University of the Peloponnese, Greece
* Corresponding author
University of the Peloponnese, Greece
University of the Peloponnese, Greece

Article Main Content

This study examines the academic performance and demographic characteristics of Greek students in elementary and secondary school. It is an extension of an earlier study that was done in a different time frame to support related conclusions. The dataset includes all students in the last two grades of primary school and the first three years of secondary school. The academic success levels, as well as their longitudinal dimension and divergence by demographic factors, were identified. The results of our previous study, which showed that there are four consistent levels of academic achievement across time, were confirmed by the current research. In addition, the significance of demographic characteristics such as gender and guardian occupation were verified. Last but not least, the importance of early identification of low-performing students was emphasized, as was the likelihood of a significant improvement in their performance. We think that one of the biggest problems with making good educational policies is that it's hard to help students who aren't performing well.

References

  1. Papadogiannis I, Wallace M, Poulopoulos V, Karountzou G, Ekonomopoulos D. A First Ever Look into Greece’s Vast Educational Data: Interesting Findings and Policy Implications. Education Sciences. 2021;11(9):489.
     Google Scholar
  2. Coleman, J. Equality of Educational Opportunity; U.S. Department of Health, Education, and Welfare. U.S. Government Printing Office: Washington, DC.1966.
     Google Scholar
  3. Bourdieu P. Cultural Reproduction and Social Reproduction. In. Power and Ideology in Education. J. Karabel, & A. H. Halsey. Oxford University Press. 1977, pp. 487–511.
     Google Scholar
  4. Islam S; Baharun H, Muali C,Ghufron I; Bali I. Wijaya M, Marzuki, I. To Boost Students' Motivation and Achievement through Blended Learning. J. Phys. Conf. Ser. 2018,1114, 012046.
     Google Scholar
  5. Ozen O, The Effect of Motivation on Student Achievement. In The Factors Effecting Student Achievement. 2017.Springer: Cham, Switzerland, pp. 35–56.
     Google Scholar
  6. Marsh W; Pekrun R, Murayama K, Arens K, Parker. D; Guo J,Dicke, T. An integrated model of academic self-concept development: Academic self-concept, grades, test scores, and tracking over 6 years. Dev. Psychol. 2018;54:263–280.
     Google Scholar
  7. Lai L, Hwang J. A self-regulated flipped classroom approach to improving students' learning performance in a mathematics course. Comput. Educ. 2016;100:126–140.
     Google Scholar
  8. Cvencek D, Fryberg A, Covarrubias R, Meltzoff. N. Self-Concepts, Self-Esteem, and Academic Achievement of Minority and Majority North American Elementary School Children. Child Dev. 2018;89:1099–1109.
     Google Scholar
  9. Yang Q, Tian L, Huebner S, Zhu X. Relations among academic achievement, self-esteem, and subjective well-being in school among Elementary school students: A longitudinal mediation model. Sch. Psychol. 2019;34:328–340.
     Google Scholar
  10. Geller J. Toftness. R,Armstrong I, Carpenter S. K, Manz, C.L, Coffman C.R.; Lamm M.H. Study strategies and beliefs about learning as a function of academic achievement and achievement goals. Memory 2018;26:683–690.
     Google Scholar
  11. Day C, Gu Q, Sammons P. The Impact of Leadership on Student Outcomes. Educational. Administration. 2016;52: 221–258.
     Google Scholar
  12. Ohlson, M.; Swanson, A.; Adams-Manning, A.; Byrd, A. A Culture of Success—Examining School Culture and Student Outcomes via a Performance Framework. J. Educ. Learn. 2016;5:114.
     Google Scholar
  13. Konold T, Cornell D, Jia Y, Malone M. School Climate, Student Engagement, and Academic Achievement: A Latent Variable, Multilevel Multi-Informant Examination. AERA Open 2018;4:233285841881566.
     Google Scholar
  14. de Boer H. Timmermans A.C, van der Werf C. The effects of teacher expectation interventions on teachers' expectations and student achievement: Narrative review and meta-analysis. Educ. Res. Eval. 2018;24:180–200.
     Google Scholar
  15. Sebastian J, Moon M, Cunningham M. The relationship of school-based parental involvement with student achievement: A comparison of principal and parent survey reports from PISA 2012. Educ. Stud. 2017;43:123–146.
     Google Scholar
  16. Karadag E. The Factors Effecting Student Achievement—Meta-Analysis of Empirical Studies. Springer: Cham, Switzerland, 2017.
     Google Scholar
  17. Baker R. S, Yacef K. The state of educational data mining in 2009: A review and future visions. JEDM Educ. Data Min. 2009;1:3–17.
     Google Scholar
  18. Romero C, Ventura S. Educational Data Mining: A Review of the State of the Art. IEEE Trans, 2010;40:601–618.
     Google Scholar
  19. Papamitsiou Z, Economides A. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educ. Technol. Soc. 2014;17:49–64.
     Google Scholar
  20. Baker R. S, Inventado PS. Educational Data Mining and Learning Analytics. In Learning Analytics; Larusson J, White B.Eds.; Springer: New York, NY, USA, 2014:61–75.
     Google Scholar
  21. Dutt A, Ismail MA, Herawan T. A. Systematic Review on Educational Data Mining. IEEE Access 2017;5:15991–16005.
     Google Scholar
  22. Papadogiannis I, Poulopoulos V, Wallace M. A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen. Int. J. Educ. Res. Rev. 2020;5:353–372.
     Google Scholar
  23. Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020;10(3):e1355.
     Google Scholar
  24. Prieto L P, Sharma K, Dillenbourg P. Studying teacher orchestration load in technology-enhanced classrooms, In Design for Teaching and Learning in a Networked World, ser. Lecture Notes in Computer Science, G. Conole, T. Klobuar, C. Rensing, J. Konert, and E. Lavou, Eds. Springer International Publishing, 2015;9307:268–281.
     Google Scholar
  25. Daniel Ben Kei. Big Data and data science: A critical review of issues for educational research. British Journal of Educational Technology, 2019;50.1:101–113.
     Google Scholar
  26. Romero C, Ventura S. Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2017;7.1: e1187.
     Google Scholar
  27. Lang C, Siemens G, Wise A, Gasevic, D. The Handbook of Learning Analytics; Society for Learning Analytics Research (SoLAR): Ann Arbor, MI, USA, 2017.
     Google Scholar
  28. Pelleg D, Moore A. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 15–18 August 1999; Association for Computing Machinery: San Diego, CA, USA, 1999;1:727–734.
     Google Scholar
  29. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union L 119, 2016, 1–88.
     Google Scholar
  30. Duda R.O, Hart P.E, Stork D.G. Pattern Classification, 2nd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2001.
     Google Scholar
  31. ILO. International Standard Classification of Occupations 2008: ISCO-08. International Labour Office: Geneve, Switzerland, 2012.
     Google Scholar
  32. Domnech - Betoret F, Abelln R. Self-Efficacy, Satisfaction, and Academic Achievement: The Mediator Role of Students' Expectancy-Value Beliefs. Front. Psychol. 2017;8:1193.
     Google Scholar
  33. Rezaeinejad M, Azizifar A, Gowhary H. The Study of Learning Styles and its Relationship with Educational Achievement among Iranian High School Students. Procedia-Soc. Behav. Sci. 2015;199:218–224.
     Google Scholar