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Related Concept Videos

Observational Learning01:12

Observational Learning

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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...
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Deep learning-based model for analyzing student engagement in activities.

Feng Feng1,2

  • 1Youth League Committee, Anqing Vocaitional and Technical College, Anqing, 246003, Anhui, China. fengfeng3561@outlook.com.

Scientific Reports
|December 23, 2025
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Summary
This summary is machine-generated.

This study introduces the Intelligent Chimp-driven Bi-directional long short-term memory network-scalable gated recurrent unit (IC-BiSGRU-Net) for accurate student engagement analysis. The novel model effectively integrates multimodal data, outperforming traditional methods in real-time engagement classification.

Keywords:
Convolutional neural network (CNN)EducationIntelligent chimp-driven Bi-directional long short-term memory network–scalable gated recurrent unit (IC-BiSGRU-Net)Student engagement

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Area of Science:

  • Educational Technology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Student engagement is crucial for academic success, but traditional assessment methods are subjective and limited.
  • Existing automated models struggle to integrate multimodal behavioral cues for comprehensive engagement analysis.
  • There is a need for advanced frameworks to accurately analyze student engagement across diverse learning environments.

Purpose of the Study:

  • To introduce an advanced framework, the Intelligent Chimp-driven Bi-directional long short-term memory network-scalable gated recurrent unit (IC-BiSGRU-Net), for robust student engagement analysis.
  • To effectively integrate multimodal behavioral cues from various sources for a more accurate assessment of student engagement.
  • To develop a real-time system capable of classifying student engagement states.

Main Methods:

  • Collected multimodal educational datasets including classroom video, audio, and digital activity logs.
  • Pre-processed data using spectral median filtering, min-max normalization, and log handling.
  • Extracted facial micro-expressions and action units via a CNN encoder and processed features with the IC-BiSGRU-Net model, integrating Bi-LSTM and SGRU.

Main Results:

  • The IC-BiSGRU-Net model achieved high performance metrics, with accuracy, recall, precision, and F1-score ranging from 96% to 99%.
  • The proposed model significantly outperformed conventional models in benchmark student engagement datasets.
  • The system demonstrated real-time classification of student engagement into active, passive, and disengaged states.

Conclusions:

  • The IC-BiSGRU-Net framework offers a robust and accurate solution for multimodal student engagement analysis.
  • This advanced model effectively addresses the limitations of traditional and existing automated engagement assessment methods.
  • The developed system has the potential to enhance educational strategies through real-time, precise monitoring of student engagement.