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Frontiers in Educational Research, 2022, 5(4); doi: 10.25236/FER.2022.050403.

Management Analysis on the Improvement of Educational Service of University Library under the Environment of Big Data

Author(s)

Jing Bao1, Jun Xu2, Hu Jia3, Liying Xing4, Gengmin Jiang5, Ke Ding6

Corresponding Author:
Jing Bao
Affiliation(s)

1Department of Library, Nanyang Normal University, Nanyang, China

2Department of Civil Engineering and Architecture, Nanyang Normal University, Nanyang, China

Abstract

Under the big data (BD) environment, the main work of university library education service is to carry out corresponding business for college students. In this process, we need to constantly improve our professional knowledge, skill level and message literacy. Therefore, in order to achieve this goal, we must pay attention to the effective collection of reader resources. This paper first introduces the concept, management characteristics and functions of university library education, then expounds the principles of improving university library education service, and uses the method of questionnaire to investigate the current situation of university library education service. Finally, the survey results show that students are not satisfied with the overall image of the librarians and the quality of their staff. At the same time, some students believe that at present, colleges and universities have basically established a perfect learning resource system to provide high-quality and efficient message services for the majority of users. Others believe that college students mainly browse when reading, lacking interests and autonomy. In addition, most readers still maintain a wait-and-see attitude towards library education. Most teachers hope to change the working attitude of library staff, improve the management quality and enhance the working ability of staff.

Keywords

Big Data; University Library; Educational Service; Management Improvement

Cite This Paper

Jing Bao, Jun Xu, Hu Jia, Liying Xing, Gengmin Jiang, Ke Ding. Management Analysis on the Improvement of Educational Service of University Library under the Environment of Big Data. Frontiers in Educational Research (2022) Vol. 5, Issue 4: 9-14. https://doi.org/10.25236/FER.2022.050403.

References

[1] Xu L, Jiang C, Wang J, et al. Information Security in Big Data: Privacy and Data Mining [J]. IEEE Access, 2017, 2(2):1149-1176.

[2] Wamba S F, Angappa G, Papadopoulos T , et al. Big data analytics in logistics and supply chain management[J]. International Journal of Logs Management, 2018:00-00.

[3] Sivarajah U, Kamal M M, Irani Z , et al. Critical analysis of Big Data challenges and analytical methods[J]. Journal of Business Research, 2017, 70:263-286.

[4] Xu W, Zhou H, Cheng N , et al. Internet of Vehicles in Big Data Era[J]. IEEE/CAA Journal of Automatica Sinica, 2018, 5(1):19-35.

[5] Barbu A, She Y, Ding L, et al. Feature Selection with Annealing for Computer Vision and Big Data Learning [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(2):272-286.

[6] Yaoxue, Zhang, Ju, et al. A Survey on Emerging Computing Paradigms for Big Data [J]. Chinese Journal of Electronics, 2017, 26(1):1-12.

[7] Kusiak A. Smart manufacturing must embrace big data [J]. Nature, 2017, 544(7648):23-25.

[8] Zhang Y, Qiu M, Tsai C W, et al. Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data [J]. IEEE Systems Journal, 2017, 11(1):88-95.

[9] Rathore M, Paul A, an Ahmad, et al. Real-Time Big Data Analytical Architecture for Remote Sensing Application [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 8(10):4610-4621.

[10] Xing H, Qian A, Qiu R C, et al. A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory [J]. IEEE Transactions on Smart Grid, 2017, 8(2):674-686.

[11] Wang Y, Kung L A, Byrd T A. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations [J]. Technological Forecasting & Social Change, 2018, 126(JAN.): 3-13.

[12] Kuang L, Hao, Yang L T, et al. A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction [J]. IEEE Transactions on Emerging Topics in Computing, 2017, 2(3):280-291.