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Exploring the Cultivation of Innovative Talents in the Era of Big Data and Cloud Computing

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DOI: 10.23977/aduhe.2023.050103 | Downloads: 35 | Views: 686

Author(s)

Yajie Li 1, He Li 1

Affiliation(s)

1 School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

Corresponding Author

Yajie Li

ABSTRACT

Higher education shoulders the important mission of high-level talent training and innovation and creation, and is the cornerstone of national development and social progress. The rapid development of computer technology in the future requires a large number of composite talents in statistics, data analysis and computer application. With the goal of academic success and the spirit of scientists, we have designed a training program for data visualization and artificial intelligence applications, designed a training program for the integration of business analysis and computing technology, and conducted research on the training mode of cross integration of science and engineering. Through the curriculum practice of multivariate statistical analysis and data analysis, and through conscious and organized learning design, diversified learning activities are organized. From students' homework and classroom performance, it is fed back that students can comprehensively use R, SPSS and other computer software to conduct data mining, and students can obtain information from multiple perspectives from real problems, and think at a higher level, so that students can exercise their innovation ability, it has enhanced the interdisciplinary ability.

KEYWORDS

Computer technology, artificial intelligence, business analysis, data analysis, learning design, innovation ability

CITE THIS PAPER

Yajie Li, He Li, Exploring the Cultivation of Innovative Talents in the Era of Big Data and Cloud Computing. Adult and Higher Education (2023) Vol. 5: 19-27. DOI: http://dx.doi.org/10.23977/aduhe.2023.050103.

REFERENCES

[1] Lehmann, T. Student Teachers’ Knowledge Integration across Conceptual Borders: The Role of Study Approaches, Learning Strategies, Beliefs, and Motivation. Eur J Psychol Educ, 37, 1189–1216 (2022).
[2] Janssen, N., & Lazonder, A. W. Supporting Pre-Service Teachers In Designing Technology-Infused Lesson Plans. Journal of Computer Assisted Learning, 32(5), 456–467(2016).
[3] Cruz-Ramírez, S.R., García-Martínez, M. & Olais-Govea, J.M. NAO Robots as Context to Teach Numerical Methods. Int J Interact Des Manuf, 16, 1337–1356 (2022).
[4] Moradi, M., Noor, N.F.B.M. & Abdullah, R.B.H. The Effects of Problem-Based Serious Games on Learning 3D Computer Graphics. Iran J Sci Technol Trans Electr Eng, 46, 989–1004 (2022).
[5] Brown, R.B. Transdisciplinary Model for Environmental Protection and Primordial Prevention of Disease. J Environ Stud Sci, 12, 898–904 (2022).
[6] Azer, S. A., & Azer, D. Group Interaction in Problem-Based Learning Tutorials: A Systematic Review. European Journal of Dental Education, 19(4), 194–208(2015).
[7] Xie, T., Zheng, L., Liu, G. et al. Exploring Structural Relations Among Computer Self-Efficacy, Perceived Immersion, And Intention To Use Virtual Reality Training Systems. Virtual Reality, 26, 1725–1744 (2022).
[8] Awofala AO, Olabiyi OS, Awofala AA et al. Attitudes toward Computer, Computer Anxiety and Gender as Determinants of Pre-Service Science, Technology, and Mathematics Teachers’ Computer Self-Efficacy. Digital Educ Rev, 36, 51–67(2019).
[9] Bogusevschi D, Muntean C, Muntean GM. Teaching and Learning Physics Using 3d Virtual Learning Environment: A Case Study of Combined Virtual Reality and Virtual Laboratory in Secondary School. J Comput Math Sci Teach, 39(1), 5–18(2020).
[10] Boden D, Borrego M, Newswander L K.Student Socialization in Interdisciplinary Doctoral Education. Higher Education, 6,741-755(2011).
[11] Chen X, Jia S, Xiang Y.A review:Knowledge Reasoning over Knowledge Graph. Expert Systems with Application, 141(5), 112948(2020).
[12] Lin J, Zhao Y, Huang W, et al. Domain Knowledge Graphbased Research Progress Of Knowledge Representation. Neural Computing and Applications, 33(2), 681-690(2021).
[13] Qiu X, Sun T, Xu Y, et al. Pre-Trained Models for Natural Language Processing: A Survey.Science China Technological Sciences, 63(10), 1872-1897(2020).
[14] Shao B, Li X, Bian G.A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph. Expert Systems with Applications, 165, 113764(2021).
[15] Liu W, Zhou P, Zhao Z, et al. K-bert: Enabling Language Representation with Knowledge Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 34(3), 2901-2908(2020).
[16] Anscombe, F.J. Graphs in Statistical Analysis. American Statistician, 27, 17-21(1973).

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