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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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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...
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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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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Rotter's Locus of Control01:14

Rotter's Locus of Control

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Julian Rotter introduced the concept of locus of control, a cognitive factor that significantly influences personality development and learning. Locus of control refers to an individual's beliefs about the extent of control they have over events in their lives. According to Rotter, this belief system can be categorized into two types: internal and external locus of control.
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Occlusion Robust Cognitive Engagement Detection in Real-World Classroom.

Guangrun Xiao1,2, Qi Xu2,3, Yantao Wei2,3

  • 1School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.

Sensors (Basel, Switzerland)
|June 19, 2024
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Summary
This summary is machine-generated.

This study introduces a new AI model, OE-YOLOv8n, for automatically detecting students

Keywords:
YOLOautomatic detectioncognitive engagementreal-world classroom

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Area of Science:

  • Educational Technology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Cognitive engagement is crucial for learning and can be inferred from observable behaviors.
  • Automated detection of cognitive engagement offers valuable pedagogical insights.
  • Challenges in automated detection include object occlusion, class similarity, and variance within classes.

Purpose of the Study:

  • To propose an effective object detection model for automatically measuring cognitive engagement.
  • To address the challenges of occlusion, inter-class similarity, and intra-class variance in engagement detection.

Main Methods:

  • Development of the Object-Enhanced-You Only Look Once version 8 nano (OE-YOLOv8n) model.
  • Integration of an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss within the YOLOv8n framework.
  • Creation and utilization of a real-world Students' Cognitive Engagement (SCE) dataset for evaluation.

Main Results:

  • The proposed OE-YOLOv8n model demonstrates superior performance in detecting cognitive engagement.
  • The model achieved a precision of 92.5% across five distinct engagement classes.
  • Experiments on the custom SCE dataset validated the model's effectiveness.

Conclusions:

  • The OE-YOLOv8n model offers a robust solution for automated cognitive engagement detection.
  • The IMPDIoU Loss effectively enhances detection accuracy in challenging scenarios.
  • This approach has the potential to provide instructors with real-time insights into student engagement.