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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 a model designed to grade student audio assignments with the purpose of incorporating such a model into tools focused on helping both teachers and students in real classrooms. Building upon our prior work which explored features extracted from audio, the goal of this work is to explore additional features derived from tone and speech recognition models to help assess students on two outcomes commonly observed in language learning classes: fluency and accuracy of speech. In addition to the exploration of features, this work explores the application of Siamese deep learning models for this assessment task. 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. 

Handwriting practice is the most time-consuming activity for learners of Chinese as a foreign language (CFL). CFL instructors report allocating at least one third of their course time to handwriting practice although it prevents students from engaging in meaningful communication, especially in the earliest stages of learning. Given the amount of time students spend in a college course is relatively fixed, the preregistered study presented herein examines the best use of students’ time when primary goals are word acquisition and communication. This work replicates a pilot study examining CFL word recognition in an online learning environment (ASSISTments) and the effects of supplemental handwriting practice. We examined word acquisition and recognition while manipulating condition (no-handwriting practice and with-handwriting practice), and posttest test point (1 [immediate], 2 [1 day delay], and 3 [1 week delay]). Two-way repeated measures analyses of variance revealed significant main effects for both condition and posttest test point in online and on-paper measures of word recognition and handwriting. Potential implications for CFL instruction and directions for future work are discussed.