Open Learning Analytics (OLA)


The aim of Open Learning Analytics (OLA) is to improve learning efficiency and effectiveness in lifelong learning environments. In order to understand learning and improve the learning experience and teaching practice in today's networked and increasingly complex learning environments, there is a need to scale Learning Analytics (LA) up which requires a shift from closed LA tools and systems to LA ecosystems and platforms where everyone can contribute and benefit. OLA refers to an ongoing analytics process that encompasses openness at all four dimensions of the LA reference model (Chatti et al. 2016).

  • What?: It accommodates the considerable variety in learning data and contexts. This includes data coming from traditional education settings (e.g. LMS) and from more open-ended and less formal learning settings (e.g. PLEs, MOOCs).
  • Who?: It serves different stakeholders with very diverse interests and needs.
  • Why?: It meets different objectives according to the particular point of view of the different stakeholders.
  • How?: It leverages a plethora of statistical, visual, and computational tools, methods, and methodologies to manage large datasets and process them into metrics which can be used to understand and optimize learning and the environments in which it occurs.

Learning analytics reference model (Chatti et al., 2014)

OpenLAP Overview


OpenLAP Abstract Architecture (Chatti et al. 2016)

The Open Learning Analytics Platform (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 LA 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 (Chatti et al. 2016).

OpenLAP follows a user-centric approach to engage end users in flexible definition and dynamic generation of indicators. 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.


Features

User-Centric

OpenLAP lays the foundation of simple yet powerful tool that engages end users in a continuous inquiry-based LA process, by supporting them in interacting with the responsive and user friendly interface to set goals, pose questions, and self-define the indicators that help them achieve their diverse objectives.

Modular and Extensible

OpenLAP provides an easy and flexible mechanism for LA researchers and developers to smoothly plug-in new analytics modules, analytics methods, and visualization techniques at runtime to meet the requirements of different stakeholders such as teachers, students, institutions, researchers, and administrators.

Open Source

OpenLAP is an open source research project for open learning analytics. A comprehensive documentation and the source code is available on the project GitHub repository.