Optical Character Recognition (OCR)-Based and Gaussian Mixture Modeling-OCR-Based Slide-Level "With-Me-Ness": Automated Measurement and Feedback of Learners' Attention State during Video Lectures

As video lectures are gaining more popularity, determining their effectiveness and obtaining valuable feedback have become necessary. To measure the learners' attention state during video lectures, we specified the conceptual "with-me-ness" (WMN) as slide-level WMN (SL-WMN). The content domain on each slide was automatically extracted via an optical character recognition (OCR)-based method, while the eye gazing behaviors were analyzed through a Gaussian mixture modeling (GMM) fixation clustering method. Both domain-specific WMN and behavior-enriched WMN were then computed via OCR- and GMM-OCR-based methods to measure the learners' attention levels. We conducted an experiment to collect in-lecture eye-tracking data, video recordings, and post-lecture test scores from 50 Grade 8 students. The results demonstrated that both OCR- and GMM-OCR-based SL-WMNs are reliable and compatible automatic measurements of learners' attention states during video lectures. A survey from participating learners and lecturers also revealed highly favorable feedback for the developed SL-WMNs.