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Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning

Journal Article


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Abstract


  • The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and augmentation, in order to maintain them in a low-dimensional, descriptive and interpretable form.

UOW Authors


  •   Sun, Geng (external author)
  •   Lin, Jiayin (external author)
  •   Shen, Jun
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Chen, Huaming (external author)

Publication Date


  • 2020

Citation


  • Sun, G., Lin, J., Shen, J., Cui, T., Xu, D. & Chen, H. (2020). Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning. Lecture Notes in Computer Science, 12149 432-440.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5097&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4070

Number Of Pages


  • 8

Start Page


  • 432

End Page


  • 440

Volume


  • 12149

Place Of Publication


  • Germany

Abstract


  • The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and augmentation, in order to maintain them in a low-dimensional, descriptive and interpretable form.

UOW Authors


  •   Sun, Geng (external author)
  •   Lin, Jiayin (external author)
  •   Shen, Jun
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Chen, Huaming (external author)

Publication Date


  • 2020

Citation


  • Sun, G., Lin, J., Shen, J., Cui, T., Xu, D. & Chen, H. (2020). Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning. Lecture Notes in Computer Science, 12149 432-440.

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5097&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4070

Number Of Pages


  • 8

Start Page


  • 432

End Page


  • 440

Volume


  • 12149

Place Of Publication


  • Germany