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Related Experiment Video

Updated: Nov 22, 2025

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

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

Published on: December 15, 2023

Abnormal video homework automatic detection system.

Jinjiao Lin¹, Yanze Zhao¹, Chunfang Liu¹

¹School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China.

Journal of Ambient Intelligence and Humanized Computing

|January 11, 2021

Summary

This summary is machine-generated.

This study introduces an automated system for detecting abnormal video homework, improving grading efficiency. The Abnormal Video Homework Automatic Detection System (AVHADS) ensures timely feedback for students by identifying issues like poor video quality.

Keywords:

Abnormal detection Audio classification Open CV Video homework

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Area of Science:

Educational Technology
Computer Vision
Machine Learning

Background:

Manual review of video homework is time-consuming for educators.
Students often submit videos with technical issues such as poor image quality, missing faces, or incorrect orientation.
Inefficient manual review delays crucial feedback to students.

Purpose of the Study:

To develop an automated system for detecting abnormal video homework.
To enhance the efficiency and timeliness of homework marking processes.
To provide prompt and constructive feedback to students regarding video submissions.

Main Methods:

Utilized suffix and parameter identification for initial video analysis.
Employed Open CV for image and video processing tasks.
Implemented an audio classification model based on Mel-frequency cepstral coefficients (MFCC) features.
Developed the Abnormal Video Homework Automatic Detection System (AVHADS).

Main Results:

The AVHADS successfully identified various forms of abnormal video homework.
The system demonstrated feasibility in automatically detecting problematic video submissions.
Experimental results confirmed the effectiveness of the proposed detection methods.

Conclusions:

Automated detection of abnormal video homework significantly improves grading efficiency.
The AVHADS provides a viable solution for identifying technical issues in student video submissions.
This system facilitates faster feedback loops, benefiting student learning and engagement.