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

arXiv: https://arxiv.org/abs/2006.01204 · HTML: https://mlsanity.com/paper/2006.01204/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 4 of 4)

- [The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education](https://mlsanity.com/paper/2404.02444/) (2024)
- [Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models](https://mlsanity.com/paper/2107.07119/) (2021)
- [An Educational System for Personalized Teacher Recommendation in K-12 Online Classrooms](https://mlsanity.com/paper/2107.07124/) (2021)
- [DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments](https://mlsanity.com/paper/2206.12034/) (2022)

## Abstract (arXiv)

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.