Master Thesis Student
: Supporting Interactive Indicators in OpenLAP
: 01/2020 - 6/2020
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 (Chatti et al., 2017). The Open Learning Analytics Platform (OpenLAP) is a framework that addresses these issues and lays the foundation for an ecosystem of OLA. It 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 visualization 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 (Muslim et al., 2018).