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

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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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Acknowledgements

This work was supported by Ministry of Education Project of Humanities and Social Sciences in China (Grant No.17YJAZH120), Education and Teaching Method project in Central University of Finance and Economics (Grant No.040950317001), the foundation for Key Research Items in Teaching Reform Program of Central University of Finance and Economics (Grant No. JG201610) and the Key Research Grant of School of Foreign Studies of Central University of Finance and Economics (Grant No. 20141010), and the National Science Foundation of China (Grant No. 61603420). We wish to thank the anonymous reviewers who helped to improve the quality of the paper.

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Correspondence to Wei Zhang.

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Wen, J., Zhang, W. & Shu, W. A cognitive learning model in distance education of higher education institutions based on chaos optimization in big data environment. J Supercomput 75, 719–731 (2019). https://doi.org/10.1007/s11227-018-2256-2

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  • DOI: https://doi.org/10.1007/s11227-018-2256-2

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