Minor in Data Science. Virtual Learning Environment with Learning Analytics Support
Keywords:
virtual learning environment, data analysis, learning analytics, machine learning, social network analysis
Abstract
This paper describes the design of a bootstrapped Virtual Learning Environment, developed for the undergraduate minor in Data Processing and Analysis. The environment is designed and deployed simultaneously with Learning Analytics subsystem to support students of diverse academic background studying in mixed groups. It includes integrated data analysis and statistical graphics environment, data applications server, interactive exercises system, tools for online communication and peer support, and progress tracking for students. Learning analytics subsystem includes analytical routines for behavioral logs, context-based surveys with a network component, and tools for analysis of online communication among the participants. The focus of Learning Analytics subsystem is on the social side of learning. Thus it includes advanced instruments for Social Network Analysis.
References
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[3] S. R. Hiltz and B. Wellman, “Asynchronous Learning Networks As a Virtual Classroom,” Communications of the ACM, vol. 40, no. 9, pp. 44–49, 1997; doi: 10.1145/260750.260764
[4] D. R. Valeeva, O. V. Poldin, and M. M. Yudkevich, “Svyazi druzhby i pomoshchi pri obuchenii v universitete” [Friendly Relationships and Relationships of Assistance at a University], Educational Studies, no. 4, pp. 70–84, 2013 (in Russian).
[5] J. M. Wing, “Computational thinking,” Communications of the ACM, vol. 49, no. 3, pp. 33–35, 2006; doi: 10.1145/1118178.1118215
[6] E. K. Khenner, “Vychislitel'noe myshlenie“ [Computational thinking], The Education and science journal, no. 2, pp. 18–33, 2016 (in Russian); doi:10.17853/1994-5639-2016-2-18-33
[7] F. Provost and T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, Sebastopol, CA: O’Reilly Media, 2013.
[8] D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, New York, NY: Cambridge University Press, 2010.
[9] C. Cioffi-Revilla, Introduction to Computational Social Science: Principles and Applications, London; New York: Springer, 2014.
[10] A. C. Pronin, E. V. Veretennik, and A. V. Semyonov, “Formirovanie uchebnykh grupp v universitete s pomoshch'yu analiza sotsial'nykh setei” [Grouping University Students Using Social Network Analysis], Educational Studies, no. 3, pp. 54–73, 2014 (in Russian).
[11] R Core Team, “R: A Language and Environment for Statistical Computing,” In R Foundation for Statistical Computing, Vienna, Austria, 2015.
[12] C. Gandrud, Reproducible Research with R and R Studio, CRC Press, 2013.
[13] D. E. Knuth, “Literate programming,” The Computer Journal, vol. 27, no. 2, pp. 97–111, 1984; doi:10.1093/comjnl/27.2.97
[14] J. J. Allaire et al., rmarkdown: Dynamic Documents for R, R package version 0.8.1, 2015.
[15] N. Carchedi et al., swirl: Learn R, in R, R package version 2.2.21. 2015.
[16] G. Siemens and D. Gasevic, “Guest editorial-learning and knowledge analytics,” Journal of Educational Technology & Society, vol. 15, no. 3, pp. 1–2, 2012.
[17] L. M. Vaquero and M. Cebrian, “The rich clubphenomenon in the classroom,” Scientific reports, vol. 3, 2013; doi: 10.1038/srep01174
[18] R. Ferguson “Learning analytics: drivers, developments and challenges,” International Journal of Technology Enhanced Learning, vol. 4 Learning analytics, no. 5–6, pp. 304–317, 2012; doi:10.1504/IJTEL.2012.051816
[19] M. Huisman and T. A. Snijders “Statistical analysis of longitudinal network data with changing composition,” Sociological methods & research, Vol. 32, no. 2, pp. 253–287, 2003.
[20] C.T. Butts “A relational event framework for social action,” Sociological Methodology, vol. 38, no. 1, pp. 155–200, 2008.
[21] G. L. Robins, Doing Social Network Research: Network-based Research Design for Social Scientists, SAGE Publications Ltd, 2015.
[22] D. Vu, P. Pattison, and G. Robins, “Relational event models for social learning in MOOCs,” Social Networks, vol. 43, pp. 121–135, 2015; doi:10.1016/j.socnet.2015.05.001
Published
2017-06-04
How to Cite
Мусабиров, И. Л., & Сироткин, А. В. (2017). Minor in Data Science. Virtual Learning Environment with Learning Analytics Support. Computer Tools in Education, (4), 32-42. Retrieved from http://cte.eltech.ru/ojs/index.php/kio/article/view/1405
Issue
Section
Training of specialits: studying programms
This work is licensed under a Creative Commons Attribution 4.0 International License.
