A scalable learning analytics platform for automated writing feedback

Nicholas Lewkow, Jacqueline Feild, Neil Zimmerman, Mark Riedesel, Alfred Essa, David Boulanger, Jeremie Seanosky, Vive Kumar Kinshuk, Sandhya Kode

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

9 Citations (Scopus)

Abstract

In this paper, we describe a scalable learning analytics platform which runs generalized analytics models on educational data in parallel. As a proof of concept, we use this platform as a base for an end-to-end automated writing feedback system. The system allows students to view feedback on their writing in near real-time, edit their writing based on the feedback provided, and observe the progression of their performance over time. Providing students with detailed feedback is an important part of improving writing skills and an essential component towards solving Bloom's "two sigma" problem in education. We evaluate the effectiveness of the feedback for students with an ongoing pilot study with 800 students who are using the learning analytics platform in a college English course.

Original languageEnglish
Title of host publicationL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
Pages109-112
Number of pages4
ISBN (Electronic)9781450337267
DOIs
Publication statusPublished - 25 Apr. 2016
Event3rd Annual ACM Conference on Learning at Scale, L@S 2016 - Edinburgh, United Kingdom
Duration: 25 Apr. 201626 Apr. 2016

Publication series

NameL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale

Conference

Conference3rd Annual ACM Conference on Learning at Scale, L@S 2016
Country/TerritoryUnited Kingdom
CityEdinburgh
Period25/04/1626/04/16

Keywords

  • Analytic tools for learners
  • Automatic essay feedback
  • Natural language processing
  • Performance feedback
  • Scalable analytics

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