Features Exposing Responses of Hungarian Students for the Real-Time

Abstract

Exploring the behavior of students towards technology is a promising job. Considering the problem, we used Correspondence Analysis on real data samples gathered from a Hungarian public university. We have identified student's likeness and dislikes with the four technology parameters: attitude, growth, use, and benefits. Being a powerful feature selection approach, it recommended many useful features to identify students' behavior about technology. We found a qualified bonding among technology use, growth, attitude, and benefit with the student's response. Moreover, we suggested to practically deploy this behavior approach identification model for our “E-lection,” a real-time student response system. The online behavioral association model might help management get aware of the student's behavior towards campus technology.