Evaluation of a Maturity Model for the Adoption of Learning Analytics in Higher Education Institutions
DOI:
https://doi.org/10.17398/1695-288X.19.2.101Keywords:
Higher Education, Learning Analytics, Politics of Education, Questionnaires, ModelsAbstract
Learning Analytics (LA) aims to analyze the data generated by both students and teachers in online environments in order to promote actions to improve teaching and learning processes. The results of such analyzes can help teachers to know their students’ study processes, as well as being able to assist with the verification and correction of both educational activities and practices. For students, LA can help with reflection and self-regulation of learning. However, despite its benefits, institutions have difficulties in adopting it. In this sense, an instrument that can support the use of LA is the Maturity Model (MM), which has been used in different knowledge areas in order to indicate an improvement roadmap for organizations. Hence, this paper aims to present the assessment results of a MM proposed for the adoption of LA in Higher Education Institutions, called MMALA. The evaluation focused on the model composition and was carried out through a questionnaire addressed to LA researchers and professionals. After conducting analyzes, both qualitative and quantitative, suggestions for improvement for the proposed model were identified, and the model was validated, supporting its further development.
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Arnold, K., & Pistilli, M. (2012). Course signals at Purdue: using learning analytics to increase student success. Proceedings of the International Conference on Learning Analytics and Knowledge - LAK '12, New York, NY, USA, 267-270. https://doi.org/10.1145/2567574.2567621
Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management – A Procedure Model and its Application. Business & Information Systems Engineering, 1(3), 213–222. https://doi.org/10.1007/s12599-009-0044-5
CMMI. (2010). CMMI para Desenvolvimento (v1.3). Software Eng. Institute, Carnegie Mellon.
Dawson, S., Joksimovic, S., Poquet, S., & Siemens, G. (2019). Increasing the Impact of Learning Analytics. Proceedings of the International Conference on Learning Analytics and Knowledge - LAK’19, Tempe, Arizona, USA, 446-455. https://doi.org/10.1145/3303772.3303784
DAMA International. (2009). The DAMA guide to the data management body of knowledge (DAMA-DMBOK), Tech. Publications.
DMM. (2014). Data management maturity model – 1.0 version. CMMI Institute.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4 (5/6), 304-317. https://doi.org/10.1504/IJTEL.2012.051816
Freitas, E. L. S. X., Souza, F. F., & Garcia, V. C. (2019). Learning Analytics em Ação: Uma Revisão Sistemática de Literatura. Anais do Simpósio Brasileiro de Informática na Educação - SBIE, Brasília. https://doi.org/10.5753/cbie.sbie.2019.1581
Freitas, E. L. S. X., Souza, F. F., Garcia, V. C., Mello, R. F., & Gasevic, D. (2020). Towards a Maturity Model for Learning Analytics Adoption: An Overview of its Levels and Areas. Proceedings of the International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia, 2020. https://doi.org/10.1109/ICALT49669.2020.00059
Gallego, F. O., & Corchuelo, R. (2020). An encoder–decoder approach to mine conditions for engineering textual data. Engineering Applications of Artificial Intelligence, 91, 103568. https://doi.org/10.1016/j.engappai.2020.103568
Gewerc, A., Rodríguez-Groba, A., & Martínez-Piñeiro, E. (2016). Academic Social Networks and Learning Analytics to Explore Self-Regulated Learning: a Case Study. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 11(3), 159-166. https://doi.org/10.1109/RITA.2016.2589483
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Análise multivariada de dados. Bookman Editora.
Halper, F., & Stodder, D. (2014). TDWI analytics maturity model guide. TDWI Research. Recuperado a partir de https://tdwi.org/pages/maturity-model/analytics-maturity-model-assessment-tool.
Hiles, A. (2010). The Definitive Handbook of Business Continuity Management, 3ª Ed., Wiley.
Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 Horizon Report. Austin, Texas, The New Media Consortium. Recuperado a partir de https://library.educause.edu/-/media/files/library/2011/2/hr2011-pdf.pdf.
Keystone Strategy. (2016). Data & analytics maturity model & business impact. White Paper. Recuperado a partir de https://info.microsoft.com/rs/157-GQE-382/images/EN-CNTNT-SQL-Data%20Analytics%20Maturity%20Model-en-us.pdf
Kitto, K., Cross, S., Waters, Z., & Lupton, M. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. Proceedings of the International Conference on Learning Analytics And Knowledge - LAK '15. New York, NY, USA, 11-15. https://doi.org/10.1145/2723576.2723627.
Li, M., & Smidts, C. (2003). A ranking of software engineering measures based on expert opinion. IEEE Transactions on Software Engineering, 29(9), 811–824. https://doi.org/10.1109/TSE.2003.1232286
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist, 57(10), 1439-1459. https://doi.org/10.1177/0002764213479367.
Pedhazur, E. J., & Schmelkin, L. P. (2013). Measurement, design, and analysis: An integrated approach. Psychology Press.
Rau, M. A., Aleven, V., & Rummel, N. (2014). Sequencing Sense-Making and Fluency-Building Support for Connection Making between Multiple Graphical Representations. En J. Polman, E. Kyza, D. K. O'Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O'Connor, T. Lee, L. D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS), (2, 977-981).
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior, 47, 157-167. https://doi.org/10.1016/j.chb.2014.05.038.
Tlili, A., Essalmi, F., Jemni, M., & Kinshuk. (2015). An educational game for teaching computer architecture: Evaluation using learning analytics. Proceedings of the International Conference on Information & Communication Technology and Accessibility (ICTA), Marrakech, 1-6. https://doi.org/10.1109/ICTA.2015.7426881.
Tsai, Y. S., & Gašević, D. (2017). The State of Learning Analytics in Europe – Executive Summary – SHEILA. Recuperado a partir de http://sheilaproject.eu/2017/04/18/the-state-of-learning-analytics-in-europe-executive-summary.
Tsai, Y., Moreno-Marcos, P., Jivet, I., Scheffel, M, Tammets, K., Kollom, K., & Gasevic, D. (2018). The SHEILA framework: informing institutional strategies and policy processes of learning analytics. Journal of Learning Analytics, 5(3), 5-20. https://doi.org/10.18608/jla.2018.53.2
União Europeia. (2014). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Towards a thriving data-driven economy, SWD(2014) 214 final. Brussels. Recuperado a partir de https://ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=6216.
Yassine, S., Kadry, S., & Sicilia, M. A. (2016). A framework for learning analytics in moodle for assessing course outcomes. Proceedings of the IEEE Global Engineering Education Conference (EDUCON), Abu Dhabi, 261-266. https://doi.org/10.1109/EDUCON.2016.7474563.
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