Supporting Indicator Personalization and Platform Extensibility in Open Learning Analytics

The demand for Open learning analytics (OLA) has grown in recent years due to the increasing interest in the usage of self-organized, networked, and lifelong learning environments. However, platforms that can deliver an effective and efficient OLA are still lacking. Most OLA platforms currently available do not continuously involve end-users in the indicator definition process and follow design patterns which make it difficult to extend the platform to meet new user requirements. These limitations restrict the scope of such platforms where users regulate their own learning process according to their needs. In this paper, we discuss the Open learning analytics platform (OpenLAP) as a step toward an ecosystem that addresses the indicator personalization and platform extensibility challenges of OLA. OpenLAP follows a user-centered learning analytics approach that involves end-users in the process of defining custom indicators that meet their needs. Moreover, it provides a modular and extensible architecture that allows the easy integration of new analytics methods and visualization techniques.