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Predicting students' behavioural engagement in microlearning using learning analytics model

Predicting students' behavioural engagement in microlearning using learning analytics model

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The student-centred learning trend is one of the e-learning service factors in universities and schools that have been improved with added values. Now, students can access the e-learning platform on a cloud server with their mobile devices. Several ways and practices in e-learning today include learning management systems (LMS), blended learning, microlearning, mobile learning, open learning, self-learning, and virtual learning. Microlearning refers to the micro perspective in learning contact, education, and exercise. Student engagement is one of the key indicators of a successful implementation of e-learning. Those studies were carried out based on the educational data mining technique, which is widely used in analysing the various patterns of online learning behaviour and predicting learning outcomes. Another popular technique that uses a similar approach but with different focus is learning analytics.

Chapter Contents:

  • 3.1 Introduction
  • 3.2 LA studies
  • 3.3 Methods
  • 3.4 Results
  • 3.4.1 Analysis of using NN
  • 3.4.2 Analysis using LR
  • 3.5 Comparison analysis using NN and LR
  • 3.6 Conclusion
  • 3.7 Future scope
  • References

Inspec keywords: cloud computing; mobile computing; computer aided instruction

Other keywords: microlearning; learning analytics model; open learning; educational data mining technique; self-learning; mobile learning; student behavioural engagement; LMS; online learning behaviour; cloud server; e-learning service factors; mobile devices; blended learning; learning management systems; student-centred learning; virtual learning

Subjects: Ubiquitous and pervasive computing; Internet software; Computer-aided instruction

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