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

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