Term of Award
Master of Science in Computer Science (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Computer Science
Committee Member 1
Gursimran Singh Walia
Committee Member 2
Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, mini-lectures, case studies, or videos (Young et al., 2009). The tracking of these “drops” in attention can be crucial for accurate timing of these change-ups in activities. This allows for maximal attention and a greater amount of deeply learned material. Autonomously collected data can be also used either real-time or post-hoc to be able to alter the design and presentation of lectures. Being able to keep track of student attention is vital to be able to have confidence in the delivery of material. Even if lectures do not break up presentation slides with attention-raising activities, they can still show more important information during periods of high attention and less important information during periods of low attention. This area of research has applications both in in-person classrooms and in online learning environments, which is especially relevant now during the COVID-19 pandemic. For large in-person classrooms, or classrooms where students’ faces are obscured, such as behind computer monitors, this research area could prove invaluable.
Sanders, Andrew, "Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System" (2022). Electronic Theses and Dissertations.
Research Data and Supplementary Material