Abstract
This study reports on the application of text mining and machine learning methods in the context of asynchronous peer instruction, with the objective of automatically identifying high quality student explanations. Our study compares the performance of state-of-the-art methods across different reference datasets and validation schemes. We demonstrate that when we extend the task of argument quality assessment along the dimensions of convincingness, from curated datasets, to data from a real learning environment, new challenges arise, and simpler vector space models can perform as well as a state-of-the-art neural approach.
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- 1.
Modified from the run_glue.py script provided by the tranformers package, built by company hugging face. All code for this study provided at https://github.com/sameerbhatnagar/ectel2020.
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Acknowledgements
Funding for the development of myDALITE.org is made possible by Entente-Canada-Quebec, and the Ministère de l’Éducation et Enseignment Supérieure du Québec. Funding for this research was made possible by the support of the Canadian Social Sciences and Humanities Research Council Insight Grant. This project would not have been possible without the SALTISE/S4 network of researcher practitioners, and the students using myDALITE.org who consented to share their learning traces with the research community.
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Bhatnagar, S., Zouaq, A., Desmarais, M.C., Charles, E. (2020). Learnersourcing Quality Assessment of Explanations for Peer Instruction. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds) Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science(), vol 12315. Springer, Cham. https://doi.org/10.1007/978-3-030-57717-9_11
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