Abstract
Learning Management Systems (LMSs) are becoming more and more popular and incorporate many different functionalities. For this reason, an evaluation of the quantitative utilization of all the parts of a LMS is essential. In this research we propose indicators and techniques which allow to understand in detail how a functionality is accessed by the users. These analytic tools are useful in particular for the administrators of the LMS which are in charge of allocating resources according to the workload and importance of the functionalities. We tested the proposed indicators with the data obtained from the LMS of Università degli Studi di Milano-Bicocca (Milan, Italy) about the messaging functionality. Although the students’ messages can potentially be a source of big data, in the present case it is observed that the utilization is limited. With this analysis it has been possible to notice a similarity between the utilization of the message system and the empirical Zipf law. We also introduced the description of the structure of a dashboard which allows to access to the indicators and goes towards the definition of a global tool for students, teachers and administrators.
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References
Meluso, F., Avogadro, P., Calegari, S., Dominoni, M.: Success of the functionalities of a learning management system. In: DATA 2017 6th International Conference on Data Science, Technology and Applications, 24–26 July 2017, Madrid, Spain (2017)
Avogadro, P., Calegari, S., Dominoni, M.: Analyzing social learning management systems for educational environments. In: Fred, A., Dietz, J.L.G., Aveiro, D., Liu, K., Filipe, J. (eds.) IC3K 2015. CCIS, vol. 631, pp. 470–491. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-52758-1_25
Avogadro, P., Calegari, S., Dominoni, M.: Expert students in social learning management systems. Interact. Technol. Smart Educ. 13(3), 202–217 (2016)
Dalsgaard, C.: Social software: e-learning beyond learning management systems. Eur. J. Open Distance E-Learning 2(1), 1–9 (2006)
Ferguson, R.: Learning analytics don’t just measure students progress they can shape it (2014). theguardian.com
Filiz, O., Kurt, A.: Flipped learning: misunderstanding and the truth. J. Educ. Res. 5(1), 215–229 (2015)
Kingsley, G.: Human Behavior and the Principle of Least Effort. Addison-Wesley Press, Cambridge (1946)
Lukarov, V., Chatti, M.A., Schroeder, U.: Learning analytics evaluation - beyond usability. In: Proceedings of DeLFI Workshops 2015 Co-located with 13th e-Learning Conference of the German Computer Society (DeLFI 2015), Seiten/Artikel-Nr: 123–131 (2015)
Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an early warning system for educators: a proof of concept. Comput. Educ. 54, 588–599 (2010)
Mödritscher, F., Andergassen, M., Neumann, G.: Dependencies between e-learning usage patterns and learning results. In: Lindstaedt, S., Granitzer, M. (eds.) Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies (i-Know 2013), article 24, 8 pages. ACM, New York (2013)
Moodlerooms. XRay learning analytics 2017. https://www.moodlerooms.com/resource/x-ray-learning-analytics/. Accessed 26 Apr 2017
Rabbany, R., Takaffoli, M., Zaïane, O.R.: Analyzing participation of students in online courses using social network analysis techniques. In: Proceedings of the 4th International Conference on Educational Data Mining, Eindhoven, The Netherlands, 6–8 July 2011 (2011)
Romero, C., Ventura, S., García, E.: Data mining in course management systems: Moodle case study and tutorial. Comput. Educ. 51(1), 368–384 (2007)
Scheffel, M.: A framework of quality indicators for learning analytics. Learn. Anal. Rev. no. 2 (2015). ISSN 2057-7494
Scheffel, M., Drachsler, H., Specht, M.: Developing an evaluation framework of quality indicators for learning analytics. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge LAK 2015, pp. 16–20. ACM Press, New York (2015)
Scheffel, M., Drachsler, H., Stoyanov, S.: Quality indicators for learning analytics. Educ. Technol. Soc. 17(4), 117–132 (2014)
Sclater, N., Peasgood, A., Mullan, J.: Learning analytics in higher education. Jisc (2016)
Susilo, A.: Exploring facebook and whatsapp as supporting social network applications for English learning in higher education. In: PDE Professional Development in Education Conference 2014, 11–12 June 2014, Park Hotel Bandung (2014)
Wang, Q., Woo, H.L., Quek, C.L., Yang, Y., Liu, M.: Using the facebook group as a learning management system: an exploratory study. Comput. Educ. 54, 588–599 (2010)
Zipf, G.K.: The Psychobiology of Language. Houghton-Mifflin, Oxford (1935)
Graf, S., Ives, C., Rahman, N., Ferri, A.: AAT: a tool for accessing and analysing students’ behavior data in learning systems. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK 2011), pp. 174–179. ACM, New York (2011)
Website (2016). https://www.moodlerooms.com/resource/x-ray-learning-analytics/
Verbert, K., Govaerts, S., Duval, E., Santos, J., Van Assche, F., Parra, G., Klerkx, J.: Learning dashboards: an overview and future research opportunities. Pers. Ubiquit. Comput. 18(6), 1499–1514 (2014)
Yu, Y.-C., You, S.-C.D., Tsai, D.-R.: Social interaction feedback system for the smart classroom. In: Proceedings of the 2012 IEEE International Conference on Consumer Electronics (ICCE), pp. 500–501. IEEE (2012)
Son, L.H.: Supporting reflection and classroom orchestration with tangible tabletops. Ph.D. thesis. http://biblion.epfl.ch/EPFL/theses/2012/5313/5313_abs.pdf
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Meluso, F., Avogadro, P., Calegari, S., Dominoni, M. (2018). Utilization Measures in a Learning Management System. In: Filipe, J., Bernardino, J., Quix, C. (eds) Data Management Technologies and Applications. DATA 2017. Communications in Computer and Information Science, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-319-94809-6_9
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