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The Journal of Supercomputing

Erschienen in:

24.01.2018

A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment

verfasst von: Jianbo Wen, Wei Zhang, Wanneng Shu

Erschienen in: The Journal of Supercomputing | Ausgabe 2/2019

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Abstract

In the era of big data, the traditional distance learning method cannot effectively distinguish the weight of each course attribute, ignoring the recognition process of knowledge cognition law in higher education institutions. In order to better study the learning motivation, learning effect, and cognitive process of distance learners, a chaos optimization cognitive learning model based on chaos optimization and big data analysis is designed in this paper. The proposed model takes into account the learners’ learning motivation, learning task demands, and the change rate of cognitive rules and transforms the learning process of distance learning into a multi-objective optimization problem. The experimental results show that the proposed model can effectively improve the teaching quality of distance education courses in higher education institutions, and the model is scalable and compatible.

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Titel: A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment
verfasst von: Jianbo Wen
Wei Zhang
Wanneng Shu
Publikationsdatum: 24.01.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 2/2019
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2256-2

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