The relative effect of student, family and school-related factors on math achievement by location of the school

Abstract

This study aims to examine the relative effects of student, family and school-related characteristics on 4th grade students’ math achievement according to location of the school in Turkey. The data of 6435 students studying at 260 primary schools were analyzed using TIMSS-2015 database. The dependent variable of the study was students’ math scores and 19 factors constituting the student, family and school-related characteristics were the independent variables. The location of the school was classified as urban, suburban, medium-size city and village. The data was analyzed via single level multiple linear regression. The results revealed that the entire models explained the largest amount of variance (52%) in the schools located in the villages and the least amount of variance (44%) in those located in the urban area. Although all of the student, family and school-related characteristic sets were found to be significantly related with the achievement, the student-related characteristics explained the largest amount of variance in achievement. Students’ confidence in math contributed almost the highest amount of variance, and the early numeracy tasks, absenteeism in school, parents’ highest education level, parents’ highest occupation level, early numeric activities before school explained small amounts of variance in students’ math achievement in the schools of all locations.

Analysis of Science Learning Achievement Based on Parents' Socio-Economic Status and Students' Interest in Science Learning

“…The data was obtained based on daily, mid-semester and semester test scores during online learning from home. An overview of the socioeconomic status of Class VII A MTS students Haqqul Yaqien NW Sayang-Sayang during the covid-19 pandemic Socio-economic status Socio-economic status usually includes the social status given by the community, economic status, parents' occupations, and many others related to the socioeconomic status of people (Tomul, et al, 2021). So that one of the most decisive factors for differences in student achievement is the education level of parents and the highest family income (Chevalier & Lanot, 2002).…”

Machine Learning Method for High-Dimensional Education Data

“…When we examine the selected important predictors ranked by the ML procedures, we confirmed that all the predictors are theoretically supportive as predictors for students' learning outcome, such as students' previous learning outcomes (Hailikari, Nevgi, & Komulainen, 2008), absence of classes (Gottfried, 2009), family relationships (Roksa & Kinsley, 2019;Tomul, Önder, & Taslidere, 2021), class failures (Salal, Abdullaev, & Kumar, 2019), and study time (Xu, 2022).…”

Why is the mastery of key competencies in mathematics for Indonesian students low? : Re-analysis of PISA 2012

“…Learning trajectories provide insight into students' thinking and learning in mathematics. It demonstrates the student's learning process through a series of tasks and promotes the development of psychological processes and students' thinking levels (Duschl et al, 2011;Jacobs, Mhakure, Fray, Holtman, & Julie, 2014;Tomul, Önder, & Taslidere, 2021). The learning trajectory plays an important role in the process of selecting learning objectives and learning methods.…”