Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes
Shiting Xu, Wenbiao Ding, Zitao Liu

TL;DR
This paper develops six dialogic instructions for online K-12 one-on-one classes and employs LSTM neural models to automatically detect these instructions, enhancing interactive learning experiences.
Contribution
It introduces a set of six dialogic instructions and applies LSTM models for their automatic detection in online education settings, improving instructional analysis.
Findings
LSTM models achieve AUC scores from 0.840 to 0.979
Six dialogic instructions effectively enhance online teaching
Demonstrates feasibility of automatic instruction detection in real-world data
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.
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Speech and dialogue systems
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory
