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Summary
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Open learning offers accessible computer science education but struggles with quality assessment and individual differences. This research proposes adaptive, crowdsourced educational technology to address these limitations for software development students.

Keywords:
Adaptive learning systemsCrowdsourcingOpen learning

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Area of Science:

  • Educational Technology
  • Computer Science Education

Background:

  • Technological advancements facilitate open learning, removing geographical barriers to education.
  • Online courses offer flexible learning opportunities, particularly beneficial for in-demand fields like computer science.
  • Current open learning platforms face challenges in assessing learning quality and accommodating individual learner differences.

Purpose of the Study:

  • To propose an adaptive, crowdsourced educational technology for software development students.
  • To address the limitations of current open learning systems in assessing learning quality and individual needs.
  • To provide a technology that supports both individual and group learning.

Main Methods:

  • Development of an adaptive educational technology platform.
  • Integration of crowdsourcing for collaborative learning and assessment.
  • Continuous monitoring and assessment of student performance throughout the educational process.

Main Results:

  • The proposed technology aims to provide a more effective assessment of learning outcomes in open learning environments.
  • It offers a personalized learning experience by adapting to individual student progress.
  • It supports collaborative learning through crowdsourced features.

Conclusions:

  • Adaptive, crowdsourced educational technology can enhance the quality and effectiveness of open learning in computer science.
  • Continuous performance monitoring is crucial for accurate assessment in online education.
  • This approach has the potential to improve skill development for software development students in flexible learning settings.