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This study introduces a digital case-based learning framework to enhance programming education. It uses a personalized genetic algorithm for tailored assessments and recommends learning strategies for effective information acquisition.

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Area of Science:

  • Computer Science
  • Educational Technology

Background:

  • Growing volume of multimedia learning resources necessitates effective exploration methods.
  • Digital case-based learning systems are crucial for organizing multimedia materials to support programming education.

Purpose of the Study:

  • To propose an integrated framework for a case-oriented e-learning system.
  • To develop personalized test sheets and recommend learning strategies for learners.

Main Methods:

  • Integration of multimedia resources, testing, and learning strategy recommendations into a learning unit.
  • Development of an improved personalized genetic algorithm incorporating learner preference and usage data.
  • Implementation of a learning mechanism involving guidance, material learning, and feedback.

Main Results:

  • The proposed personalized genetic algorithm effectively constructs tailored test sheets for individual learners.
  • Experimental results demonstrate the overall effectiveness of the digital case-based learning framework.
  • The system supports personalized learning guidance, multimedia material engagement, and testing feedback.

Conclusions:

  • The developed framework successfully personalizes the learning experience in programming education.
  • The integration of multimedia resources and adaptive algorithms enhances learning efficiency.
  • The study validates the proposed approaches for creating effective digital case-based learning environments.