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Making distance matter: leveraging scale and diversity in massive online classes

Published: 05 October 2014 Publication History

Abstract

The large scale of online classes and the diversity of the students that participate in them can enable new educational systems. This massive scale and diversity can enable always-available systems that help students share diverse ideas, and inspire and learn from each other. We introduce systems for two core educational processes at scale: discussion and assessment. To date, several thousand students in a dozen online classes have used our discussion system. Controlled experiments suggest that participants in more diverse discussions perform better on tests and that discussion improves engagement. Similarly, more than 100,000 students have reviewed peer work for both summative assessment and feedback. Through these systems, we argue that to create new educational experiences at scale, pedagogical strategies and software that leverage scale and diversity must be co-developed. More broadly, we suggest the key to creating new educational experiences online lies in leveraging massive networks of peers.

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      cover image ACM Conferences
      UIST '14 Adjunct: Adjunct Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology
      October 2014
      150 pages
      ISBN:9781450330688
      DOI:10.1145/2658779
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 05 October 2014

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      Author Tags

      1. MOOC
      2. discussion
      3. online education
      4. peer assessment
      5. social computing

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      UIST '14 Adjunct Paper Acceptance Rate 74 of 333 submissions, 22%;
      Overall Acceptance Rate 355 of 1,733 submissions, 20%

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