Mudslide: A Spatially Anchored Census of Student Confusion for Online Lecture Videos

  • Elena L. Glassman ,
  • Juho Kim ,
  • Andrés Monroy-Hernández ,
  • Meredith Ringel Morris

Proceedings of CHI 2015 |

Published by ACM - Association for Computing Machinery

Best Paper Honorable Mention

Publication

Educators have developed an effective technique to get feedback after in-person lectures, called “muddy cards.” Students are given time to reflect and write the “muddiest” (least clear) point on an index card, to hand in as they leave class. This practice of assigning end-of-lecture reflection tasks to generate explicit student feedback is well suited for adaptation to the challenge of supporting feedback in online video lectures. We describe the design and evaluation of Mudslide, a prototype system that translates the practice of muddy cards into the realm of online lecture videos. Based on an in-lab study of students and teachers, we find that spatially contextualizing students’ muddy point feedback with respect to particular lecture slides is advantageous to both students and teachers. We also reflect on further opportunities for enhancing this feedback method based on teachers’ and students’ experiences with our prototype.