Project Team
Students
Anirudh Ram
Computer Engineering
Penn State Erie, Penn State University Park
Faculty Mentors
Omar Ashour
Penn State Erie
Industrial Engineering
Brad Sottile
Penn State University Park
School of Electrical Engineering and Computer Science
Project
https://sites.psu.edu/mcreu/files/formidable/2/Final-Poster-1.pdf
Project Video
Project Abstract
Anirudh Ram
Campus Affiliations: Penn State Erie, Penn State University Park
Major: Computer Engineering; Minor: Mechatronics & Computer Science
Anticipated Graduation Date: May 2024
Faculty Advisors: Omar Ashour (Erie), Brad Sottile (University Park)
Project Title: Analyzing Facial Expressions to Measure Student Engagement
Student engagement has long been fostered in the realm of higher education and is widely considered to be an integral factor in learning success, and the expansion of a student’s knowledge base. Several studies show that alongside the effort and time spent learning a new skill, a student’s own willingness to learn and make progress also plays an important role in the educational process. Students who learn in an environment that enables them to be completely immersed and engaged in the learning process typically have a better understanding of concepts and have a higher rate of success than students who do not feel as engaged. In addition, student engagement serves as a valuable indicator for teachers who seek to further improve their teaching methods to better address the needs of their students.
This research topic intends to introduce an approach to recognizing and measuring student engagement. Evaluative tests, self-reflective questionnaires, and inconspicuous surveillance are some of the current ways to determine student engagement, however, such methods may result in a biased or inaccurate judgment of a student’s true level of engagement.
To effectively reduce bias and human error in measuring student engagement, this research places an emphasis on automation and therefore integrates concepts of machine learning for image processing and model improvement in conjunction with a camera for frame collection and facial detection. The system automatically separates collected facial data into a variety of engagement categories which can prove useful to the teacher/learning tool in evaluating/adjusting teaching methods.
Evaluate this Project
Use this form link to provide feedback to the presenters, and add your project evaluation for award(s) consideration.