Volume 13, Number 04, February 2023

Analyzing and Personalizing the Learning Performance for Special Needs Students Usingmachine Learning and Data Analytics

  Authors

Eric Xiong¹, Yu Sun², ¹Crean Lutheran High school, USA, ²California State Polytechnic University, USA

  Abstract

We design a server-client system that collects students’ engagement information and reports it to a centralized server to help teachers assist neurodivergent students in order to provide a visual representation of students’ engagement status aiming to promote an equal learning opportunity for neurodivergent students [6]. In recent years, everyone throughout the globe are all seeking higher education, either for themselves, or for their children. Students are learning an increasing amount in classes and have needed to spend a lot more effort and attention to succeed. In this race for higher education, a specific group of underrepresented minorities has been left behind. This group being the neurodivergent population, specifically high-functioning people with ASD(Autism Spectrum Disorder) [7]. These students often require more attention due to hypersensitivity, and a shorter attention span than the neurotypical populace. These students have all that's necessary to learn and understand the material, although teachers are often stuck to a faster pace curriculum that does not easily allot so much attention to a singular student. Due to this problem many teachers believe that a efficient way to passively gauge these students attentiveness would significantly benefit their education. This paper develops a server-client system that collects students’ engagement information and reports it to a centralized server to help teachers assist neurodivergent students in order to provide a visual representation of students’ engagement status aiming to promote an equal learning opportunity for neurodivergent students. We applied our application to [Class] and conducted an Evaluation of the approach based on the qualitative data collected from the students.

  Keywords

Facial features, information collection, Education