# SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior

arXiv: https://arxiv.org/abs/2310.02522 · HTML: https://mlsanity.com/paper/2310.02522/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [Reconstruction Network for Video Captioning](https://mlsanity.com/paper/1803.11438/) (2018)
- [Fast Learning of Temporal Action Proposal via Dense Boundary Generator](https://mlsanity.com/paper/1911.04127/) (2019)
- [A Comprehensive Study of Deep Video Action Recognition](https://mlsanity.com/paper/2012.06567/) (2020)
- [The AVA-Kinetics Localized Human Actions Video Dataset](https://mlsanity.com/paper/2005.00214/) (2020)
- [Actions as Moving Points](https://mlsanity.com/paper/2001.04608/) (2020)
- [Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning](https://mlsanity.com/paper/1804.05448/) (2018)
- [Actor Conditioned Attention Maps for Video Action Detection](https://mlsanity.com/paper/1812.11631/) (2018)
- [Student Classroom Behavior Detection based on Improved YOLOv7](https://mlsanity.com/paper/2306.03318/) (2023)
- [StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking](https://mlsanity.com/paper/2211.03127/) (2022)

## Built on by (most cited first, 5 of 5)

- [Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusion](https://mlsanity.com/paper/2310.16267/) (2023)
- [On the development of an AI performance and behavioural measures for teaching and classroom management](https://mlsanity.com/paper/2506.11143/) (2025)
- [Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring](https://mlsanity.com/paper/2507.22369/) (2025)
- [A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Dataset](https://mlsanity.com/paper/2606.21568/) (2026)
- [ChildGaze: A Benchmark Dataset for Collaborative Behavior Understanding in Children](https://mlsanity.com/paper/2609.06353/) (2026)

## Abstract (arXiv)

The use of deep learning methods to automatically detect students' classroom behavior is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available datasets on student behavior poses a challenge for researchers in this field. To address this issue, we propose the Student Classroom Behavior dataset (SCB-dataset3), which represents real-life scenarios. Our dataset comprises 5686 images with 45578 labels, focusing on six behaviors: hand-raising, reading, writing, using a phone, bowing the head, and leaning over the table. We evaluated the dataset using the YOLOv5, YOLOv7, and YOLOv8 algorithms, achieving a mean average precision (map) of up to 80.3%. We believe that our dataset can serve as a robust foundation for future research in student behavior detection and contribute to advancements in this field. Our SCB-dataset3 is available for download at: https://github.com/Whiffe/SCB-dataset