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Comprehension of an audio versus an audiovisual lecture at 50% time-compression
Title
Comprehension of an audio versus an audiovisual lecture at 50% time-compression.
Creator
Perez, Nicole, Barenholtz, Elan, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Psychology
Abstract/Description
Since students can adjust the speed of online videos by time-compression which is available through common software (Pastore & Ritzhaupt, 2015), it is important to learn at which point compression impacts comprehension. The focus of the study is whether the speaker’s face benefits comprehension during a 50% compressed lecture. Participants listened to a normal lecture or a 50% compressed lecture. Each participant saw an audio and audiovisual lecture, and were eye tracked during the...
Show moreSince students can adjust the speed of online videos by time-compression which is available through common software (Pastore & Ritzhaupt, 2015), it is important to learn at which point compression impacts comprehension. The focus of the study is whether the speaker’s face benefits comprehension during a 50% compressed lecture. Participants listened to a normal lecture or a 50% compressed lecture. Each participant saw an audio and audiovisual lecture, and were eye tracked during the audiovisual lecture. A comprehension test revealed that participants in the compressed lecture group performed better with the face. Eye fixations revealed that participants in the compressed lecture group looked less at the eyes and more at the nose when compared to eye fixations for those that viewed the normal lecture. This study demonstrates that 50% compression affects eye fixations and that the face benefits the listener, but this much compression will still lessen comprehension.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004847, http://purl.flvc.org/fau/fd/FA00004847
Subject Headings
Learning--Case studies., Perceptual-motor learning., Nonverbal communication., Internet videos--Education.
Format
Document (PDF)
DEEP LEARNING OF POSTURAL AND OCULAR DYNAMICS TO PREDICT ENGAGEMENT AND LEARNING OF AUDIOVISUAL MATERIALS
Title
DEEP LEARNING OF POSTURAL AND OCULAR DYNAMICS TO PREDICT ENGAGEMENT AND LEARNING OF AUDIOVISUAL MATERIALS.
Creator
Perez, Nicole, Barenholtz, Elan, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description
Engagement with educational instruction and related materials is an important part of learning and contributes to test performance. There are various measures of engagement including self-reports, observations, pupil diameter, and posture. With the challenges associated with obtaining accurate engagement levels, such as difficulties with measuring variations in engagement, the present study used a novel approach to predict engagement from posture by using deep learning. Deep learning was used...
Show moreEngagement with educational instruction and related materials is an important part of learning and contributes to test performance. There are various measures of engagement including self-reports, observations, pupil diameter, and posture. With the challenges associated with obtaining accurate engagement levels, such as difficulties with measuring variations in engagement, the present study used a novel approach to predict engagement from posture by using deep learning. Deep learning was used to analyze a labeled outline of the participants and extract key points that are expected to predict engagement. In the first experiment two short lectures were presented and participants were tested on a lecture to motivate engagement. The next experiment had videos that varied in interest to understand whether a more interesting presentation engages participants more, therefore helping participants achieve higher comprehension scores. In a third experiment, one video was presented to attempt to use posture to predict comprehension rather than engagement. The fourth experiment had videos that varied in level of difficulty to determine whether a challenging topic versus an easier topic affects engagement. T-tests revealed that the more interesting Ted Talk was rated as more engaging, and for the fourth study, the more difficult video was rated as more engaging. Comparing average pupil sizes did not reveal significant differences that would relate to differences in the engagement scores, and average pupil dilation did not correlate with engagement. Analyzing posture through deep learning resulted in three accurate predictive models and a way to predict comprehension. Since engagement relates to learning, researchers and educators can benefit from accurate engagement measures.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013558
Subject Headings
Instruction, Effective teaching, Pupil (Eye), Posture, Deep learning, Engagement
Format
Document (PDF)