Classifying Students' Age-Group based on Technology's Opinions for Real-Time Automated Web Applications

The identification of students' demographic features is a promising problem in educational data mining. Considering the issue of predicting the age-group of students towards their opinion about technology, we analyzed 162 primary samples with 37 features from Indian University collected in 2017-2018. We used the binary Logistic Regression (LR) classification approach with feature selection based on the chi(2), and Wald Statistics to classify the age group. The responses towards three features, "Prepare class lessons", "Smart classroom", and "High-speed Internet with wi-fi" were found significant to accurately classify (73.9%) the age-group of students. Our model can be helpful to make real-time automated web applications or mobile applications for age-group identification. Also, this model can be beneficial for Google classroom, Microsoft Forms, Google Forms, E-lection to identify the age-group of respondents after filling up their survey responses.