Designing Educational Analytics Tools Using Visualization Based on Behavioral, Motivational, and Social Network Data

  • Iliya L. Musabirov HSE University, 16, Soyuza Pechatnikov st., 190008, Saint Petersburg, Russia
Keywords: educational Analytics, pedagogical design, data analysis, data visualization

Abstract

The article presents a description of the approach to the use of data visualization in various educational Analytics tools when building University courses. In addition to the analysis of educational behavior, socio-psychological approaches, including the theory of expectations and social values, and the social network approach, are separately considered as prospects for analysis. An example of designing training Analytics using modern data analysis and visualization tools is analyzed.

Author Biography

Iliya L. Musabirov, HSE University, 16, Soyuza Pechatnikov st., 190008, Saint Petersburg, Russia

Senior lecturer, Department of Informatics; Junior Research Fellow, Sociology of Education and Science Laboratory HSE University, ilya@musabirov.info

References

G. Siemens and R. S. Baker, “Learning analytics and educational data mining: towards communication and collaboration,” in Proc. of the 2nd Int. Conf. on Learning Analytics and Knowledge, Vancouver, Canada, 2012, pp. 252–254; doi: 10.1145/2330601.2330661

X. Ochoa, “Multimodal learning analytics,” in The Handbook of Learning Analytics A. C. Lang, G. Siemens, A. Wise, and D. Gaˇsevi´c, eds., 2017, pp. 129–141; doi: 10.18608/hla17

I. L. Musabirov and A. V. Sirotkin, “Spetsializatsiya ‘Analiz dannykh’. Virtual’noe obrazovatel’noe okruzhenie s podderzhkoi sredstv obrazovatel’noi analitiki” [Minor in Data Science. Virtual Learning Environment with Learning Analytics Support], Computer tools in education, no. 4, pp. 32–42, 2016 (in Russian).

G. Sedrakyan, J. Malmberg, K. Verbert, S. J¨arvel¨a, and P. A. Kirschner, “Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation,” Computers in Human Behavior, 2018; doi: 10.1016/j.chb.2018.05.004

D. L. Butler and P. H. Winne, “Feedback and self-regulated learning: A theoretical synthesis,” Review of educational research, vol. 65, no. 3, pp. 245–281, 1995; doi: 10.3102/00346543065003245

B. J. Zimmerman, “Self-regulated learning and academic achievement: An overview,” Educational psychologist, vol. 25, no. 1, pp. 3–17, 1990; doi: 10.1207/s15326985ep2501_2

W. Matcha, D. Gasevic, N. A. Uzir, and A. Pardo, “A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective,” IEEE Transactions on Learning Technologies, pp. 1–20, 2019; doi: 10.1109/TLT.2019.2916802

J. Kuzilek, M. Hlosta, and Z. Zdrahal, “Open University Learning Analytics dataset,” Sci. Data, 4:170171, 2017; doi: 10.1038/sdata.2017.171

V. A. Ivanyushina, D. A. Aleksandrov, and I. L. Musabirov, “Struktura akademicheskoi motivatsii: ozhidaniya i sub’ektivnye tsennosti osvoeniya universitetskogo kursa” [The structure of academic motivation: expectations and subjective values of the development of a university course], Voprosy obrazovaniya, no. 4, pp. 229–250, 2016 (in Russian); doi: 10.17323/1814-9545-2016-4-229-250

M. C. Lee, “Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model,” Computers & Education, vol. 54, no. 2, pp. 506–516, 2010; doi: 10.1016/j.compedu.2009.09.002

H. W. Marsh, H. Kuyper, A. J. Morin, P. D. Parker, and M. Seaton, “Big-fish-little-pond social comparison and local dominance effects: Integrating new statistical models, methodology, design, theory and substantive implications,” Learning and Instruction, vol. 33, pp. 50–66, 2014; doi: 10.1016/j.learninstruc.2014.04.002

S. V. Dokuka, D. R. Valeeva, and M. M. Yudkevich, “Koevolyutsiya sotsial’nykh setei i akademicheskikh dostizhenii studentov” [Co-evolution of social networks and academic achievements of students], Voprosy obrazovaniya, no. 3, pp. 44–65, 2015 (in Russian); doi: 10.17323/1814-9545-2015-3-44-65

D. R. Valeeva, O. V. Pol’din, and M. M. Yudkevich, “Svyazi druzhby i pomoshchi pri obuchenii v universitete” [Friendship and Learning Assistance Relationships in the University], Voprosy obrazovaniya, no. 4, pp. 70–84, 2013 (in Russian).

A. S. Pronin, E. V. Veretennik, and A. V. Semenov, “Formirovanie uchebnykh grupp v universitete s pomoshch’yu analiza sotsial’nykh setei” [Formation of study groups in university through social network analysis], Voprosy obrazovaniya, no. 3, pp. 54–73, 2014 (in Russian); doi: 10.17323/1814-9545-2014-3-54-73

C. Abras, D. Maloney-Krichmar, and J. Preece, “User-centered design,” in Encyclopedia of Human Computer Interaction, W. Bainbridge ed., Thousand Oaks: Sage Publications, 2004, pp. 445–456.

D. Hern´andez-Leo, R. Martinez-Maldonado, A. Pardo, J. A. Munoz-Crist ˜ ´obal, and M. J. Rodr´ıguez Triana, “Analytics for learning design: A layered framework and tools,” British Journal of Educational Technology, vol. 50, no. 1, pp. 139–152, 2019; doi: 10.1111/bjet.12645

J. A. Munoz-Crist ˜ ´obal, D. Hern´andez-Leo, L. Carvalho, R. Martinez-Maldonado, K. Thompson, D. Wardak, and P. Goodyear, “4FAD: A framework for mapping the evolution of artefacts in the learning design process,” Australasian Journal of Educational Technology, vol. 34, no. 2, pp. 16–34, 2018; doi: 10.14742/ajet.3706

H. Wickham, “A layered grammar of graphics,” Journal of Computational and Graphical Statistics, vol. 19, no. 1, pp. 3–28, 2010; doi: 10.1198/jcgs.2009.07098

Igraph: Network Analysis and Visualization (version 1.2.4.2). [Online]. Available: https://CRAN.R-project.org/package=igraph

B. Schloerke, J. Crowley, et al., GGally: Extension to “Ggplot2” (version 1.4.0), 2018, [Online]. Available: https://CRAN.R-project.org/package=GGally

T. Pedersen, Ggraph: An Implementation of Grammar of Graphics for Graphs and Networks (version 2.0.0), 2019, [Online]. Available: https://CRAN.R-project.org/package=ggraph

T. Pedersen, Patchwork: The Composer of Plots (version 1.0.0), 2019, [Online]. Available: https://CRAN.R-project.org/package=patchwork

R. Iannone, J. J. Allaire, B. Borges et al., flexdashboard: R Markdown Format for Flexible Dashboards. [Online]. Available: https://CRAN.R-project.org/package=flexdashboard

W. Chang and H. Wickham, ggvis: Interactive Grammar of Graphics, 2019. [Online]. Available: https: //CRAN.R-project.org/package=ggvis

C. Sievert, C. Parmer, T. Hocking, S. Chamberlain, K. Ram, M. Corvellec, and P. Despouy, plotly: Create Interactive Web Graphics via ‘plotly.js’ (Version 4.9.1). [Online]. Available: https://CRAN.R-project.org/package=plotly

C. Xiong, J. Shapiro, J. Hullman, and S. Franconeri, “Illusion of Causality in Visualized Data,”IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 853–862, 2019; doi: 10.1109/TVCG.2019.2934399

A. Mathisen and K. Grønbæk, “Clear visual separation of temporal event sequences,” IEEE Visualization in Data Science (VDS), pp. 7–14, 2017; doi: 10.1109/VDS.2017.8573439

D. Weng, R. Chen, Z. Deng, F. Wu, J. Chen, and Y. Wu, “SRVis: Towards Better Spatial Integration in Ranking Visualization,” IEEE transactions on visualization and computer graphics, vol. 25, no. 1, pp. 459–469, 2018; doi: 10.1109/TVCG.2018.2865126

P. Mylavarapu, A. Yalcin, X. Gregg, and N. Elmqvist, “Ranked-List Visualization: A Graphical Perception Study,” in Proc. of the 2019 CHI Conf. on Human Factors in Computing Systems, 2019, pp. 1–12; doi: 10.1145/3290605.3300422

S. S. Alhadad, “Visualizing Data to Support Judgement, Inference, and Decision Making in Learning Analytics: Insights from Cognitive Psychology and Visualization Science,” Journal of Learning Analytics, vol. 5, no. 2, pp. 60–85, 2018; doi: 10.18608/jla.2018.52.5

W. Paul and R. C. Cunnington, “An exploration of student attitudes and satisfaction in a GAISE influenced introductory statistics course,” Statistics Education Research Journal, vol. 16, no. 2, pp. 487–510, 2017.

H. C. Ong and J. S. Lim, “Identifying Factors Influencing Mathematical Problem Solving among Matriculation Students in Penang,” Pertanika Journal of Social Sciences & Humanities, vol. 22, no. 1, 2014.

A. V. Sirotkin, “Baiesovskie seti doveriya: derevo sochlenenii i ego veroyatnostnaya semantika” [Bayesian confidence networks: the joint tree and its probabilistic semantics], Trudy SPIIRAN, vol. 1, no. 3, pp. 228–239, 2006.

L. Lockyer, E. Heathcote, and S. Dawson, “Informing pedagogical action: Aligning learning analytics with learning design,” American Behavioral Scientist, vol. 57, no. 10, pp. 1439–1459, 2013; doi: 10.1177/0002764213479367

Published
2019-12-28
How to Cite
Musabirov, I. L. (2019). Designing Educational Analytics Tools Using Visualization Based on Behavioral, Motivational, and Social Network Data. Computer Tools in Education, (4), 81-93. https://doi.org/10.32603/2071-2340-2019-4-81-93
Section
Informational systems