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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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Study on Personalized Recommendation Algorithm of Online Educational Resources Based on Knowledge Association.

Ziqian Xu1, Sheng Jiang2

  • 1Huai'an Campus of Nanjing Forestry University, Nanjing, Jiangsu 210037, China.

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Summary

This study introduces a personalized educational resource recommendation algorithm using knowledge association. It improves accuracy to 97% and user satisfaction for online learning platforms.

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Area of Science:

  • Educational Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional educational resource recommendation algorithms suffer from low accuracy, efficiency, and user satisfaction.
  • Personalized recommendations are crucial for effective online learning.

Purpose of the Study:

  • To propose a novel personalized recommendation algorithm for online educational resources.
  • To enhance recommendation accuracy, efficiency, and user satisfaction.

Main Methods:

  • Collecting online educational resources using association rules.
  • Classifying resources with the firefly algorithm.
  • Filtering resources via vector space functions.
  • Calculating knowledge point correlations using knowledge association theory.

Main Results:

  • Achieved a resource recommendation accuracy of 97%.
  • Recommendation time is less than 5.0 seconds.
  • Demonstrated increased user satisfaction with the recommendation effect.

Conclusions:

  • The proposed knowledge association-based algorithm effectively personalizes online educational resource recommendations.
  • The method significantly improves upon existing recommendation systems in accuracy and efficiency.