AI Summary & Key Takeaways
AI InsightResults showed that some students did not achieve sufficient gains to advance to higher performance levels.
📌 3 Key Takeaways
Abstract
In this study, gain score, and categorical growth models were used to examine the role of student (gender and socioeconomic level) and school characteristics (school size and school resources) in the student growth on comprehension skills in language. The participants of this study were 2,416 sixth-grade students in 2011 who became seventh-grade students in 2012. The data was collected through two achievement tests, student and school questionnaires. Two achievement tests were calibrated using the Rasch Model and were scaled using the concurrent estimation method. Moreover, the cut-off scores of these tests were determined by using the bookmark method. Students’ growth was modelled with the gain score and categorical growth models. All data was analyzed using multilevel models. Results showed that some students did not achieve sufficient gains to advance to higher performance levels. Although some schools’ average gains were higher, their performance was still not significant enough in terms of tests’ standards. Moreover, the analyses demonstrated that the student gain scores and growth categories varied significantly among the schools. In addition, the study was able to determine student and school characteristics that have an impact on the students' gain scores and categorical growth. Given the different aspects gained about students’ performance with these models, it is recommended to utilize different growth models in schools.
Özet
In this study, gain score, and categorical growth models were used to examine the role of student (gender and socioeconomic level) and school characteristics (school size and school resources) in the student growth on comprehension skills in language. The participants of this study were 2,416 sixth-grade students in 2011 who became seventh-grade students in 2012. The data was collected through two achievement tests, student and school questionnaires. Two achievement tests were calibrated using the Rasch Model and were scaled using the concurrent estimation method. Moreover, the cut-off scores of these tests were determined by using the bookmark method. Students’ growth was modelled with the gain score and categorical growth models. All data was analyzed using multilevel models. Results showed that some students did not achieve sufficient gains to advance to higher performance levels. Although some schools’ average gains were higher, their performance was still not significant enough in terms of tests’ standards. Moreover, the analyses demonstrated that the student gain scores and growth categories varied significantly among the schools. In addition, the study was able to determine student and school characteristics that have an impact on the students' gain scores and categorical growth. Given the different aspects gained about students’ performance with these models, it is recommended to utilize different growth models in schools.