1007/s12564-018-9563-4
- O’Connell, M. (2019). Is the impact of SES on educational performance overestimated? Evidence from the PISA survey. Intelligence, 75, 41–47. https://doi.org/https://doi.org/10.1016/j.intell.2019.04.005
- Organisation for Economic Co-operation and Development. (2009). PISA data analysis manual: SPSS (2nd ed.). https://doi.org/https://doi.org/10.1787/9789264056275-en
- Organisation for Economic Co-operation and Development. (2016). PISA 2015 technical report. https://www.oecd.org/pisa/data/2015-technical-report/
- Ortega, L., Malmberg, L.-E., & Sammons. P. (2018) School effects on Chilean children’s achievement growth in language and mathematics: An accelerated growth curve model. School Effectiveness and School Improvement, 29(2), 308–337. https://doi.org/https://doi.org/10.1080/09243453.2018.1443945
- Pham, B. T., Pradhan, B., Bui, D. T., Prakash, I., & Dholakia, M. B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software, 84, 240–250. https://doi.org/https://doi.org/10.1016/j.envsoft.2016.07.005
- Plomin, R., & Deary, I. J. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20(1), 98–108. https://doi.org/https://doi.org/10.1038/mp.2014.105
- Povey, J., Campbell, A. K., Willis, L.-D., Haynes, M., Western, M., Bennett, S., Antrobus, E., & Pedde, C. (2016). Engaging parents in schools and building parent-school partnerships: The role of school and parent organization leadership. International Journal of Educational Research, 79, 128–141. https://doi.org/https://doi.org/10.1016/j.ijer.2016.07.005
- Raudenbush, S., & Bryk, A. S. (1986). A hierarchical model for studying school effects. Sociology of Education, 59(1), 1–17. https://doi.org/https://doi.org/10.2307/2112482
- Raudenbush, S. W., & Bryk, A. S. (1988). Methodological advances in analyzing the effects of schools and classrooms on student learning. Review of Research in Education, 15(1), 423–476. https://doi.org/https://doi.org/10.3102/0091732X015001423
- Reardon, S. F., Fahle, E. M., Kalogrides, D., Podolsky, A., & Zárate, R. C. (2019). Gender achievement gaps in U.S. school districts. American Educational Research Journal, 56(6), 2474–2508. https://doi.org/https://doi.org/10.3102/0002831219843824
- Reezigt, G., & Creemers, B. P. M. (2005). A comprehensive framework for effective school improvement. School Effectiveness and School Improvement, 16(4), 407–424. https://doi.org/https://doi.org/10.1080/09243450500235200
- Reynolds, D., Sammons, P., De Fraine, B., Van Damme, J., Townsend, T., Teddlie, C., & Stringfield, S. (2014). Educational effectiveness research (EER): A state-of-the-art review. School Effectiveness and School Improvement, 25(2), 197–230. https://doi.org/https://doi.org/10.1080/09243453.2014.885450
- Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458. https://doi.org/https://doi.org/10.1111/j.1468-0262.2005.00584.x
- Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517. https://doi.org/https://doi.org/10.1093/bioinformatics/btm344
- Sammons, P., Hall, J., Sylva, K., Melhuish, E., Siraj-Blatchford, I., & Taggart, B. (2013). Protecting the development of 5–11-year-olds from the impacts of early disadvantage: The role of primary school academic effectiveness. School Effectiveness and School Improvement, 24(2), 251–268. https://doi.org/https://doi.org/10.1080/09243453.2012.749797
- Sarafidou, J.-O., & Chatziioannidis, G. (2013). Teacher participation in decision making and its impact on school and teachers. International Journal of Educational Management, 27(2), 170–183. https://doi.org/https://doi.org/10.1108/09513541311297586
- Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Educational Researcher, 44(7), 371–386. https://doi.org/https://doi.org/10.3102/0013189X15603982
- Shafri, H. Z. M, & Ramle, F. S. H. (2009). A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island. Information Technology Journal, 8(1), 64–70. https://doi.org/https://doi.org/10.3923/itj.2009.64.70
- Shao, Y., & Lunetta, R. S. (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78–87. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2012.04.001
- She, H. C., Lin, H. S., & Huang, L. Y. (2019). Reflections on and implications of the Programme for International Student Assessment (PISA 2015) performance of students in Taiwan: The role of epistemic beliefs about science in scientific literacy. Journal of Research in Science Teaching, 56(10), 1309–1340. https://doi.org/https://doi.org/10.1002/tea.21553
- 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/https://doi.org/10.3102/00346543075003417
- Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). SAGE Publications.
- Soh, K. (2014). Finland and Singapore in PISA 2009: Similarities and differences in achievements and school management. Compare: A Journal of Comparative and International Education, 44(3), 455–471. https://doi.org/https://doi.org/10.1080/03057925.2013.787286
- Strand, S. (2010). Do some schools narrow the gap? Differential school effectiveness by ethnicity, gender, poverty, and prior achievement. School Effectiveness and School Improvement, 21(3), 289–314. https://doi.org/https://doi.org/10.1080/09243451003732651
- Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/https://doi.org/10.1037/a0016973
- Teddlie, C., & Reynolds, D. (2000). The international handbook of school effectiveness research. Falmer Press.
- Thorpe, G. (2006). Multilevel analysis of PISA 2000 reading results for the United Kingdom using pupil scale variables. School Effectiveness and School Improvement, 17(1), 33–62, https://doi.org/https://doi.org/10.1080/09243450500264473
- Turkheimer, E., Pettersson, E., & Horn, E. E. (2014). A phenotypic null hypothesis for the genetics of personality. Annual Review of Psychology, 65, 515–540. https://doi.org/https://doi.org/10.1146/annurev-psych-113011-143752
- van Hek, M., Kraaykamp, G., & Pelzer, B. (2018). Do schools affect girls’ and boys’ reading performance differently? A multilevel study on the gendered effects of school resources and school practices. School Effectiveness and School Improvement, 29(1), 1–21. https://doi.org/https://doi.org/10.1080/09243453.2017.1382540
- Vanlaar, G., Kyriakides, L., Panayiotou, A., Vandecandelaere, M., McMahon, L., De Fraine, B., & Van Damme, J. (2016). Do the teacher and school factors of the dynamic model affect high- and low-achieving student groups to the same extent? A cross-country study. Research Papers in Education, 31(2), 183–211. https://doi.org/https://doi.org/10.1080/02671522.2015.1027724
- Verachtert, P., Van Damme, J., Onghena, P., & Ghesquière, P. (2009). A seasonal perspective on school effectiveness: Evidence from a Flemish longitudinal study in kindergarten and first grade. School Effectiveness and School Improvement, 20(2), 215–233. https://doi.org/https://doi.org/10.1080/09243450902883896
- Wei, X., & Li, K.-C. (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(15), 6737–6742. https://doi.org/https://doi.org/10.1073/pnas.0910140107
- Wei, Y., Yang, Q., Chen, J., & Hu, J. (2018). The exploration of a machine learning approach for the assessment of learning styles changes. Mechatronic Systems and Control, 46(3), 121–126. https://doi.org/https://doi.org/10.2316/Journal.201.2018.3.201-2979
- Xia, J., Broadhurst, D., Wilson, M., & Wishart, D. S. (2013). Translational biomarker discovery in clinical metabolomics: An introductory tutorial. Metabolomics, 9(2), 280–299. https://doi.org/https://doi.org/10.1007/s11306-012-0482-9
- Xiao, Y., & Hu, J. (2019). The moderation examination of ICT use on the association between Chinese mainland students’ socioeconomic status and reading achievement. International Journal of Emerging Technologies in Learning, 14(15), 107–120. https://doi.org/https://doi.org/10.3991/ijet.v14i15.10494
- Xiao, 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, Article 1646. https://doi.org/https://doi.org/10.3389/fpsyg.2019.01646
- Yan, L., Rodier, R., Mozer, M., & Wolniewicz, R. (2003). Optimizing classifier performance via the Wilcoxon-Mann-Withney statistics. In Proceedings of the twentieth International Conference on Machine Learning (pp. 848–855). https://www.aaai.org/Papers/ICML/2003/ICML03-110.pdf
- Yu, C. H., Kaprolet, C., Jannasch-Pennell, A., & Digangi, S. (2012). A data mining approach to comparing American and Canadian grade 10 students’ PISA science test performance. Journal of Data Science, 10(3), 441–464. https://doi.org/https://doi.org/10.6339/JDS.201207_10(3).0006