Abstract
Human-centered learning analytics (HCLA) is an approach that emphasizes the human factors in learning analytics and truly meets user needs. User involvement in all stages of the design, analysis, and evaluation of learning analytics is the key to increase value and drive forward the acceptance and adoption of learning analytics. Visual analytics is a multidisciplinary data science research field that follows a human-centered approach and thus has the potential to foster the acceptance of learning analytics. Although various domains have already made use of visual analytics, it has not been considered much with respect to learning analytics. This paper explores the benefits of incorporating visual analytics concepts into the learning analytics process by (a) proposing the Learning Analytics and Visual Analytics (LAVA) model as enhancement of the learning analytics process with human in the loop, (b) applying the LAVA model in the Open Learning Analytics Platform (OpenLAP) to support human-centered indicator design, and (c) evaluating how blending Learning Analytics and Visual Analytics can enhance the acceptance and adoption of learning analytics, based on the technology acceptance model (TAM).
Keywords
- Human-centered learning analytics
- Open learning analytics
- Visual analytics
- Acceptance
- Adoption
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Bakharia, A., & Dawson, S. (2011). SNAPP: A bird’s-eye view of temporal participant interaction. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 168–173). ACM.
Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., et al. (2018). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 41–50). ACM.
Brooke, J. (1996). SUS-A quick and dirty usability scale. In P. W. Jordan, B. Thomas, I. L. McClelland, & B. Weerdmeester (Eds.), Usability evaluation in industry (pp. 189–194). London: CRC Press.
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318–331.
Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., et al. (2014). Learning analytics: Challenges and future research directions. eleed, 10(1).
Chatti, M. A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. International Review of Research in Open and Distributed Learning, 20(1).
Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an open learning analytics ecosystem. In B. Kei Daniel (Ed.), Big data and learning analytics in higher education: current theory and practice (pp. 195–219). Cham, Switzerland: Springer International Publishing.
Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134–138). ACM.
Corbin, J. M., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13(1), 3–21.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Dix, A., Finlay, J. E., Abowd, G. D., & Beale, R. (2003). Human-computer interaction (3rd ed.). Upper Saddle River, NJ: Prentice-Hall, Inc..
Endert, A., Hossain, M. S., Ramakrishnan, N., North, C., Fiaux, P., & Andrews, C. (2014). The human is the loop: New directions for visual analytics. Journal of Intelligent Information Systems, 43(3), 411–435.
Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 56–65). ACM.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
Jovanovic, J., Gasevic, D., Brooks, C., Devedzic, V., Hatala, M., Eap, T., et al. (2008). LOCO-Analyst: Semantic web technologies in learning content usage analysis. International Journal of Continuing Engineering Education And Life Long Learning, 18(1), 54–76.
Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges. In Information visualization (pp. 154–175). Berlin\Heidelberg, Germany: Springer.
Keim, D. A., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in visual data analysis. In Tenth International Conference on Information Visualisation (IV’06) (pp. 9–16). IEEE.
Keim, D. A., Mansmann, F., Stoffel, A., & Ziegler, H. (2009). Visual analytics. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems. Boston: Springer.
Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., & Kloos, C. D. (2012). GLASS: A learning analytics visualization tool. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 162–163). ACM.
Lukarov, V., Chatti, M. A., Thüs, H., Kia, F. S., Muslim, A., Greven, C., & Schroeder, U. (2014). Data models in learning analytics. In Proceedings of DeLFI Workshops (pp. 88–95).
Muslim, A., Chatti, M. A., Bashir, M. B., Varela, O. E. B., & Schroeder, U. (2018). A modular and extensible framework for open learning analytics. Journal of Learning Analytics, 5(1), 92–100.
Muslim, A., Chatti, M. A., Mahapatra, T., & Schroeder, U. (2016). A rule-based indicator definition tool for personalized learning analytics. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 264–273). ACM.
Muslim, A., Chatti, M. A., Mughal, M., & Schroeder, U. (2017). The goal-question-indicator approach for personalized learning analytics. In Proceedings of the 9th international conference on computer supported education CSEDU (1) (pp. 371–378).
Nielsen, J. (1994). Usability inspection methods. In Conference companion on, human factors in computing systems, CHI ’94 (pp. 413–414). New York: ACM.
Ritsos, P. D., & Roberts, J. C. (2014). Towards more visual analytics in learning analytics. In Proceedings of the 5th EuroVis Workshop on Visual Analytics (pp. 61–65).
Thomas, J. J., & Cook, K. A. (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Press,
Thüs, H., Chatti, M. A., Greven, C., & Schroeder, U. (2014). Kontexterfassung,-modellierung und-auswertung in Lernumgebungen. DeLFI 2014-Die 12. In e-Learning Fachtagung Informatik (pp. 157–162). Gesellschaft für Informatik.
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., et al. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499–1514.
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Chatti, M.A., Muslim, A., Guliani, M., Guesmi, M. (2020). The LAVA Model: Learning Analytics Meets Visual Analytics. In: Ifenthaler, D., Gibson, D. (eds) Adoption of Data Analytics in Higher Education Learning and Teaching. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-47392-1_5
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