SavedAdd your journal

Journals / International Journal of Assessment Tools in Education / 2020 / Cilt: 7 - Sayı: 4

Spatial Models for Identifying Factors in Student Academic Achievement

Hüseyin Koçak
Filiz Akbaş Yeşilyurt
M. Ensar Yeşilyurt
Pages
735–752
Publication Date
2022-10-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We observed an interaction between students who live in the same neighbourhood and found evidence of an interaction among students in terms of their achievement based on a spatial error model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the literature, estimation results of the determinants of academic achievement are controversial.
📊
Key Findings & Data:There may be several reasons for these controversial results, including sample or cultural differences.
💡
Conclusion & Impact:We observed an interaction between students who live in the same neighbourhood and found evidence of an interaction among students in terms of their achievement based on a spatial error model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Student achievement · Interaction · Spatial models

Abstract

In the literature, estimation results of the determinants of academic achievement are controversial. There may be several reasons for these controversial results, including sample or cultural differences. Conversely, these results may arise from ignoring certain important facts, such as an interaction effect. Some studies do not consider interactions among students, and some studies may not use effective models. Surprisingly, very few studies have focused on student academic achievement using spatial models, which may be one of the most suitable models for testing interaction effects. In this study, we estimated student achievement using spatial models and a data sets from Turkey. We observed an interaction between students who live in the same neighbourhood and found evidence of an interaction among students in terms of their achievement based on a spatial error model.

Özet

In the literature, estimation results of the determinants of academic achievement are controversial. There may be several reasons for these controversial results, including sample or cultural differences. Conversely, these results may arise from ignoring certain important facts, such as an interaction effect. Some studies do not consider interactions among students, and some studies may not use effective models. Surprisingly, very few studies have focused on student academic achievement using spatial models, which may be one of the most suitable models for testing interaction effects. In this study, we estimated student achievement using spatial models and a data sets from Turkey. We observed an interaction between students who live in the same neighbourhood and found evidence of an interaction among students in terms of their achievement based on a spatial error model.

🔖

Cite This Article

Koçak, H., Yeşilyurt, F. A., Yeşilyurt, M. E. (2022). Spatial Models for Identifying Factors in Student Academic Achievement. International Journal of Assessment Tools in Education, Cilt: 7 - Sayı: 4, 735–752. https://ajindex.com/articles/540148

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Aaronson, D. (1998). Using sibling data to estimate the impact of neighborhoods on children’s educational outcomes. Journal of Human Resources, 33, 915 946. https://doi.org/10.2307/146403
  2. Adejoro, O. E. (2016), Does location also matter? A spatial analysis of social achievements of young south Australians [Unpublished Master Thesis]. Department of Physical Geography and Ecosystem Science, Lund University, Lund, 2016.
  3. Akerhielm, K. (1995). Does class size matter?. Economics of Education Review, 14(3), 229-241. https://doi.org/10.1016/0272-7757(95)00004-4
  4. Akerlof, G. A. (1997). Social distance and social decisions. Econometrica, 65(5), 1005-1027. https://doi.org/10.2307/2171877
  5. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer Science & Business Media. Aslund, O., Edin, Per-Anders, Fredriksson, P., & Grönqvist, H. (2011). Peers, neighborhoods, and immigrant student achievement: evidence from a placement policy. American Economic Journal: Applied Economics, 3(2), 67-95. https://doi.org/10.1257/app.3.2.67
  6. Bayer, P., Hjalmarsson, R., & Pozen, D. (2009). Building criminal capital behind bars: Peer effects in juvenile corrections. The Quarterly Journal of Economics, 124(1), 105–147. https://doi.org/10.3386/w12932
  7. Bernheim, B. D. (1994). A theory of conformity. Journal of Political Economy, 102, 841-877. https://doi.org/10.1086/261957
  8. Blatchford, P., & Mortimore, P. (1994). The issue of class size for young children in schools: What can we learn from the research?. Oxford Review of Education, 20(4), 411-428. https://doi.org/10.1080/0305498940200402
  9. Booth, A., & Kee, H. (2006). Birth order matters: The effect of family size and birth order on educational attainment. Journal of Population Economics, 22(2), 367 397. https://doi.org/10.1007/s00148-007-0181-4
  10. Brannstrom, L. (2008). Making their mark: The effects of neighbourhood and upper secondary school on educational achievement. European Sociological Review, 24(4), 463–478. https://doi.org/10.1093/esr/jcn013
  11. Bray, M. (2013). Shadow education: comparative perspectives on the expansion and implications of private supplementary tutoring. Procedia - Social and Behavioral Sciences, 77(22), 412-420. https://doi.org/10.1016/j.sbspro.2013.03.096
  12. Brock, W. A., & Durlauf, S. N. (2001). Discrete choice with social interactions. The Review of Economic Studies, 68(2), 235-260. https://doi.org/10.1111/1467-937X.00168
  13. Bronfenbrenner, U. (1994). Ecological models of human development. Readings on the Developme