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 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 language | English |
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| Pages | 688-693 |
| Number of pages | 6 |
| Publication status | Published - 2016 |
| Event | 9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: 29 Jun. 2016 → 2 Jul. 2016 |
Conference
| Conference | 9th International Conference on Educational Data Mining, EDM 2016 |
|---|---|
| Country/Territory | United States |
| City | Raleigh |
| Period | 29/06/16 → 2/07/16 |
Keywords
- Analytic tools for learners
- Automated essay feedback
- Natural language processing
- Performance feedback
- Scalable analytics