Миља Б. Вујачић, Институт за педагошка истраживања, Београд, Република Србија, имејл: mvujacic@ipi.ac.rs
Рајка С. Ђевић, Институт за педагошка истраживања, Београд, Република Србија
Смиљана M. Јошић, Институт за педагошка истраживања, Београд, Република Србија
Иновације у настави, XXXIII, 2020/2, стр. 15–28

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doi: 10.5937/inovacije2002015V

Резиме: Школска клима, као важан фактор школске ефективности, обухвата различите аспекте рада школе и има снажан утицај на целокупан процес учења и постигнуће ученика. Намера нам је била да секундарном анализом података из циклуса истраживања TIMSS 2015 утврдимо да ли је и како школска клима повезана са постигнућем ученика, демографским карактеристикама школе, образовањем директора и дужином ангажовања на тој функцији, школским ресурсима и понашањем ученика и наставника. Репрезентативни узорак обухвата 160 директора, 192 учитеља, 3911 родитеља и 3999 ученика четвртих разреда основних школа у Србији. Налази указују на то да на целокупном узорку није утврђена повезаност школске климе и постигнућа ученика из математике и природних наука. Издвајајући школе у две групе према критеријуму екстремних величина скорова школске климе, утврђено је да је постигнуће ученика из ова два предмета веће у школама у којима је клима изразито повољна, у поређењу са школама у којима је клима изразито неповољна. Школска клима је повезана са демографским варијаблама, те је повољнија у школама које се налазе у мањим местима, са мањим бројем становника и мањим бројем ученика, у којима је доступност компјутера већа и у којима су у мањој мери заступљени непримерени облици понашања ученика и наставника. Кључне педагошке импликације нашег истраживања односе се на неговање оних пракси у школама које доприносе стварању позитивне школске климе као што су превенција и решавање дисциплинских проблема, смањивање броја уписаних ученика и омогућавање приступа информационим технологијама наставницима. Указано је на ограничења истраживања и дате су препоруке за даља проучавања у овој области.
Кључне речи: школска клима, TIMSS 2015, постигнуће ученика, контекстуалне варијабле, секундарна анализа података.

Summary: 
School climate, as an important factor of school effectiveness, comprises different aspects of school work and strongly impacts the entire learning process, as well as student achievement. Our intention was to determine, using secondary data analysis from the research cycle of TIMSS 2015, whether and how school climate correlates with student achievement, demographic characteristics of schools, principals’ education and the length of their engagement in this capacity, school resources, and students’ and teachers’ behaviour. The representative sample comprised 160 school principals, 192 primary school teachers, 3911 parents, and 3999 fourth-graders from primary schools in Serbia. According to the research findings, no correlation was identified between school climate and student achievement in mathematics and science subjects. By categorising the schools in two groups according to the criterion of extreme values of the scores of school climate, it was determined that student achievement in these two subjects was higher in the schools with an exceptionally favourable school climate, compared to the schools with an extremely unfavourable school climate. School climate correlates with demographic variables, and it is more favourable in schools located in smaller towns, with fewer inhabitants and fewer students, where students have a better access to computers and where there are fewer examples of students’ and teachers’ inappropriate behaviour. The key pedagogical implications of our research refer to fostering school practices that contribute to creating a positive school climate, such as prevention and resolving discipline-related issues, decreased school enrolment, and providing easier access to information technologies for teachers. The authors of the paper elaborate on the limitations of this research and offer recommendations for further research in this area.
Кeywords: school climate, TIMSS 2015, student achievement, contextual variables, secondary data analysis.

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