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Student performance prediction for adaptive e-learning systems

Student performance prediction for adaptive e-learning systems

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The growth of e-learning systems has changed current learning behavior and tries to present a new framework for the learners. E-learning platforms have become common and approachable for a vast set of audiences. The COVID-19 pandemic in 2020 has triggered the application of these online learning platforms. The number of e-learning platforms has been increasing rapidly to fulfill the requirement. This chapter tries to estimate the three factors consisting of learner's personality, learning style and knowledge level in order to recommend the content that is best suited to the learner. An ensemble approach to solving this problem has been used, which utilizes a genetic algorithm and KNN to find the content appropriate for the learner.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 Literature survey
  • 4.2.1 Learner profile
  • 4.2.2 Soft computing techniques
  • 4.3 Methodology
  • 4.3.1 Conversion of numeric to intuitionistic fuzzy value
  • 4.3.2 Learning style model
  • 4.3.3 Personality model
  • 4.3.4 Assessment of knowledge level
  • 4.3.5 Intuitionistic fuzzy optimization algorithm and KNN classifier
  • 4.4 Experimental results
  • 4.5 Future work
  • 4.6 Conclusion
  • References

Inspec keywords: computer aided instruction; nearest neighbour methods; genetic algorithms

Other keywords: genetic algorithm; K-NN; learners personality; adaptive e-learning systems; online learning platforms; learning behavior; student performance prediction; k-nearest neighbour

Subjects: Data handling techniques; Optimisation techniques; Computer-aided instruction; Other topics in statistics

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