A novel web-based application to identify multiple intelligence types of high school students and examining the impact of differentiated homework on academic performance

This study aimed to develop and implement a web-based system integrating multiple intelligence assessment, student profiling, differentiated mathematics assignments, monitoring, and reporting processes, and to examine changes in students' academic performance. The study was conducted in a science high school in Genç district of Bingöl province during the 2024-2025 academic year, focusing on the topic of "Statistical Research Process" within the scope of ninth-grade mathematics lessons. A quasi-experimental pre-test-post-test control group design was used with a total of 60 students, 30 in the experimental group and 30 in the control group. The dominant intelligence types of the students in the experimental group were determined by analyzing data obtained from the Multiple Intelligence Survey administered via the developed web-based platform, using an algorithm developed within the scope of the research. Subsequently, differentiated mathematics performance assignments were given to the students via the same system according to their determined dominant intelligence types, while the control group continued with their existing teaching and assessment practices. Academic performance scores obtained before and after the application were used to examine changes within and between the groups. Parametric and non-parametric statistical methods were selected according to the distribution characteristics of the data. The findings showed a descriptive increase in academic performance in the experimental group, but within-group variation was not statistically significant. While a decrease in academic performance was observed in the control group, a comparison of the change scores showed a statistically significant difference between the experimental and control groups. The study demonstrates an applied approach to integrating student assessment, intelligence profiling, differentiated assignment, monitoring, and reporting in a web-based learning environment and provides a foundation for further research on technology-assisted differentiated learning.