Skip to Main content Skip to Navigation
Conference papers

Towards a modular and flexible Learning Analytics framework

Abstract : This paper introduces a Learning Analytics platform which aims at being modular, evolving and flexible. The general framework architecture is completely independent from the digital systems to which it is connected. It collects learning data of various origins in data storages. Then it extracts a subset of the data which is aggregated into a data warehouse. Finally, these data are processed through various algorithms. Such a framework reinforces the control of data integrity in an experimental context and allows the students to refine the authorizations they give about their data. These data processing lead to indicators that will be used in student and teacher dashboards allowing a clear and fast access to learning information. In a second step, the platform will compute student profiles, facilitating the design of adaptive courses for each student.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03005330
Contributor : François Bouchet <>
Submitted on : Saturday, November 14, 2020 - 2:27:52 AM
Last modification on : Tuesday, March 23, 2021 - 9:28:02 AM
Long-term archiving on: : Monday, February 15, 2021 - 6:03:20 PM

File

LAK20-LAPAD.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Identifiers

  • HAL Id : hal-03005330, version 1

Citation

Yves Noel, François Bouchet, Roland Mergoil, Vanda Luengo. Towards a modular and flexible Learning Analytics framework. Learning Analytics and Knowledge 2020, SOLAR, Mar 2020, Frankfurt, Germany. pp.178-179. ⟨hal-03005330⟩

Share

Metrics

Record views

30

Files downloads

25