# Measuring Student Behavioral Engagement using Histogram of Actions

arXiv: https://arxiv.org/abs/2307.09420 · HTML: https://mlsanity.com/paper/2307.09420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition](https://mlsanity.com/paper/1907.13025/) (2019)
- [Multimodal Engagement Analysis from Facial Videos in the Classroom](https://mlsanity.com/paper/2101.04215/) (2021)
- [Shape Preserving Facial Landmarks with Graph Attention Networks](https://mlsanity.com/paper/2210.07233/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers](https://mlsanity.com/paper/2508.15782/) (2025)
- [EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition](https://mlsanity.com/paper/2408.05421/) (2024)

## Abstract (arXiv)

In this paper, we propose a novel technique for measuring behavioral engagement through students' actions recognition. The proposed approach recognizes student actions then predicts the student behavioral engagement level. For student action recognition, we use human skeletons to model student postures and upper body movements. To learn the dynamics of student upper body, a 3D-CNN model is used. The trained 3D-CNN model is used to recognize actions within every 2minute video segment then these actions are used to build a histogram of actions which encodes the student actions and their frequencies. This histogram is utilized as an input to SVM classifier to classify whether the student is engaged or disengaged. To evaluate the proposed framework, we build a dataset consisting of 1414 2-minute video segments annotated with 13 actions and 112 video segments annotated with two engagement levels. Experimental results indicate that student actions can be recognized with top 1 accuracy 83.63% and the proposed framework can capture the average engagement of the class.