Video analytics in education: Investigation of behavioral patterns by content type
2023
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Advisor: Doç. Dr. Emine Şendurur
Abstract (EN)
The spread of information communication technologies has supported the development of video-based online learning environments. The interaction features added to videos have had a major impact on gathering information about online learners. Previous studies have identified principles and teaching strategies for online learning and video material preparation. Some studies have investigated how to extract data from video materials. A very limited number of studies have used this data to study how students behave and what behavioral patterns they exhibit when interacting with the material.This study aims to investigate this engagement from different content types: academic vs. non-academic. First, the interaction patterns were examined through logs and screen recordings. Second, they were interviewed to have insights into views about the contents. Forty high school students were involved in the study. The research design of the study is an explanatory sequential mixed design. Participants were exposed to two types of interactive video content. The interactive features and design principles did not vary across videos. The findings indicated that students have smoother video-watching experiences in non-academic content and are more successful in scores collected during the interactions. In general, students expressed their views positively, but some found academic content too challenging
Author
Dr. Cansu Gökrem
Institution
How to Cite
Cansu Gökrem (Master Thesis). Video analytics in education: Investigation of behavioral patterns by content type, 2023, Ondokuz Mayıs University.
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