Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes

2020/05/16 by Shiting Xu, Xu, Shiting, Wenbiao Ding +3
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2006.01204

openalex publication_date 2020/05/16 · openalex created_date 2020/06/12 · openalex updated_date 2026/07/28

Abstract

Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.

Citations

• Long Short-Term Memory
• Stochastic gradient boosting
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

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