Profile variables of high- and low-performing schools: Discriminating factors of mathematics performance

Julius Serquinia Valderama

doi:10.32890/jcia2022.1.2.5

Authors

Julius Serquinia Valderama Nueva Vizcaya State University, Philippines https://orcid.org/0000-0002-8467-0540 (unauthenticated)

DOI:

https://doi.org/10.32890/jcia2022.1.2.5

Keywords:

discriminant analysis, discriminators, math modelling, mathematics performance, national achievement test

Abstract

The study aimed to identify profile variables that can discriminate the high-performing schools and low-performing schools based on the Mathematics test of the National Achievement Test results. Ten high schools each from high and low Mathematics performance groups were the study areas. Purposive sampling was considered in the study; all the principals and teachers from the high and low-performing schools were taken as principal- and teacher-respondents; simple random sampling was performed to identify student-respondents from the classes of each teacher-respondents. The researcher personally conducted the study using the three validated questionnaires to the 10 principals, 24 Mathematics teachers, and 500 students from the schools with high mathematics performance, and 10 principals, 41 Mathematics teachers, and 589 students from the schools with low Mathematics performance. The data gathered were analyzed using the pairwise correlation before the discriminant analysis of the SPSS. The analysis identified 18 out of 49 variables that could discriminate between the two groups of schools. Principals played big roles to attain and maintain the schools’ high Mathematics performance. Teachers’ number of training, attainment of Master’s degrees, class size, and the provisions for Mathematics textbooks, Activity Sheets, and a functional library were associated with schools’ high Mathematics performance. Educators and administrators could adopt the established discriminant function to identify the weaknesses of their schools’ mathematics programs and to have scientific-based decisions and interventions. This study did not only establish how the identified variables were related to students’ Mathematics performance, but it also showed how the influence of these variables to discriminate the high from low performing schools.

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