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Related Concept Videos

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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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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Introduction to Learning01:18

Introduction to Learning

743
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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Purposive Learning01:22

Purposive Learning

320
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...
320
Associative Learning01:27

Associative Learning

916
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.
Classical conditioning, also known...
916
Metacognition01:26

Metacognition

466
Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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Related Experiment Video

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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Learning recommendation with formal concept analysis for intelligent tutoring system.

Jirapond Muangprathub1, Veera Boonjing2, Kosin Chamnongthai3

  • 1Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani, 84000, Thailand.

Heliyon
|November 2, 2020
PubMed
Summary

This study developed an intelligent tutoring system (ITS) with adaptive learning to personalize content for individual learner styles. The system demonstrated improved learning outcomes in real-world educational settings.

Keywords:
Adaptive learningComputer ScienceFormal concept analysisIntelligent tutoring systemLearning recommendation

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education

Background:

  • Intelligent Tutoring Systems (ITS) require dynamic adaptation to individual learner styles for effective personalized learning.
  • Existing ITS often lack sophisticated mechanisms for predicting and responding to diverse learning preferences.

Purpose of the Study:

  • To develop a learning recommendation component for an ITS that dynamically predicts and adapts to a learner's style.
  • To create an improved knowledge base supporting adaptive learning through effective knowledge construction.
  • To implement and evaluate a web-based online tutor system demonstrating personalized learning capabilities.

Main Methods:

  • Development of an improved knowledge base for adaptive learning.
  • Implementation of a web-based online tutor system.
  • Design and application of an adaptive algorithm for personalized content retrieval based on learner characteristics.
  • Evaluation using pre-test and post-test assessments in a classroom and a real teaching/learning environment.

Main Results:

  • The proposed adaptive algorithm accurately suggested course content tailored to individual learner styles.
  • The implemented ITS demonstrated effectiveness in improving student learning in a real educational setting.
  • The system successfully retrieved content aligned with individual learner characteristics.

Conclusions:

  • The developed intelligent tutoring system with its adaptive learning component enhances the learning experience.
  • The proposed model provides a viable solution for personalized education by adapting to learner styles.
  • The system shows potential for significant improvements in student learning achievements.