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Smart e-learning transition using big data: perspectives and opportunities

Smart e-learning transition using big data: perspectives and opportunities

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E-learning is providing education through a computing platform that encourages the learners to learn from anywhere at anytime. Web 5.0 will be able to map the emotions of the people when they are interacting with computers. The users can interact with content with the help of a headphone. While interacting with the content, their emotions are captured by the facial recognition system to bring real classroom learning into the existing e-learning system. Any e-learning approach will lead to the explosion of different types of information such as text, videos, and images, results in different data types that are not used in traditional data management systems. Analytical operations cannot be applied directly to these data. The online learning platforms generate enormous learner behavioral data, and educational big data plays an important role in transforming the data obtained from online learning platforms into useful information for the improvement of academic activities. The teachers can develop the content for personalized learning analyzing the current knowledge level of the students. The students have the opportunity to learn at their own pace. The key issue here is the effective analysis and utilization of the data to improve the e-learning features. Big data technology provides the capability of analytics to enhance the e-learning process. This chapter presents the outline of the big data techniques such as prediction, clustering, relationship mining, structure discovery, and various tools used for big data analytics in e-learning.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 Big data applications in e-learning
  • 7.2.1 Performance prediction
  • 7.2.2 Attrition risk detection
  • 7.2.3 Data visualization
  • 7.2.4 Intelligent feedback
  • 7.2.5 Course recommendation
  • 7.2.6 Student skill estimation
  • 7.2.7 Behavior detection
  • 7.2.8 Collaboration and social network analysis
  • 7.2.9 Developing concept maps
  • 7.2.10 Constructing courseware
  • 7.2.11 Planning and scheduling
  • 7.3 Big data techniques for e-learning
  • 7.3.1 Classification in e-learning
  • Fuzzy logic
  • ANN and evolutionary computation
  • Association rule
  • 7.4 Big data tools
  • 7.4.1 Hadoop platform for e-learning
  • Apache Hadoop
  • Hadoop Distributed File System
  • MapReduce
  • YARN
  • 7.4.2 Spark
  • 7.4.3 Orange
  • 7.5 Recent research perspectives and future direction
  • 7.5.1 Future direction
  • 7.6 Conclusion
  • References

Inspec keywords: computer aided instruction; data mining; learning (artificial intelligence); Big Data; Internet; data analysis

Other keywords: facial recognition system; online learning platforms; enormous learner behavioral data; classroom learning; e-learning approach; personalized learning; e-learning process; big data techniques; e-learning features; educational big data; existing e-learning system; traditional data management systems; big data analytics; computing platform; big data technology; smart e-learning transition

Subjects: Data handling techniques; Computer-aided instruction; Information networks

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