Abstract

Analytics on data is based on various data mining and statistical techniques that have changed over a number of decades (Papamitsiou & Economides, 2014). A growing interest for a better use of data from various sources and an increasing number of “actions” have been implemented by Universities to be more effective in Learning Analytics (LA). In fact, the result of these analyses offers information which can be used to sustain and/or to enable decision making processes. For this reason, scholars and practitioners have approached LA from a range of perspectives: it is necessary to identify not only the aims that can be achieved using LA but also what should be done to attain these aims. Generally, LA are considered as measurement, collection, analysis and reporting of students’ data and their contexts, to optimize learning and the environments in which it occurs (Khalil & Ebner, 2016). Starting from this definition, and using a design science approach, we propose an “extended” LA framework that puts the learner at the centre.

Citation

Caporarello, L., Cirulli, F. & Milani, M. (2019). Learning Analytics in Higher Education: a framework proposal. In S. Carliner (Ed.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 477-483). New Orleans, Louisiana, United States: Association for the Advancement of Computing in Education (AACE). Retrieved August 10, 2024 from https://www.learntechlib.org/primary/p/211116/.

© 2019 Association for the Advancement of Computing in Education (AACE)