Master Thesis Student
Thesis topic: Integration of an xAPI-based Learning Record Store in the Open Learning Analytics Platform
Related project: OpenLAP
Supervisor: Dr. Arham Muslim
Thesis duration: 07/2018 - 01/2019
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. The diversity in different dimensions of OLA introduces a set of challenges for the LA developers and researchers while designing solutions for OLA. The Open Learning Analytics Platform (OpenLAP) is a framework which 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. OpenLAP adopts a data model called the Learning Context Data Model (LCDM) to address the data aggregation and integration as well as the interoperability requirements.
The aim of this thesis is to increase the interoperability of OpenLAP with available solutions in the eLearning community by replacing LCDM with widely used data model called Experience API (xAPI). Therefore, an appropriate xAPI-based Learning Record Store (LRS) should be selected based on different dimensions and integrated into the OpenLAP. Moreover, a set of template data queries should be provided, which can support the current process of definition and generation of personalized indicators by end-users.