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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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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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Introduction to Learning01:18

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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.
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Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

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Associative Learning01:27

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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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Observational Learning01:12

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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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Related Experiment Video

Updated: Jan 17, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Personalised and Collaborative Learning Experience (PCLE) Framework for AI-driven Learning Management System (LMS).

Claireta Tang Weiling1, Lew Sook Ling2, Ooi Shih Yin2

  • 1Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia.

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|September 15, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances AI-driven e-learning by developing machine learning models for better course recommendations. K-Nearest Neighbours excelled on structured data, while SVD and NCF offered consistent performance across diverse datasets, improving student engagement.

Keywords:
Collaborative FilteringK-Nearest Neighbours Model (KNN)Learning Management SystemNeural Collaborative Filtering Model (NCF)Personalised LearningSingular Value Decomposition Model (SVD)

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Machine Learning Applications

Background:

  • Effective student engagement and academic performance are vital in AI-driven e-learning.
  • Learning Management Systems (LMS) often lack robust collaborative course recommendation strategies.
  • Personalized learning experiences are limited by current recommendation systems.

Purpose of the Study:

  • To develop and evaluate collaborative filtering and machine learning models for course recommendations.
  • To enhance personalized learning experiences within AI-driven e-learning environments.
  • To improve student engagement and academic performance through advanced recommendation strategies.

Main Methods:

  • Applied machine learning models: K-Nearest Neighbours (KNN), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF).
  • Utilized two large datasets: 100,000 Coursera reviews and 209,000 Udemy course details/comments.
  • Evaluated model performance using Mean Absolute Error (MAE), Hit Rate (HR), and Average Reciprocal Hit Ranking (ARHR).

Main Results:

  • K-Nearest Neighbours (KNN) demonstrated superior performance on the structured Coursera dataset.
  • Singular Value Decomposition (SVD) and Neural Collaborative Filtering (NCF) exhibited stable predictive accuracy across both Coursera and Udemy datasets.
  • Dataset characteristics significantly influenced model performance, with KNN favoring structured data and SVD/NCF showing versatility.

Conclusions:

  • Collaborative filtering and machine learning models significantly enhance course recommendations in LMS.
  • The study highlights the potential to boost student engagement and academic performance in e-learning.
  • Future research will incorporate learning styles and evaluate broader educational contexts for adaptive learning.