Student and School Factor’s Influencing the Mathematics Achievement: An HLM Analysis of Indonesian Data in TIMSS 2015

Mulia Sari Dewi

Abstract


Indonesia continues to participate in Trends in International Mathematics and Science Studies (TIMSS) to increase understanding of academic performance in mathematics and science. This study aims to examine the determinants of the mathematics achievement of fourth-grade students in Indonesia from student-level variables and school-level variables. Two-level hierarchical linear modeling was used to analyze data of 4025 students from 230 schools in Indonesia who had participated in the TIMSS 2015 study. The result indicated schools resource shortage has a negative direct effect on mathematics performance, while literacy and numeracy skill when the student enters the school has a positive direct effect. In student level, home resources, parents' education, self-efficacy and students' interest in mathematics have a positive direct effect. The model also revealed a cross-level interaction between school level and student level. It is the economic background of student in one school that had a moderating effect on home resource toward mathematics performance. Variance explained from students and school levels were 17% and 44%, whereas total variance explained were 28%. The results were sizeable to make some recommendation for policy consideration which social economic background and affective characteristics of students are the main determinants of mathematics performance among Indonesian Students.


Keywords


mathematics achievement; self-efficacy; students' interest in mathematics; TIMSS study

References


Ali, H. H., & Jameel, H. T. (2016). Causes of poor performance in mathematics from teachers, parents and student’s perspective. American Academic Scientific Research Journal for Engineering, Technology, and Sciences, 15(1), 122-136.

Bloom, C. M., & Owens, E. W. (2011). Principals’perception of influence on factors affecting student achievement in low- and high-achieving urban high schools. Education and Urban Society, 45, 208-233. https://doi.org/10.1177/0013124511406916

Cambridge International Examinations. (2015). International survey: PISA, TIMSS and PIRLS. Education Brief 7 Cambridge IGCSE.

Chand, S., Chaudhary, K. C., Prasad, A., & Chand, V. (2021). A dissection of achievements in mathematics. Frontiers in Applied Mathematics and Statistics, 7, 10.

Creemers, B. P. M., & Kyriakides, L. (2006). Critical analysis of the current approaches tomodelling educational effectiveness: The importance of establishing a dynamic model. School Effectiveness and School Improvement, 17, 347–366. https://doi.org/10.1080/09243450600697242

Creemers, B. P. M. (1994). The effective classroom. Cassell.

Demira, I., Kilic, S., Ünalc, H. (2010). Effects of students’ and schools’ characteristics on mathematics achievement: Findings from PISA 2006. Procedia Social and Behavioral Sciences 2, 3099–3103.

Fuller, B. (1987). What school factors raise achievement in the third world. Review of Educational Research, 57, 255-292. https://doi.org/10.3102/00346543057003255

Hammouri, H. A. M. (2004). Attitudinal and motivational variables related to mathematics achievement in Jordan: Findings from the Third International Mathematics and Science Study (TIMSS). Educational Research, 46. https://doi.org/10.1037/a0014532. ISSN: 0008-400X

Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Mathematics low achievement in Greece: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data. Themes in Science and Technology Education, 9(1), 3-24.

Ker, H. W. (2016). The impacts of student-, teacher-and school-level factors on mathematics achievement: an exploratory comparative investigation of Singaporean students and the USA students. Educational Psychology, 36(2), 254-276.

Kraft, M., & Dougherty, S. (2013). The effect of teacher–family communication on student engagement: Evidence from a randomized field experiment. Journal of Research on Educational Effectiveness, 6, 199–222. https://doi.org/10.1080/19345747.2012.743636

Krajcik, J. (2011). Learning progressions provide road maps for the development and validity of assessments and curriculum materials. Measurement: Interdisciplinary Research & Rerspective, 9 (2), 155-158.

Lee, V. E., Smith, J. B., & Croninger, R. G. (1997). How high school organization influencesthe equitable distribution of learning in mathematics and science. Sociology of Education, 70, 128–150. https://doi.org/10.2307/2673160

LeFevre, J., Skwarchuk, S.L., Smith-Chant, B. L., Fast, L., Kamawar, D., & Bisanz, J. (2009). Home numeracy experiences and children's math performance in the early school years. Canadian Journal of Behavioural Science, 41(2), 55–66.

Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning mathematics as “given” in STEM education. International Journal of STEM Education, 6(1), 1-13.

Linn, M.C., Else-Quest, N. M., & Hyde, J.S. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136, 103–127.

Lopez, E. M., Gallimore, R., Garnier, H., & Reese, L. (2007). Preschool antecedents of mathematics achievement of Latinos: The influence of family resources, early literacy experiences, and preschool attendance. Hispanic Journal of Behavioral Sciences, 29(4), 456-471.

Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

Nizam & Santoso, A. (2013, March 26-27). Indonesia: OER initiatives & ICT in Teacher Training. Paper presented at the UNESCO-OER Follow up Meeting, Paris http://www.unesco.org/new/fileadmin/MULTIMEDIA/HQ/CI/CI/pdf/news/indonesia_oer_initiatives.pdf

Pannen, P. (2015). Integrating technology in teaching and learning mathematics. Southeast Asian Mathematics Education Journal, 5(1), 31-48.

Purpura, D. J., Hume, L. E., Sims, D.M., & Lonigan, C.J. (2011). Early literacy and early numeracy: The value of including early literacy skills in the prediction of numeracy development. Journal of Experimental Child Psychology, 110, 647-658.

Raudenbush, S. W., Bryk, A. S., Cheong Y. K., & Congdon, R. T., Jr. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International.

Rumberger, R. W., & Palardy, G. J. (2004). Multilevel models for school effectivenessresearch.http://pages.gseis.ucla.edu/faculty/muthen/Ed255C/Reference/Rumberger%20and%20Palardy–School%20Effectiveness.pdf

Sa’ad, T. U., Adamu, A., & Sadiq, A. M. (2014). The causes of poor performance in mathematics among public senior secondary school students in Azare metropolis of Bauchi State, Nigeria. Journal of Research & Method in Education, 4(6), 32.

Scheerens, J., & Creemers, B. (1989). Conceptualizing school effectiveness. International Journal of Educational Research, 13, 691–706. https://doi.org/10.1016/0883-0355(89)90022-0

Spaull,N. (2011). A preliminary analysis of SACMEQ III South Africa. Stellenbosch Economic Working Papers: 11/11.

Spaull, N. (2013). Poverty & Privilege: Primary school inequality in South Africa. International Journal of Educational Development, 33:436-447.

Strand, S. (2010). Do some schools narrow the gap? differential school effectiveness byethnicity, gender, poverty, and prior achievement. School Effectiveness and School Improvement, 21, 289–314. doi:10.1080/09243451003732651

Suleiman, Y., & Hammed, A. (2019). Perceived causes of students’ failure in mathematics in Kwara State Junior Secondary Schools: Implication for educational managers. International Journal of Educational Studies in Mathematics, 6(1), 19-33.

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 Assess Educ. 3(3). https://doi.org/10.1186/s40536-015-0013-z

Tshabalala, T., & Ncube, A. C. (2013). Causes of poor performance of ordinary level pupils in mathematics in rural secondary schools in Nkayi district: Learner’s attributions. Nova Journal of Medical and Biological Sciences, 1(1), 4-14.

Visser, M., Juan, A., & Feza, N. (2015). Home and school resources as predictors of mathematics performance in South Africa. South African Journal of Education, 35(1).

Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12, 265–310. https://doi.org/10.1016/0273-2297(92)90011-P

Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. https://doi.org/10.1006/ceps.1999.1015


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DOI: 10.15408/tazkiya.v10i1.24890

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