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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

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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...
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Cognitive Learning01:21

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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.
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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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Purposive Learning01:22

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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Video

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Application of Deep Learning on Student Engagement in e-learning environments.

Prakhar Bhardwaj1, P K Gupta1, Harsh Panwar1

  • 1Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India.

Computers & Electrical Engineering : an International Journal
|December 26, 2022
PubMed
Summary

This study introduces deep learning algorithms to monitor student emotions and engagement in real-time during online classes. The Mean Engagement Score (MES) helps improve digital learning experiences for better educational outcomes.

Keywords:
COVID-19Deep learningDigital learningEmotion recognitionEngage DetectionEngagement detection

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Vision

Background:

  • The COVID-19 pandemic accelerated the adoption of e-learning and remote digital education.
  • Maintaining student engagement in online classes is crucial for effective learning.
  • Traditional classroom engagement metrics are difficult to translate to digital environments.

Purpose of the Study:

  • To develop novel deep learning algorithms for real-time monitoring of student emotions and engagement during online classes.
  • To introduce a Mean Engagement Score (MES) for quantifying student participation.
  • To provide educational institutions with tools for enhancing digital learning methods.

Main Methods:

  • Real-time emotion recognition using facial landmark detection.
  • Analysis of facial expressions to identify emotions like happiness, sadness, anger, fear, disgust, and surprise.
  • Computation of the Mean Engagement Score (MES) integrating facial analysis and student survey data.

Main Results:

  • The proposed algorithms can monitor a range of student emotions in real-time.
  • The Mean Engagement Score (MES) provides a quantitative measure of student engagement.
  • The system offers an automated approach to assess and potentially improve online learning interactions.

Conclusions:

  • Deep learning-based emotion and engagement monitoring can significantly enhance online education.
  • The MES offers a novel metric for evaluating the effectiveness of digital learning platforms.
  • This technology can help educational institutions create more interactive and engaging remote learning experiences.