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.
CITATION STYLE
Bhatnagar, S., Zouaq, A., Desmarais, M. C., & Charles, E. (2020). Learnersourcing quality assessment of explanations for peer instruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12315 LNCS, pp. 144–157). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57717-9_11
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