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

Authors

Geng Sun, University Of WollongongFollow
Jiayin Lin, University of WollongongFollow
Jun Shen, University of WollongongFollow
Tingru Cui, University of WollongongFollow
Dongming Xu
Huaming Chen, University of WollongongFollow

RIS ID

143556

Publication Details

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.

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.