Electrical and Computer Engineering ETDs

Publication Date

Summer 7-10-2024

Abstract

This thesis presents a system for analyzing student activities during class sessions to gain insights into the learning process. A dataset consisting of 14 screen recordings, with 2 videos labeled across two stages, was used for training, validation, and testing. The methodology employs adaptive sampling, initially at 10 frames per minute to identify active regions, followed by detailed analysis at 1 frame per second using OCR to detect typing activities through character changes. The results demonstrate over a 50% reduction in computational load while maintaining high accuracy in detecting student engagement. A total of 26 hours of screen capture videos were processed, with AWS Textract achieving 87% accuracy, Tesseract 80%, EasyOCR 76%, and MMOCR 72%. By analyzing activity and typing patterns, the system significantly reduces the need for manual video review, providing valuable insights into how students interact with computers, the time spent by each group, and periods of engagement, thereby enhancing our understanding of student interaction dynamics.

Keywords

Video Analysis, Screen Recordings, Adaptive Sampling, Student Engagement, Multilayered Dataset, Typing Detection

Document Type

Thesis

Language

English

Degree Name

Computer Engineering

Level of Degree

Masters

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Marios Pattichis

Second Committee Member

Ramiro Jordan

Third Committee Member

Venkatesh Jatla

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