Déterminants de la performance scolaire des élèves du primaire au Togo, à la convergence de l’objectif de développement durable 4
DOI :
https://doi.org/10.18559/rielf.2025.2.3011Mots-clés :
performance scolaire, éducation, multiniveau, TogoRésumé
Objectif : Cet article examine les facteurs déterminants de la performance scolaire des élèves du primaire en mathématiques et en lecture au Togo, en lien avec les Objectifs de Développement Durable 4 (ODD4).
Conception/méthodologie/approche : Nous avons appliqué un modèle multiniveau à deux niveaux, inspiré de Pascal Bressoux, pour analyser les effets des caractéristiques individuelles et contextuelles sur la performance scolaire. Cette approche tient compte de la structure hiérarchique des données et de la forte dépendance intra-groupe (ICC = 0,723). L’analyse utilise les données PASEC 2019 au Togo (6000 élèves dans 180 écoles), la performance étant mesurée par la moyenne des scores plausibles en mathématiques et lecture, avec pondération et correction des erreurs standards pour la structure en grappes.
Résultats : Les résultats révèlent que, les caractéristiques individuelles, telles que le sexe, l’âge, l'intérêt pour la matière, l’accès à l’éducation préscolaire, le redoublement, le statut socioéconomique, ainsi que des facteurs contextuels tels que le sexe, l’âge, le niveau d’éducation et l’expérience des enseignants, l’effectif de classe et la localisation de l’école, jouent un rôle déterminant dans la réussite scolaire des élèves du primaire au Togo.
Originalité/valeur : L’amélioration de la performance scolaire passe par des réformes ciblées visant à promouvoir l’éducation préscolaire, à instaurer des mécanismes stimulant l’intérêt des élèves et à réduire le redoublement grâce à des évaluations adaptées. Toutefois, ces mesures doivent s’accompagner d’un renforcement de la formation des enseignants et d’un investissement accru dans les infrastructures scolaires, en particulier en milieu rural, afin de garantir une éducation plus équitable et efficace.
JEL Classification
Input–Output Models (C67)
Analysis of Education (I21)
Education and Economic Development (I25)
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Références
Ainley, J. (1995). Students’ views of their schools. Unicorn, 21(3), 5–16.
View in Google Scholar
Andrabi, T., Das, J., Khwaja, A. I., Ozyurt, S., & Singh, N. (2020). Upping the ante: The equilibrium effects of unconditional grants to private schools. American Economic Review, 110(10), 3315–3349. https://doi.org/10.1257/aer.20180924
View in Google Scholar
DOI: https://doi.org/10.1257/aer.20180924
Asadullah, M., Bouhlila, D. S., Chan, S. J., Draxler, A., Ha, W., Heyneman, S. P., Luschei, T. F., Semela, T., & Yemini, M. (2023). A year of missed opportunity: Post-COVID learning loss–a renewed call to action. International Journal of Educational Development, 99, 102770. https://doi.org/10.1016/j.ijedudev.2023.102770
View in Google Scholar
DOI: https://doi.org/10.1016/j.ijedudev.2023.102770
Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5, Part 2), 9–49.
View in Google Scholar
DOI: https://doi.org/10.1086/258724
Berlinski, S., Galiani, S., & Gertler, P. (2009). The effect of pre-primary education on primary school performance. Journal of Public Economics, 93(1–2), 219–234.
View in Google Scholar
DOI: https://doi.org/10.1016/j.jpubeco.2008.09.002
Bianco, M., Bressoux, P., Doyen, A. L., Lambert, E., Lima, L., Pellenq, C., & Zorman, M. (2010). Early training in oral comprehension and phonological skills: Results of a three-year longitudinal study. Scientific Studies of Reading, 14(3), 211–246.
View in Google Scholar
DOI: https://doi.org/10.1080/10888430903117518
Borde, S. F. (1998). Predictors of student academic performance in the introductory marketing course. Journal of Education for Business, 73(5), 302–306.
View in Google Scholar
DOI: https://doi.org/10.1080/08832329809601649
Bourdieu, P. (1966). L’école conservatrice: Les inégalités devant l’école et devant la culture. Revue Française de Sociologie, 7(3), 325–347.
View in Google Scholar
DOI: https://doi.org/10.2307/3319132
Coleman, J. S. (1968). Equality of educational opportunity. Integrated Education, 6(5), 19–28.
View in Google Scholar
DOI: https://doi.org/10.1080/0020486680060504
Considine, G., & Zappalà, G. (2002). The influence of social and economic disadvantage in the academic performance of school students in Australia. Journal of Sociology, 38(2), 129–148.
View in Google Scholar
DOI: https://doi.org/10.1177/144078302128756543
Duflo, E., Hanna, R., & Ryan, S. P. (2012). Incentives work: Getting teachers to come to school. American Economic Review, 102(4), 1241–1278.
View in Google Scholar
DOI: https://doi.org/10.1257/aer.102.4.1241
Edwards, D. B., Jr., Asadullah, M. N., & Webb, A. (2024). Critical perspectives at the mid-point of Sustainable Development Goal 4: Quality education for all—progress, persistent gaps, problematic paradigms, and the path to 2030. International Journal of Educational Development, 107, 103031. https://doi.org/10.1016/j.ijedudev.2024.103031
View in Google Scholar
DOI: https://doi.org/10.1016/j.ijedudev.2024.103031
Erdogdu, F., & Erdogdu, E. (2025). Beyond rankings: A multidimensional analysis of factors shaping student achievement in the PISA 2022 dataset. Educational Review, 77(5), 1–42.
View in Google Scholar
DOI: https://doi.org/10.1080/00131911.2025.2506807
Evans, D. K., & Yuan, F. (2022). How big are effect sizes in international education studies? Educational Evaluation and Policy Analysis, 44(3), 532–540.
View in Google Scholar
DOI: https://doi.org/10.3102/01623737221079646
Feitelson, D., Kita, B., & Goldstein, Z. (1986). Effects of listening to series stories on first graders’ comprehension and use of language. Research in the Teaching of English, 20(4), 339–356.
View in Google Scholar
DOI: https://doi.org/10.58680/rte198615596
Fredriksson, P., Öckert, B., & Oosterbeek, H. (2013). Long-term effects of class size. The Quarterly Journal of Economics, 128(1), 249–285.
View in Google Scholar
DOI: https://doi.org/10.1093/qje/qjs048
Fuchs, T., & Woessmann, L. (2007). What accounts for international differences in student performance? CESifo Working Paper, 1235. https://www.econstor.eu/bitstream/10419/18874/1/cesifo1_wp1235.pdf
View in Google Scholar
Gershenson, S., & Langbein, L. (2015). The effect of primary school size on academic achievement. Educational Evaluation and Policy Analysis, 37(1), 135S–155S.
View in Google Scholar
DOI: https://doi.org/10.3102/0162373715576075
Hanushek, E. A., & Raymond, M. E. (2005). Does school accountability lead to improved student performance? Journal of Policy Analysis and Management, 24(2), 298–327.
View in Google Scholar
DOI: https://doi.org/10.1002/pam.20091
Jaramillo, S. G., Monsalve, C. F., & Torres, F. J. S. (2010). Deserción y repetición en los primeros grados de la básica primaria: Factores de riesgo y alternativas de política pública. Proyecto Educación Compromiso de Todos.
View in Google Scholar
Kane, M. T. (2001). Current concerns in validity theory. Journal of Educational Measurement, 38(4), 319–342.
View in Google Scholar
DOI: https://doi.org/10.1111/j.1745-3984.2001.tb01130.x
Krueger, A. B., & Lindahl, M. (2001). Education for growth: Why and for whom? Journal Of Economic Literature, 39(4), 1101–1136.
View in Google Scholar
DOI: https://doi.org/10.1257/jel.39.4.1101
Lazarides, R., & Schiefele, U. (2021). Teacher motivation: Implications for instruction and learning. Introduction to the special issue. Learning and Instruction, 76, 101543. https://doi.org/10.1016/j.learninstruc.2021.101543
View in Google Scholar
DOI: https://doi.org/10.1016/j.learninstruc.2021.101543
Liu, X., He, P., Chen, W., & Gao, J. (2019). Multi-task deep neural networks for natural language understanding. Computer Science. https://doi.org/10.48550/arXiv.1901.11504
View in Google Scholar
DOI: https://doi.org/10.18653/v1/P19-1441
Magnuson, K. (2007). Maternal education and children’s academic achievement during middle childhood. Developmental Psychology, 43(6), 1497–1512.
View in Google Scholar
DOI: https://doi.org/10.1037/0012-1649.43.6.1497
Mason, W. M., Wong, G. Y., & Entwisle, B. (1983). Contextual analysis through the multilevel linear model. Sociological Methodology, 14, 72–103.
View in Google Scholar
DOI: https://doi.org/10.2307/270903
Meuret, D. (2001). Les recherches sur la réduction de la taille des classes. Report. Haut Conseil de l’Évaluation de l’École. http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14215753
View in Google Scholar
Michaelowa, K. (2001). Primary education quality in francophone sub-Saharan Africa: Determinants of learning achievement and efficiency considerations. World Development, 29(10), 1699–1716.
View in Google Scholar
DOI: https://doi.org/10.1016/S0305-750X(01)00061-4
Michaelowa, K., & Wechtler, A. (2006). The cost-effectiveness of inputs in primary education: Insights from the literature and recent student surveys for sub-Saharan Africa. Study commissioned by the Association for the Development of Education in Africa,.ADEA Working Document.
View in Google Scholar
Mincer, J. (1958). Investment in human capital and personal income distribution. Journal of Political Economy, 66(4), 281–302.
View in Google Scholar
DOI: https://doi.org/10.1086/258055
Mokonzi Bambanota, G., Van Damme, J., & De Fraine, B. (2019). Impact des caractéristiques individuelles et des caractéristiques de la classe sur le concept de soi des élèves de quatrième année primaire en République Démocratique du Congo. European Scientific Journal, 15(22), 56–82.
View in Google Scholar
DOI: https://doi.org/10.19044/esj.2019.v15n22p56
Muralidharan, K., & Sundararaman, V. (2011). Teacher performance pay: Experimental evidence from India. Journal of Political Economy, 119(1), 39–77.
View in Google Scholar
DOI: https://doi.org/10.1086/659655
Nye, B., Hedges, L. V., & Konstantopoulos, S. (2000). The effects of small classes on academic achievement: The results of the Tennessee class size experiment. American Educational Research Journal, 37(1), 123–151.
View in Google Scholar
DOI: https://doi.org/10.3102/00028312037001123
PASEC. (2019). Base de données PASEC 2019. Programme d’Analyse des Systèmes Éducatifs.
View in Google Scholar
Piketty, T., & Yang, L. (2022). Income and wealth inequality in Hong Kong, 1981–2020: The rise of Pluto-communism? The World Bank Economic Review, 36(4), 803–834. https://doi.org/10.1093/wber/lhac019
View in Google Scholar
DOI: https://doi.org/10.1093/wber/lhac019
Postlethwaite, T. N., & Wiley, D. E. (1992). The IEA study of science II: Science achievement in 23 countries. Pergamon Press.
View in Google Scholar
Raudenbush, S., & Bryk, A. S. (1986). A hierarchical model for studying school effects. Sociology of Education, 59(1), 1–17.
View in Google Scholar
DOI: https://doi.org/10.2307/2112482
Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037.
View in Google Scholar
DOI: https://doi.org/10.1086/261420
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.
View in Google Scholar
DOI: https://doi.org/10.3102/0013189X10363170
Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51(1), 1–17.
View in Google Scholar
Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95(6), 323–332.
View in Google Scholar
DOI: https://doi.org/10.1080/00220670209596607
Taiwo, A. A., & Tyolo, J. B. (2002). The effect of pre-school education on academic performance in primary school: A case study of grade one pupils in Botswana. International Journal of Educational Development, 22(2), 169–180.
View in Google Scholar
DOI: https://doi.org/10.1016/S0738-0593(01)00020-7
Teegarden, L. (1932). Clinical identification of the prospective non-reader. Child Development, 3(4), 346–358.
View in Google Scholar
DOI: https://doi.org/10.2307/1125361
Teese, R. V. (1995). Who wins at school? Boys and girls in Australian secondary education. Australian Government Publishing Service.
View in Google Scholar
Thamtanajit, K. (2020). The impacts of natural disaster on student achievement: Evidence from severe floods in Thailand. The Journal of Developing Areas, 54(4), 129–143. https://doi.org/10.1353/jda.2020.0042
View in Google Scholar
DOI: https://doi.org/10.1353/jda.2020.0042
Toews, G., & Vézina, P. L. (2022). Resource discoveries, FDI bonanzas, and local multipliers: Evidence from Mozambique. Review of Economics and Statistics, 104(5), 1046–1058. https://doi.org/10.1162/rest_a_00999
View in Google Scholar
DOI: https://doi.org/10.1162/rest_a_00999
Tolosa, I., Bayona, J. M., & Albaigés, J. (1996). Aliphatic and polycyclic aromatic hydrocarbons and sulfur/oxygen derivatives in northwestern Mediterranean sediments: Spatial and temporal variability, fluxes, and budgets. Environmental Science & Technology, 30(8), 2495–2503.
View in Google Scholar
DOI: https://doi.org/10.1021/es950647x
Van De Werfhorst, H. G., Kessenich, E., & Geven, S. (2022). The digital divide in online education: Inequality in digital readiness of students and schools. Computers and Education Open, 3, 100100. https://doi.org/10.1016/j.caeo.2022.100100
View in Google Scholar
DOI: https://doi.org/10.1016/j.caeo.2022.100100
Woessmann, L., & West, M. (2006). Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS. European Economic Review, 50(3), 695–736.
View in Google Scholar
DOI: https://doi.org/10.1016/j.euroecorev.2004.11.005
Wößmann, L. (2003). Specifying human capital. Journal of Economic Surveys, 17(3), 239– 270. https://doi.org/10.1111/1467-6419.00195
View in Google Scholar
DOI: https://doi.org/10.1111/1467-6419.00195
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