A Modular and Extensible Framework for Open Learning Analytics

Arham Muslim
Mohamed Amine Chatti
Muhammad Bassim Bashir
Oscal Eduardo Barrios Varela
Ulrik Schroeder


Open Learning Analytics (OLA) is an emerging concept in the field of Learning Analytics (LA). It deals with learning data collected from multiple environments and contexts, analyzed with a wide range of analytics methods to address the requirements of different stakeholders. Due to this diversity in different dimensions of OLA, the LA developers and researchers face numerous challenges while designing solutions for OLA. The Open Learning Analytics Platform (OpenLAP) is a framework that addresses these issues and lays the foundation for an ecosystem of OLA that aims at supporting learning and teaching in fragmented, diverse, and networked learning environments. It follows a user-centric approach to engage end users in flexible definition and dynamic generation of personalized indicators. In this paper, we address a subset of OLA challenges and present the conceptual and implementation details of the analytics framework component of OpenLAP, which follows a flexible architecture that allows the easy integration of new analytics methods and visualization techniques in OpenLAP to support end users in defining indicators based on their needs in order to embed the results into their personal learning environment.


Personalised learning analytics; OpenLAP; analytics framework; modularity; extensibility.

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DOI: http://dx.doi.org/10.18608/jla.2018.51.7


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