Developing Automated Audio Assessment Tools for a Chinese Language Course Public
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Assessment in the context of foreign language learning can be difficult and time-consuming for instructors. Distinctive from other domains, language learning often requires teachers to assess each student's ability to speak the language, making this process even more time-consuming in large classrooms which are particularly common in post-secondary settings;considering that language instructors often assess students through assignments requiring recorded audio, a lack of tools to support such teachers makes providing individual feedback even more challenging. In this work, we seek to explore the development of tools to automatically assess audio responses within a college-level Chinese language-learning course. We build models designed to grade student audio assignments with the purpose of incorporating such models into tools focused on helping both teachers and students in real classrooms. This work also includes exploring various features - audio, tonal and text to help assess students on two outcomes commonly observed in language learning classes: fluency and accuracy of speech. We find that models utilizing tonal features exhibit higher predictive performance of student fluency while text-based features derived from speech recognition models exhibit higher predictive performance of student accuracy of speech.
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Permanent link to this page: https://digital.wpi.edu/show/8049g769q