Abstract
The open university is carrying out various types of academic and non-academic education to serve the city’s citizens and usually has gained a good reputation in the local area. However, previously, due to its inadequate hardware system, fragmented information system, and incomplete data collection, it carried out various operations more by empirical judgment and lacked objective data support, thus making it impossible to promote the development of a lifelong education system accurately. By collecting, integrating, and analyzing these lifelong education projects, converting them into lifelong education big data, and establishing various lifelong education data visualization and analysis models, we can promote the transformation of life-long education from discrete demand to education big data precision-driven development mode, provide support for the supply-side structural reform of lifelong education, and at the same time improve the leading role of the open university in the construction of lifelong education system, thus promoting the high-quality development of lifelong education for citizens.
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References
Taşçı, G., Titrek, O.: Evaluation of lifelong learning centers in higher education: a sustainable leadership perspective. Sustainability 12(1), 22 (2020)
Luan, H., Geczy, P., Lai, H., et al.: Challenges and future directions of big data and artificial intelligence in education. Front. Psychol. 11, 580820 (2020)
Ang, K.L.M., Ge, F.L., et al.: Big educational data & analytics: survey, architecture and challenges. IEEE Access 8, 116392–116414 (2020)
Hu, H., Zhang, G., Gao, W., Wang, M.: Big data analytics for MOOC video watching behavior based on Spark. Neural Comput. Appl. 32(11), 6481–6489 (2019). https://doi.org/10.1007/s00521-018-03983-z
Baturay, M.H.: An overview of the world of MOOCs. Proc. Soc. Behav. Sci. 174, 427–433 (2015)
Dai, H.M., Teo, T., Rappa, N.A., et al.: Explaining Chinese university students’ continuance learning intention in the MOOC setting: a modified expectation confirmation model perspective. Comput. Educ. 150, 103850 (2020)
Jung, E., Kim, D., Yoon, M., et al.: The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Comput. Educ. 128, 377–388 (2019)
Son, N.T., Jaafar, J., Aziz, I.A., et al.: Meta-heuristic algorithms for learning path recommender at MOOC. IEEE Access 9, 59093–59107 (2021)
Ruipérez-Valiente, J.A., Halawa, S., Slama, R., et al.: Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world. Comput. Educ. 146, 103776 (2020)
Farr, A., Aliberti, S., Loukides, S., et al.: A pathway to keep all lifelong learners up to date: the ERS continuing professional development programme. Eur. Respir. J. 55(2), 1902425 (2020)
Castro, A., Villagrá, V.A., García, P., et al.: An ontological-based model to data governance for big data. IEEE Access 9, 109943–109959 (2021)
Zhou, X.: The establishment of education quality evaluation system and standards of open universities. Agro Food Ind. Hi Tech 28(1), 3061–3063 (2017)
Mandinach, E.B., Schildkamp, K.: Misconceptions about data-based decision making in education: an exploration of the literature. Stud. Educ. Eval. 69, 100842 (2021)
Xu, H., He, Q., Li, X., et al.: BDSS-FA: a blockchain-based data security sharing platform with fine-grained access control. IEEE Access 8, 87552–87561 (2020)
Zhang, D., Wang, Y., Liu, Z., et al.: Improving NoSQL storage schema based on Z-curve for spatial vector data. IEEE Access 7, 78817–78829 (2019)
Qiu, M., Cao, D., Su, H., Gai, K.: Data transfer minimization for financial derivative pricing using Monte Carlo simulation with GPU in 5G. Int. J. Commun. Syst. 29(16), 2364–2374 (2016)
Lu, R., Jin, X., Zhang, S., Qiu, M., Wu, X.: A study on big knowledge and its engineering issues. IEEE Trans. Knowl. Data Eng. 31(9), 1630–1644 (2018)
Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)
Acknowledgments
This work was supported by grants from the Natural Science Foundation of Guangdong Province No. 2018A0303130082, The Features Innovation Program of the Department of Education of Foshan No. 2019QKL01, Basic and Applied Basic Research Fund of Guangdong Province No. 2019A1515111080.
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Ma, L., Yang, Z., Yang, W., Yang, H., Lao, Q. (2022). Study on the Organization and Governance of Bigdata for Lifelong Education. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_45
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