Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Observational Learning01:12

Observational Learning

188
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
188
Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Introduction to Learning01:18

Introduction to Learning

446
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
446
Aggregates Classification01:29

Aggregates Classification

328
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
328
Cognitive Learning01:21

Cognitive Learning

249
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
249
Classification of Signals01:30

Classification of Signals

484
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
484

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Advances in Research on Autophagy in Steroid-Induced Osteonecrosis of the Femoral Head: Dual Regulation of Its Beneficial and Detrimental Effects.

Drug design, development and therapy·2026
Same author

CTI-related composite adiposity indices and future cardiovascular disease risk among middle-aged and older Chinese adults.

Frontiers in nutrition·2026
Same author

Ultrasound-guided foam sclerotherapy vs. open surgical ligation for incompetent perforator veins: a retrospective cohort study.

Frontiers in surgery·2026
Same author

Thin-film lithium tantalate electro-optic modulator with balanced bandwidth-voltage and low DC drift.

Optics express·2026
Same author

Preliminary clinical observation of unilateral implantation of trifocal intraocular lens.

BMC ophthalmology·2026
Same author

A New Arbitrary-Time Guaranteed-Performance Adaptive Tracking Control Scheme Design for Uncertain Nonlinear Systems.

IEEE transactions on cybernetics·2026

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Student Behavior Detection in the Classroom Based on Improved YOLOv8.

Haiwei Chen1, Guohui Zhou1, Huixin Jiang2

  • 1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces an improved YOLOv8 model for detecting student behaviors in classroom videos. The enhanced model shows better performance, increasing average precision by 4.2% for improved teaching analysis.

Keywords:
EMAMHSAYOLOv8classroom behavior detection

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.4K

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.4K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Educational Technology

Background:

  • Accurate detection of student classroom behaviors is crucial for performance analysis and enhancing teaching effectiveness.
  • Challenges in classroom videos include high object density, occlusion, and multi-scale scenarios.
  • Existing models may struggle to address these complex visual conditions effectively.

Purpose of the Study:

  • To develop an improved YOLOv8 model for accurate student behavior detection in classroom videos.
  • To overcome challenges related to object density, occlusion, and multi-scale variations in classroom environments.
  • To enhance the analysis of student performance and teaching effectiveness through improved video analysis.

Main Methods:

  • Integration of Res2Net modules with the YOLOv8 network to create a novel C2f_Res2block module.
  • Incorporation of Multi-Head Self-Attention (MHSA) and Efficient Multi-Attention (EMA) mechanisms into the YOLOv8 architecture.
  • Training and evaluation of the improved model on a dedicated classroom detection dataset.

Main Results:

  • The improved YOLOv8 model demonstrated superior detection performance compared to the original YOLOv8.
  • A significant increase in average precision (mAP@0.5) of 4.2% was achieved by the enhanced model.
  • The proposed C2f_Res2block module, along with MHSA and EMA, contributed to the performance gains.

Conclusions:

  • The enhanced YOLOv8 model effectively addresses the complexities of student behavior detection in classroom videos.
  • The proposed architectural improvements lead to more accurate and reliable detection results.
  • This advancement offers potential for more insightful analysis of classroom dynamics and pedagogical strategies.