Open Learning Analytics Platform

Open Learning Analytics Platform (OpenLAP)

Goals

Open Learning Analytics (OLA) is an emerging field, which deals with learning data collected from various environments and contexts, analyzed with a wide range of analytics methods to address the requirements of different stakeholders. OLA introduces a set of requirements and implications for LA practitioners, developers, and researchers. These include data aggregation and integration, interoperability, reusability, modularity, flexibility, extensibility, performance, scalability, usability, privacy, and personalization. The Open Learning Analytics Platform (OpenLAP) is a framework which addresses these issues and lays the foundation for an ecosystem of OLA.

OpenLAP provides a detailed technical OLA architecture with a concrete implementation of all its components, seamlessly integrated in a platform. It encompasses different stakeholders associated through a common interest in learning analytics but with diverse needs and objectives, a wide range of data coming from various learning environments and contexts, as well as multiple infrastructures and methods that enable to draw value from data in order to gain insight into learning processes.

OpenLAP follows a user-centric approach to engage end users in flexible definition and dynamic generation of personalized indicators. The generated indicators are executed by querying data, applying filters, performing analysis, and generating visualizations to be rendered on the client side. To meet the requirements of diverse users, OpenLAP provides a modular and extensible architecture that allows the easy integration of new analytics modules, analytics methods, and visualization techniques.

 

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