Project description

This PhD project explores the role of video structure in influencing learning outcomes as well as popularity. By studying structural elements of videos through cognitive psychology, event segmentation theory, and basic shapes of narratives, the project examines how these impact measures of learning and video popularity. The research combines traditional psychological methods with big data and machine learning techniques to gain comprehensive insights into video impact.

Online video content consumption, including educational, entertaining, and social media videos, holds significant relevance in today's society. As such, this PhD project delves into how video structure can influence video impact. The research is subdivided into two primary areas. The first part investigates how structural elements in educational videos affect learning outcomes. It utilizes event segmentation theory which posits that people break down events into units to comprehend them. This theory is employed to get an understanding of video structure. It combines this information with the learning outcome analysis to gain valuable insights into the impact of video structure on learning success.
The project's second aspect addresses the impact of narrative video structure on the popularity and reach of TikTok short videos. It employs an approach derived from narrative theory and Kurt Vonnegut's six basic shapes of narratives to categorize video content into emotional narrative arcs. In order to obtain these narrative arcs, machine learning techniques are employed.
In conjunction, this project seeks not only to deepen the understanding of video structure's influence on video impact but also to show how machine learning and big data can enhance psychological research.