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Mining sequential patterns with flexible constraints from MOOC data.

Wei Song1, Wei Ye1, Philippe Fournier-Viger2

  • 1School of Information Science and Technology, North China University of Technology, Beijing, 100144 China.

Applied Intelligence (Dordrecht, Netherlands)
|March 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces flexible constraints for mining sequential patterns in massive open online course (MOOC) enrollment data. The new method enhances efficiency and reduces patterns, improving online learning insights.

Keywords:
Downward closure propertyMOOCSequential patternSupport with flexible constraints

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Area of Science:

  • Education Technology
  • Data Mining
  • Computer Science

Background:

  • Online learning is crucial in modern education.
  • Massive Open Online Courses (MOOCs) generate vast learner data.
  • Sequential Pattern Mining (SPM) can extract valuable insights from this data.

Purpose of the Study:

  • To propose a novel approach for mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data.
  • To introduce three specific constraints: length, discreteness, and validity, to refine pattern discovery.
  • To enhance the efficiency of SPM by integrating these constraints into a new parameter, Support with Flexible Constraints (SFC).

Main Methods:

  • Developed three flexible constraints (length, discreteness, validity) for SPM.
  • Integrated these constraints into the support parameter, creating Support with Flexible Constraints (SFC).
  • Proposed two algorithms (breadth-first and depth-first) to discover SPs using SFC.

Main Results:

  • SFC satisfies the downward closure property, crucial for efficient mining.
  • The proposed algorithms effectively reduce the number of discovered patterns.
  • The algorithms demonstrate comparable performance to classical SPM methods while offering more refined results.

Conclusions:

  • Flexible constraints significantly improve the effectiveness of SPM in MOOC data.
  • The SFC parameter and associated algorithms offer a more efficient and targeted approach to knowledge discovery in online learning.
  • This research contributes to enhancing the online learning experience through advanced data analysis techniques.