Lamb, RichardLinder, Kayleigh A2022-07-192022-07-192023-052022-05-04May 2023http://hdl.handle.net/10342/10817Students' 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.application/pdfscience student learningonline learningfunctional near-infrared spectroscopyHonors Thesis2022-07-12