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Flipped Classroom Video Analytics

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2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Alternative Teaching Methods

Tagged Division

Engineering Technology

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Paper Authors


Rob Garrick Rochester Institute of Technology (CAST)

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Robert D. Garrick, Ph.D., P.E., is a Professor in the Department of Manufacturing and Mechanical Engineering Technology at the Rochester Institute of Technology (RIT) and Associate Department Chair. Garrick worked for 25 years in automotive engineering research and holds seven U.S. patents.

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Rising tuition and stringent accreditation standards increase the pressure educators feel to improve student learning using active teaching techniques. The flipped classroom allows educators to use externally viewed videos to focus on in-class time for problem-based learning. The flipped classroom is a recent pedagogical model in Engineering Technology classes Video learning analytics are an important tool to understand student engagement and usage of flipped classroom video resources. Video analytics can also help instructors understand and continuously improve the effectiveness of the video lecture class component. This research used video analytics of a Fluid Mechanics & Fluid Power Engineering Technology flipped class to determine who, where, when, and on what devices students watch flipped classroom videos over multiple semesters/sections. Results show a very high percentage of students watch the video before class, most students watch in close proximity to the campus (using IP address analysis), within one/two days before the class, some students re-review sections of videos before tests, students use multiple mobile and desktop devices that at times shift during the semester. Video analytic analysis allows the instructor to adjust video content knowing who, where, when and how these flipped classroom videos are used by students.

Garrick, R. (2018, June), Flipped Classroom Video Analytics Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30526

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