Abstract
Writing is a central skill needed for learning that is tightly linked to text comprehension. Good writing skills are gained through practice and are characterized by clear and organized language, accurate grammar usage, strong text cohesion, and sophisticated wording. Providing constructive feedback can help learners improve their writing; however, providing feedback is a time-consuming process. The aim of this paper is to present an updated version of the tool ReadME, which generates automated and personalized feedback designed to help learners improve the quality of their writing. Sampling a corpus of over 15,000 essays, we used the ReaderBench framework to generate more than 1,200 textual complexity indices. These indices were then grouped into six writing components using a Principal Component Analysis. Based on the components generated by the PCA, as well as individual index values, we created an extensible rule-based engine to provide personalized feedback at four granularity levels: document, paragraph, sentence, and word levels. The ReadME tool consists of a multi-layered, interactive visualization interface capable of providing feedback to writers by highlighting sections of texts that may benefit from revision.
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Acknowledgments
This research was partially supported by the ReadME project “Interactive and Innovative application for evaluating the readability of texts in Romanian Language and for improving users’ writing styles”, contract no. 114/15.09.2017, MySMIS 2014 code 119286, as well as the FP7 2008-212578 LTfLL project.
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Sirbu, MD., Botarleanu, RM., Dascalu, M., Crossley, S.A., Trausan-Matu, S. (2018). ReadME – Enhancing Automated Writing Evaluation. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_28
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DOI: https://doi.org/10.1007/978-3-319-99344-7_28
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