Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes
This work addresses the need for scalable teacher assessment in online education, but it is incremental as it applies an existing method to a new domain-specific task.
The paper tackled the problem of automatically detecting six dialogic instructions in K-12 online one-on-one classes to support teacher training, using an LSTM model that achieved AUC scores ranging from 0.840 to 0.979 on a real-world dataset.
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.