Abstract
As education increasingly takes place in technologically mediated settings, it has become easier to collect student data that would be valuable to researchers. However, much of this data is not available due to concerns surrounding the protection of student privacy. Deidentification of student data is a partial solution to this problem, but student-generated text, a form of unstructured data, is a major challenge for deidentification strategies. In response to this problem, we develop and evaluate two approaches for the automatic detection of student names. We develop one system using a rule-based approach and one using a transformer-based approach that relies on finetuning a pretrained large language model. Our findings indicate that the transformer-based approach to student name detection shows more promise, especially when there is a high degree of variation between texts in a dataset.
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This material is based upon work supported by the National Science Foundation under Grant 2112532 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Holmes, L., Crossley, S.A., Morris, W., Sikka, H., Trumbore, A. (2023). Deidentifying Student Writing with Rules and Transformers. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_109
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DOI: https://doi.org/10.1007/978-3-031-36336-8_109
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