ArticleScientific reports2024
Post-secondary classroom teaching quality evaluation using small object detection model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- STEAP: Camera-Based Longitudinal Classroom Behavior Sensing and Static-Temporal Data Fusion for Academic Performance Prediction in Software Engineering Education.Sensors (Basel, Switzerland) · 2026Article
- Teacher behavior-related object detection with an edge-enhanced and dynamic multi-scale network toward metaverse-oriented educational scenarios.Scientific reports · 2026Article
- YOLO-CBD: Classroom Behavior Detection Method Based on Behavior Feature Extraction and Aggregation.Sensors (Basel, Switzerland) · 2025Article
- Real-time classroom student behavior detection based on improved YOLOv8s.Scientific reports · 2025Article
- Evaluating simulated teaching audio for teacher trainees using RAG and local LLMs.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The classroom video has a complex background and dense targets. This study utilizes small object detection technology to analyze and evaluate students' behavior in the classroom, aiming to objectively and accurately assess classroom quality. Firstly, noise is removed from the images using a median filter, and the contrast of the images is enhanced through histogram equalization. Label smoothing is applied to reduce the model's sensitivity to labels. Then, features are extracted from the preprocessed images, and multi-scale feature fusion is employed to enhance semantic expression across multiple scales. Finally, a combination loss function is utilized to improve the accuracy of multi-object recognition tasks. Real-time detection of students' behaviors in the classroom is performed based on the small object detection model. The average head-up rate in the classroom is calculated, and the quality of teaching is evaluated and analyzed. This study explores the methods and applications of small object detection technology based on actual teaching cases and analyzes and evaluates its effectiveness in evaluating the quality of higher education classroom teaching. The research findings demonstrate the significant importance of small object detection technology in effectively evaluating students' learning conditions in higher education classrooms, leading to improved teaching quality and personalized education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.