A scalable learning analytics platform for automated writing feedback

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

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


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 our feedback system in two ways. First, we evaluate the effectiveness of the feedback for students with an ongoing pilot study with eight hundred students who are using the learning analytics platform in a college English course. In addition, we process an existing set of graded student essays and analyze the performance feedback. Results show a correlation between feedback values and human graded scores.

Original languageEnglish
Number of pages6
Publication statusPublished - 2016
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States
Duration: 29 Jun. 20162 Jul. 2016


Conference9th International Conference on Educational Data Mining, EDM 2016
Country/TerritoryUnited States


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


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