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An Automated Feedback System to Support Student Learning in Writing-to-Learn Activities

Published:24 June 2019Publication History

ABSTRACT

Formative feedback has long been recognized as a crucial scaffold for student learning. Due to the job demand of instructors, it is impossible for them to provide individual students with on-demand formative feedback based on individual students' performance. There is a growing interest in developing better approaches to provide students with automated formative feedback to assist their learning. In this research, we design and develop an automated formative feedback system to support student learning of conceptual knowledge in the course of writing assignments. In the proposed system, formative feedback can be generated automatically with the help of concept maps constructed from instructors' lecture slides and students' writing assignments. In this paper, we present the automatic approach to generate formative feedback, discuss the system architecture, and illustrate a prototype of the proposed system.

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        • Published in

          cover image ACM Other conferences
          L@S '19: Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale
          June 2019
          386 pages
          ISBN:9781450368049
          DOI:10.1145/3330430

          Copyright © 2019 Owner/Author

          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.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 June 2019

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          • poster
          • Research
          • Refereed limited

          Acceptance Rates

          L@S '19 Paper Acceptance Rate24of70submissions,34%Overall Acceptance Rate117of440submissions,27%

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