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2020 | OriginalPaper | Buchkapitel

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

verfasst von : Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen

Erschienen in: Intelligent Tutoring Systems

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning
verfasst von
Geng Sun
Jiayin Lin
Jun Shen
Tingru Cui
Dongming Xu
Huaming Chen
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-49663-0_54