Abstract
Developing machine learning tools to aid students in the process of writing a thesis document is of great interest to students, universities, supervisors and evaluation committees. This article presents the construction and evaluation of readability comparators based in Spanish-written thesis documents of four different academic levels: Advanced College Level Technician (ACT), Undergraduate, Master and Doctoral. Specifically, we provide comparators that can evaluate, between two academic texts which one is more readable than the other. In particular, we focus on three thesis sections: Problem Statement, Justification, and Results. The successful completion of these different comparators, as shown in results, allowed to test readability evaluators for new undergrad writings, determining whether correspond to its academic level. Some guidelines to determine comparators and academic level archetypes for readability assessment are proposed, based on our results.
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Notes
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A two year program offered in some countries.
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[9] is a dataset created by the Royal Academy of (Spanish) Language, that provides statistics of the most common words.
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Acknowledgements
The first author thanks the support provided through scholarship 1009285 by Conacyt, México. The other authors were partially supported by SNI, México.
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Medardo Tapia-Téllez, J., López-López, A., González-López, S., García-Gorrostieta, J.M. (2023). Readability Assessment of Academic Texts at Different Degree Levels. In: Temperini, M., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference. MIS4TEL 2022. Lecture Notes in Networks and Systems, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-031-20617-7_6
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