GRM 2010 GRM 2011

Abstract Details

 
AUTHOR NAME
 
Family Name:
Afana
 
First Name:
Yasin
 
ABSTRACT OF PAPER
 
Title of Paper:
Common Characteristics of High-Performing Schools in the Arab Gulf: Evidence from PIRLS and TIMSS 2011
 
Paper Proposal Text :
Common Characteristics of High-Performing Schools in the Arab Gulf:
Evidence from PIRLS and TIMSS 2011

Objectives
Worldwide, decision-makers and in general people involved in the education sector more specifically are increasingly interested in identifying those school factors associated with high academic performance, as a means to improving education in their countries. Particularly, the interest is in studying the mechanisms through which schools ‘as teaching-learning units’ enable their students to achieve and perform better - at least in the basic subjects (i.e., Reading and Mathematics). In other words, the focus is on studying what makes schools successful in achieving their core objectives (Scheerens, 1999).

In practice, most schools aspire to be high-performing in terms of student achievement in core subjects. It is well established in the literature that student achievement is explained to a great extent by differences in student background characteristics, such as socio-economic status (SES), gender, previous achievement, and ethnicity. Thus, to compare schools fairly, the outcomes that reflect goal attainment (i.e., performance) should be adjusted for these characteristics. This approach is known as “value-added”. Value-added models attempt to measure the impact of a school on student learning, after accounting for factors that cannot be influenced by schools, but that have been shown to have an impact on the learning process (Scheerens, 1992).

The Arab Gulf countries have diverse schooling systems. This heterogeneity can be, at least in part, explained by a high rate of expats, and by the country’s policy to cope with that. In this context, one can expect high variation between schools rather than within schools, as is the case in most countries. Therefore examining students’ achievement in reading and mathematics as a function of school characteristics, while controlling for students’ background, can be considered as an important trend in studying school differences. In addition to that, as schools are the units around which most formal education systems are organized, it will be of particular interest to policy-makers in the Gulf who are responsible for making decisions regarding the schooling system.

The importance of the current study is that it can inform the decision-making regarding the provision of school resources and practices by providing empirical evidence of how schools in their countries are characterized, and how these characteristics are associated with student performance in reading and mathematics.

In this paper, the authors intend to answer the following research questions:

- Which school factors are significantly associated with reading and mathematics achievement in the Arab Gulf’s High-Performing schools?
- Which of those factors are associated with high student achievement, even after adjusting for the effect of students’ home and background factors, in the Gulf’s High-Performing schools?



Theoretical framework
Our theoretical framework is based, on the one hand on the general model of school learning developed by Carroll (1963), which posits three factors as being internal to the learner (i.e., aptitude, ability and perseverance) and two external factors, namely opportunity to learn and quality of instruction. On the other hand, it is based on the body of research known as effective schools, developed by Scheerens, J. (1992, 1999), among others. Additionally, we take some elements from the model developed by Keeves (1972, 1992), whereby the curriculum is divided into three components (i.e., the intended, the implemented and the achieved curriculum), which in turn are influenced by the antecedent and the contextual factors operating at the systematic, school, classroom and student levels respectively. In this paper we focus on the implemented and achieved curriculum at the school and student levels.
Data sources
The data used in this research were collected in four Arab Gulf countries (namely; Oman, Qatar, Saudi Arabia, and United Arab Emirates) as part of TIMSS and PIRLS 2011. In addition to collecting student achievement data based on reading and mathematics, information was collected from students, their parents, their teachers and their school principals by way of background questionnaires. Only data from 4th grade students is considered in this work.
In order to answer the above mentioned research questions, a number of school level variables as well as variables to control for the socio-economic background of students and schools have been used. As the background questionnaires provide more than a single variable to measure each concept, principal component analysis was used to derive composite measures for the analyses rather than individual variables. Accordingly, internal consistency procedures were implemented to assure the stability and reliability of the composites. For example, the school questionnaire contains 13 questions that pertained to principal leadership features. On the basis of a principal component analysis, all or part of these variables were combined to provide an index of leadership: “Principal-Leadership Index”. Details on the statistical procedures used to this end are not included in this proposal, but are available upon request.

Methods
The first stage of the analysis consisted in discriminating between High-Performing and Low-Performing schools. To this end, first students’ performance was aggregated to the school level. Then the schools were divided into high-performing and low-performing groups based on school average achievement in reading and mathematics. Schools in the top third of the distribution in the country were categorized as High-Performing schools and coded as ‘1’. In contrast, the bottom third were categorized as Low-Performing schools and coded as ‘0’. The middle portion of the distribution was not used, and therefore was coded as ‘missing’. After that, variables and/or indicators were identified that distinguished between the two groups. Each variable and index at the school level was dichotomized to maximize the discrimination between the two groups of schools. Subsequently, a Pearson's chi-squared test analysis was applied on the one hand, to examine the dependency between school level indicators and the two groups, and to test the significance of the association between the groups and the different categories. Only those indicators that were found to be significantly associated with high-performing schools, but not with low-performing ones, were considered as ‘common characteristics of High-Performing schools’, and were retained for further analysis in the second stage.
The second stage of analyses was aimed at identifying those factors that remain significantly associated to a high student performance after controlling for student background characteristics. In this stage researchers only used one third of the original dataset. That is, those schools categorized as High-Performing. At this stage, the data were reduced at variables level too. From the first stage analyses, the school variables and factors associated only with High-Performing schools in addition to students’ home background were kept. This second stage of the analysis used hierarchical linear modeling techniques (HLM; Raudenbush & Bryk, 2002) as its main tool. In this stage, three sets of multi-level models were used:

- A fully unconditional model is fitted to estimate the variance components between-schools and within-schools, before and after data reduction.

- An initial two-level model included students’ home and background data at the first and school characteristics factors at the second level.

- A final model, where those school factors for which the effects were considered to be not significant after adjusting statistically for the effect of students’ home background are removed from the analysis. The model was re-estimated with only the significant effects retained.


References

Carroll, J. P. (1963). A model of school learning. Teachers College Record, 64(8), 723-3.

Ina V.S. Mullis, Michael O. Martin, Pierre Foy, and Kathleen T. Drucker (2012). PIRLS 2011 International Results in Reading. Chestnut Hill, MA, USA

Ina V.S. Mullis, Michael O. Martin, Pierre Foy, and Alka Arora (2012). TIMSS 2011 International Results in Mathematics. Chestnut Hill, MA, USA

Keeves, J. P. (1972). Educational Environment and Student Achievement. Stockholm: Almqvist and Wiksell.

Keeves, J. P. (1992). Learning Science in a Changing World: Cross-national Studies of Science Achievement: 1970 to 1984. The Hague: IEA.

Martin, M. O., Mullis, I. V.S., Gregory, K. D., Hoyle, C., & Shen, C. (2000). Effective schools in science and mathematics. Chestnut Hill, MA, USA

Scheerens, J. (1999). School effectiveness in developed and developing countries; a review of the research evidence.

Scheerens, J. (1992). Effective Schooling, Research, Theory and Practice. London: Cassell.
 
 
 

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