Проектирование инструментов учебной аналитики с использованием визуализации на основе поведенческих, мотивационных и социально-сетевых данных

  • Илья Леонидович Мусабиров Национальный исследовательский университет «Высшая школа экономики», ул. Союза Печатников, 16, 190008, Санкт-Петербург, Россия
Ключевые слова: учебная аналитика, визуализация данных, педагогический дизайн, анализ данных

Аннотация

В статье представляется описание подхода к применению визуализации данных в инструментах учебной аналитики при построении университетских курсов. Помимо анализа образовательного поведения отдельно в качестве перспективы анализа рассматриваются социально-психологические подходы, в том числе теория ожиданий и ценностей, социально-сетевой подход. Разбирается пример проектирования учебной аналитики с применением современных инструментов анализа и визуализации данных.

Биография автора

Илья Леонидович Мусабиров, Национальный исследовательский университет «Высшая школа экономики», ул. Союза Печатников, 16, 190008, Санкт-Петербург, Россия

Cтарший преподаватель, департамент информатики; младший научный сотрудник, НУЛ "Социология образования и науки" НИУ ВШЭ, ilya@musabirov.info

Литература

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. 1–1, 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 HumanComputer 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´ıguezTriana, “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.Rproject.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.Rproject.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, 2020; 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 GAISEinfluenced 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.

, 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.

Опубликован
2019-12-28
Как цитировать
Мусабиров, И. Л. (2019). Проектирование инструментов учебной аналитики с использованием визуализации на основе поведенческих, мотивационных и социально-сетевых данных. Компьютерные инструменты в образовании, (4), 81-93. https://doi.org/10.32603/2071-2340-2019-4-81-93
Выпуск
Раздел
Информационные системы