MACHINE LEARNING CLASSIFICATION OF HEMODYNAMICS TO PREDICT SCIENCE STUDENT LEARNING OUTCOMES IN REAL-TIME DURING VIRTUAL REALITY EAND ONLINE LEARNING SESSIONS
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Date
2022-05-04
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Authors
Linder, Kayleigh A
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Publisher
East Carolina University
Abstract
Students' learning results in science content and practices are expected to be improved through automated interactive learning management systems and linked online video-based learning environments. The goal of this study is to see how hemodynamic response data may be used to build student-level answer predictions using machine learning algorithms in a science classroom while students are using an online learning management system. A charter school in the northeastern United States was used to recruit 40 participants (n=40), 21 females and 19 males. Students viewed a recorded film that included a 20-minute instruction and explanation of the DNA replication process. A female educator on a computer screen presented an overview of the DNA replication process during class. The findings illustrate those hemodynamic responses seen during topic presentations accurately predict student replies to subject-related questions. The results imply that hemodynamic response can be used to gauge degrees of student involvement in video-based tasks, with error rates in the predictive models below 30%. This could lead to the development of unique visual media assessment methodologies, allowing educators to assess whether students can comprehend the material.