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Continuous Evaluation of Video Lectures from Real-Time Difficulty Self-Report

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Published:02 May 2019Publication History

ABSTRACT

With the increased reach and impact of video lectures, it is crucial to understand how they are experienced. Whereas previous studies typically present questionnaires at the end of the lecture, they fail to capture students' experience in enough granularity. In this paper we propose recording the lecture difficulty in real-time with a physical slider, enabling continuous and fine-grained analysis of the learning experience. We evaluated our approach in a study with 100 participants viewing two variants of two short lectures. We demonstrate that our approach helps us paint a more complete picture of the learning experience. Our analysis has design implications for instructors, providing them with a method that helps them compare their expectations with students' beliefs about the lectures and to better understand the specific effects of different instructional design decisions.

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    • Published in

      cover image ACM Conferences
      CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
      May 2019
      9077 pages
      ISBN:9781450359702
      DOI:10.1145/3290605

      Copyright © 2019 ACM

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      Publication History

      • Published: 2 May 2019

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      CHI '19 Paper Acceptance Rate703of2,958submissions,24%Overall Acceptance Rate6,199of26,314submissions,24%

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