The Construction of Off-School Practice Teaching Base for Investment Major Based on Big Data

  • Yanli WangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


Aiming at the problems in securities investment practice teaching, such as the large gap between school practice teaching and industry demand, the core business connection in off-campus practice teaching is difficult; exploring the construction of off-campus practice bases, and reducing the gap between on-campus practice teaching and industry demand. Based on the construction of traditional off-campus practice teaching bases, this paper proposes an off-campus practice teaching base combining big data technology. The off-campus practice teaching base based on big data technology is an important part of campus construction. It not only meets the needs of “Internet +” vocational education development, but also meets the requirements for training enterprise talents. Based on the system structure of the practice base under big data, combined with the characteristics of 5 V of big data, it comprehensively analyzed the management strategies of the personnel, equipment and teaching process of the base. Through the use cases of the practice base outside the school, in the context of the era of big data, it effectively implemented the “education for the fittest is the best education” modern education concept. In this paper, a questionnaire survey is conducted on the satisfaction of students in a practical teaching base outside the school, and the teaching effect is evaluated. Experimental simulation results show that students have a good response to the experimental base. After training, the temperament and business quality of students have been improved, and teamwork awareness has been enhanced. It can be seen that the construction of a practical teaching base based on the background of big data will greatly integrate the base resources and provide timely feedback and countermeasures to the problems that arise at the base. The questionnaire survey shows that the proportion of students who are satisfied with the management of the practice base is 64%, the basic satisfaction rate is 28%, and the dissatisfied rate is 8%, which indicates that the students are mostly agreeable with the management of the practice base and are basically satisfied. The dissatisfaction accounted for 36% of the total, indicating that there is a certain deviation in the management of the practice base. Based on the big data background, the construction of an off-campus practice base for investment majors has certain research value. Big data technology can be used to improve traditional practice base some flaws.


Investment science Teaching base Big data Off-campus practice 



This work was supported by the research achievement of Jilin Province higher education reform research project “research and practice of collaborative construction of local applied undergraduate investment professional” inside and outside “practice teaching base” [Higher Education Department of Jilin Province (2017) No. 71].


  1. 1.
    Cashin, A.: The transition from university completion to employment for students with autism spectrum disorder. Issues Mental Health Nurs. 39(2), 1–4 (2018)Google Scholar
  2. 2.
    Vijayalakshmi, N.S., Sequeira, A.H.: The nature of mother’s employment on nurturing campus persistence among undergraduate students. Asian Soc. Sci. 13(6), 36–45 (2017)CrossRefGoogle Scholar
  3. 3.
    Hassani, H., Huang, X., Ghodsi, M.: Big data and causality. Ann. Data Sci. 4, 1–24 (2017)CrossRefGoogle Scholar
  4. 4.
    Mikalef, P., Pappas, I.O., Krogstie, J., et al.: Big data analytics capabilities: a systematic literature review and research agenda. IseB 2, 1–32 (2017)Google Scholar
  5. 5.
    Jiang, Y., Huang, Z., Tsang, D.H.K.: Towards max-min fair resource allocation for stream big data analytics in shared clouds. IEEE Trans. Big Data 4(1), 130–137 (2018)CrossRefGoogle Scholar
  6. 6.
    Cai, H., Xu, B., Jiang, L., et al.: IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet of Things J. 4(1), 75–87 (2017)CrossRefGoogle Scholar
  7. 7.
    Caesarius, L.M., Hohenthal, J.: Searching for big data: how incumbents explore a possible adoption of big data technologies. Scand. J. Manage. 34(2), 129–140 (2018)CrossRefGoogle Scholar
  8. 8.
    Mittelstadt, B.: From Individual to group privacy in big data analytics. Philos. Technol. 30(4), 475–494 (2017)CrossRefGoogle Scholar
  9. 9.
    Liu, X., Wang, X., Zhang, W., et al.: Parallel data: from big data to data intelligence. Moshi Shibie yu Rengong Zhineng/Pattern Recognit. Artif. Intell. 30(8), 673–681 (2017)Google Scholar
  10. 10.
    Rosa, A., Chen, L.Y., Binder, W.: Failure analysis and prediction for big-data systems. IEEE Trans. Serv. Comput. 10(6), 984–988 (2017)CrossRefGoogle Scholar
  11. 11.
    Khan, S., Liu, X., Shakil, K.A., et al.: A survey on scholarly data: from big data perspective. Inf. Process. Manage. 53(4), 923–944 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Jilin Engineering Normal UniversityChangchunChina

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