Abstract

The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making.

Highlights

  • Identification of educational factors associated with academic performance is a key aspect in educational research into school effectiveness (Rutter and Maughan, 2002; Murillo, 2007; Muijs et al, 2014; Creemers and Kyriakides, 2015)

  • Multilevel models with contextual variables applied to OECD countries based on PISA 2009 data show that Spain is one of the countries with the smallest difference between observed and estimated scores in both reading and mathematics (Lenkeit and Caro, 2014)

  • The intraclass correlation coefficient (ICC) of the final models achieved acceptable levels, since in the three models the variance explained at school level accounted for more than 50% of the total variance

Read more

Summary

Introduction

Identification of educational factors associated with academic performance is a key aspect in educational research into school effectiveness (Rutter and Maughan, 2002; Murillo, 2007; Muijs et al, 2014; Creemers and Kyriakides, 2015) Within this context, we propose an innovative approach to the analysis of good educational practices associated with school effectiveness. In contrast to traditionally used techniques (inferential and multivariate correlational statistics), data mining is not based on previous assumptions or theoretical distributions to obtain predictive models. These techniques are applied with minimal intervention by researchers, which, together with the aforementioned, represent a great advantage for the identification of valuable information in massive databases (Xu, 2005). The algorithm proposed in this study is the decision tree (classification algorithm), since it simplifies the analysis and interpretation of the predictor variables and their relationships (Martínez-Abad and Chaparro-Caso-López, 2017)

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call