Content Construction for an Intelligent Tutor System in languages: a pilot study on the OneStopEnglish corpus
Abstract
During foreign language acquisition, reading represents one of the opportunities to get closer to the language. However, inappropriate texts can cause students to have a negative experience; thus, in regular courses, teachers use their experience or an editorial team to select the readings. In an automatic system, as in an Intelligent Tutor System, making recommendations appropriate to the student's profile is a priority. It is not enough to know the language level of the text. This work uses tools to classify a sample of texts from the OneStopEnglish corpus according to the Common European Framework of Reference for Languages. We create thematic groups based on Latent Semantic Analysis (LSA) and use three popular metrics of readability as a guide to suggest texts to students.
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