This article describes Learning Analytics (LA) as a predictive and formative approach that enables the planning of educational scenarios in line with students’ needs and languages in order to set a priori and in progress systems of control and inspection of the following: consistency, relevance and effectiveness of training objectives, curriculum paths, students’ needs and learning outcomes. Thanks to LA, it is possible to understand how students learn. Training courses are designed to include the definition of those learning outcomes that respond effectively to students’ needs in terms of contents, methodologies, tools and teaching resources. The present article aims to describe and discuss, after reviewing the relevant literature, in what way LA represents a valid support not only in designing student-centred training courses, which assess outcomes, but also in carrying out a formative assessment considering the learning experience as a whole. The analysis of some case studies was a good opportunity to reflect and define the bridge existing between the use of LA for assessment purposes and personalized learning paths.
If the inline PDF is not rendering correctly, you can download the PDF file here.
Agnihotri, L., & Ott, A. (2014, July). Building a Student At-Risk Model: An End-to-End Perspective From User to Data Scientist. In Proceedings of the 7th International Conference on Educational Data Mining (pp. 209-212).
Aljohani, N. R., & Davis, H. C. (2013). Learning analytics and formative assessment to provide immediate detailed feedback using a student centered mobile dashboard.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). ACM.
Arnold, K. E., Tanes, Z., & King, A. S. (2010). Administrative perceptions of data-mining software Signals: Promoting student success and retention. The Journal of Academic Administration in Higher Education, 6(2), 29-39. Assessment, King’s College, London.
Baker, B. (2007). A conceptual framework for making knowledge actionable through capital formation. (Doctoral dissertation). University of Maryland University College, Maryland. Retrieved from ABI/INFORM Global. (Publication No. AAT 3254328).
Black P., Wiliam D. (1998), Inside the black box: Raising standards through classroom assessment. NferNelson: London.
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic Analytics: A New Tool for a New Era. Educause Review, 42(4), 40-57.
Cauley, K. M., & McMillan, J. H. (2010). Formative assessment techniques to support student motivation and achievement. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83(1), 1-6.
Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of educational psychology, 88(4), 715.
de Waal, P. (2017). Learning Analytics: i sistemi dinamici di supporto alla decisione per il miglioramento continuo dei processi di insegnamento e apprendimento. Formazione & Insegnamento. Rivista internazionale di Scienze dell’educazione e della formazione, 15(2), 43-52.
Ferguson, R. (2014). Learning Analytics: fattori trainanti, sviluppi e sfide. TD tecnologie didattiche, 22(3), 138-147.
Goos, M., Galbraith, P., & Renshaw, P. (2002). Socially mediated metacognition: Creating collaborative zones of proximal development in small group problem solving. Educational studies in Mathematics, 49(2), 193-223.
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12). Springer Science & Business Media.
Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1-2), 221-240.
Limone, P. (2012a). Ambienti di apprendimento e progettazione didattica. Proposte per un sistema educativo transmediale (pp. 1-176). Roma: Carocci.
Limone, P. (2012b). Valutare l’apprendimento on-line. Esperienze di formazione continua dopo la laurea (pp. 1-102). Bari: Progedit.
Long, P. D., & Siemens, G. (2014). Penetrare la nebbia: tecniche di analisi per l’apprendimento. TD Tecnologie Didattiche, 22(3), 132-137.
Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97.
Loperfido, F. F., Dipace, A., & Scarinci, A. (2018. Qualitative learning analytics to detect students’ emotional topography on EduOpen. Research on Education and Media, 10 (1), 49-60.
Lotti, A. (2018). Problem-Based Learning: Apprendere per problemi a scuola: guida al PBL per l’insegnante. Milano: Franco Angeli.
Kingston, N., & Nash, B. (2011). Formative assessment: A meta analysis and a call for research. Educational measurement: Issues and practice, 30(4), 28-37.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big Data: The Next Frontier for Innovation, Competition and Productivity. Washington: McKinsey Global Institute.
Papamitsiou, Z., & Economides, A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17(4), 49–4. Perspectives on Curriculum Evaluation, Rand McNally, Chicago.
Park, Y., & Jo, I. H. (2015). Development of the Learning Analytics Dashboard to Support Students’ Learning Performance. J. UCS, 21(1), 110-133.
Quagini, M. (2015). SMARTechnology. Crm & Digital Innovation per creare valore in azienda: Crm & Digital Innovation per creare valore in azienda. Franco Angeli.
Sclater, N. (2017). Learning analytics explained. New York: Routledge.
Scriven M. (1967), The methodology of evaluation. Lafayette, Ind: Purdue University.
Shute, V. J. (2008). Focus on formative feedback. Review of educational research, 78(1), 153-189.
Siemens, G., & d Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause review, 46(5), 30.