Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

Article Preview

Abstract:

The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

348-355

Citation:

Online since:

April 2021

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2021 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Atiquzzaman M, Yen N, Xu Z, Big Data Analytics for Cyber-Physical System in Smart City, Berlin, (2020).

Google Scholar

[2] Thippa RG , Praveen KRM , Lakshmanna K, et al. Analysis of Dimensionality Reduction Techniques on Big Data[J]. IEEE Access, 2020, PP(99):1-1.

Google Scholar

[3] Deepa N., Pham Q.V., Nguyen D.C., Bhattacharya S., Gadekallu T.R., Maddikunta P.K.R., Fang F., Pathirana P.N., A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions,2020,arXiv preprint arXiv:2009.00858.

DOI: 10.1016/j.future.2022.01.017

Google Scholar

[4] A.V. Bataev, Analysis of the Application of Big Data Technologies in the Financial Sphere," 2018 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies, (IT&QM&IS), St. Petersburg, 2018, pp.568-572.

DOI: 10.1109/itmqis.2018.8525121

Google Scholar

[5] Han Young Joo, Jae Wook Kim, Joo Hyun Moon,Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea,Nuclear Engineering and Technology,Volume 52, Issue 8,2020,Pages 1798-1806.

DOI: 10.1016/j.net.2020.01.015

Google Scholar

[6] Lingjun Song, Keyao Zhang, Tongyi Liang, Xuebing Han, Yingjie Zhang,Intelligent state of health estimation for lithium-ion battery pack based on big data analysis,Journal of Energy Storage,Volume 32,2020,101836.

DOI: 10.1016/j.est.2020.101836

Google Scholar

[7] Nishita Mehta, Anil Pandit, Sharvari Shukla,Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study,Journal of Biomedical Informatics,Volume 100,2019,103311.

DOI: 10.1016/j.jbi.2019.103311

Google Scholar

[8] Xiao Luo, Liang Dong, Yi Dou, Ning Zhang, Jingzheng Ren, Ye Li, Lu Sun, Shengyong Yao,Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China,Journal of Cleaner Production,Volume 142, Part 2, 2017,Pages 926-935.

DOI: 10.1016/j.jclepro.2016.05.161

Google Scholar

[9] J. Zhao and J. Guo, Big data analysis technology application in agricultural intelligence decision system,, 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, 2018, pp.209-212.

DOI: 10.1109/icccbda.2018.8386513

Google Scholar

[10] Y. Liu, Big Data Technology and Its Analysis of Application in Urban Intelligent Transportation System,, 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Xiamen, 2018, pp.17-19.

DOI: 10.1109/icitbs.2018.00012

Google Scholar

[11] Schmitz GL, Nogara PA, Natiéle Medina, et al. Cockroaches: an alternative model to teach enzymatic inhibition to undergraduate students[J].

Google Scholar

[12] J. Cui, Y. Wang, S. Fan, Z. Wang and S. Xu, Discussion on Computer Education Mode of Engineering Course in Independent College,, 2008 The 9th International Conference for Young Computer Scientists, Hunan, 2008, pp.2391-2395.

DOI: 10.1109/icycs.2008.375

Google Scholar

[13] J. Gu, J. Zhao and S. Zhang, Discussion on teaching reform of computer application fundamental course in Chinese Universities,, 2010 5th International Conference on Computer Science & Education, Hefei, 2010, pp.824-827.

DOI: 10.1109/iccse.2010.5593486

Google Scholar

[14] C. Zhang, L. Yang and H. Wang, Practice teaching reform of the course visual basic programing based on capability orientation,, 2016 11th International Conference on Computer Science & Education (ICCSE), Nagoya, 2016, pp.899-902.

DOI: 10.1109/iccse.2016.7581701

Google Scholar

[15] Shen Qing, Jiang Yunliang and Su Xiaoping, Exploration on practice teaching in computer major,, 2010 International Conference on Educational and Information Technology, Chongqing, 2010, pp. V1-416-V1-418.

DOI: 10.1109/iceit.2010.5607660

Google Scholar

[16] M. Garduño-Aparicio, J. Rodríguez-Reséndiz, G. Macias-Bobadilla and S. Thenozhi, A Multidisciplinary Industrial Robot Approach for Teaching Mechatronics-Related Courses,, in IEEE Transactions on Education, vol. 61, no. 1, pp.55-62, Feb. (2018).

DOI: 10.1109/te.2017.2741446

Google Scholar

[17] R. Precup, S. Preitl, M. Radac, E.M. Petriu, C. Dragos and J.K. Tar, Experiment-Based Teaching in Advanced Control Engineering,, in IEEE Transactions on Education, vol. 54, no. 3, pp.345-355, Aug. (2011).

DOI: 10.1109/te.2010.2058575

Google Scholar

[18] D. d. M. Magnus, L.F.B. Carbonera, L.L. Pfitscher, F.A. Farret, D.P. Bernardon and A.A. Tavares, An Educational Laboratory Approach for Hybrid Project-Based Learning of Synchronous Machine Stability and Control: A Case Study,, in IEEE Transactions on Education, vol. 63, no. 1, pp.48-55, Feb. (2020).

DOI: 10.1109/te.2019.2956909

Google Scholar