Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9202
Title: How to Use Theory to Implement Natural Language Processing for Peer-Feedback
Authors: Greisel, Martin
Bauer, Elisabeth
Kuznetsov, Ilia
Berndt, Markus
Dresel, Markus
Fischer, Martin R.
Kollar, Ingo
Fischer, Frank
Keywords: CSCL
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Greisel, M., Bauer, E., Kuznetsov, I., Berndt, M., Dresel, M., Fischer, M. R., Kollar, I., & Fischer, F. (2023). How to use theory to implement natural language processing for peer-feedback. In Damșa, C., Borge, M., Koh, E., & Worsley, M. (Eds.), Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023 (pp. 237-240). International Society of the Learning Sciences.
Abstract: Whenever learners produce text, natural language processing (NLP) has great potential to improve learning. Theories from learning sciences should guide the implementation of NLP into concrete learning scenarios. However, theoretical concepts are much more abstract than the targets and inputs NLP can work with. Therefore, a process is needed which translates theory into NLP tasks. As such a process is missing, we propose a terminological and procedural scheme which researchers and practitioners can employ to develop NLP-based adaptive support measures for learning processes. It defines a sequence of leverage points, support measures, adaptation targets, automation goals, data, prediction targets, input, intrinsic metrics, NLP model, and extrinsic metrics. To illustrate it, we apply it to peer-feedback as a use case.
Description: Short Paper
URI: https://doi.org/10.22318/cscl2023.108887
https://repository.isls.org//handle/1/9202
Appears in Collections:ISLS Annual Meeting 2023

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