English

Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education

Computation and Language 2020-01-30 v3 Machine Learning

Abstract

Spoken language proficiency is critically important for children's growth and personal development. Due to the limited and imbalanced educational resources in China, elementary students barely have chances to improve their oral language skills in classes. Verbal fluency tasks (VFTs) were invented to let the students practice their spoken language proficiency after school. VFTs are simple but concrete math related questions that ask students to not only report answers but speak out the entire thinking process. In spite of the great success of VFTs, they bring a heavy grading burden to elementary teachers. To alleviate this problem, we develop Dolphin, a spoken language proficiency assessment system for Chinese elementary education. Dolphin is able to automatically evaluate both phonological fluency and semantic relevance of students' VFT answers. We conduct a wide range of offline and online experiments to demonstrate the effectiveness of Dolphin. In our offline experiments, we show that Dolphin improves both phonological fluency and semantic relevance evaluation performance when compared to state-of-the-art baselines on real-world educational data sets. In our online A/B experiments, we test Dolphin with 183 teachers from 2 major cities (Hangzhou and Xi'an) in China for 10 weeks and the results show that VFT assignments grading coverage is improved by 22\%.

Keywords

Cite

@article{arxiv.1908.00358,
  title  = {Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education},
  author = {Wenbiao Ding and Guowei Xu and Tianqiao Liu and Weiping Fu and Yujia Song and Chaoyou Guo and Cong Kong and Songfan Yang and Gale Yan Huang and Zitao Liu},
  journal= {arXiv preprint arXiv:1908.00358},
  year   = {2020}
}

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Proceedings of The Web Conference 2020 (WWW '20)

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