Master Thesis Student

Email: abdul.khan@stud.uni-due.de 

Thesis topic: Supporting Interactive Indicators in OpenLAP

Related project: OpenLAP

Supervisor: Dr. Arham Muslim

Thesis duration: 01/2020 - 6/2020

Description

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).

The current implementation of the indicator generation process in OpenLAP applies visual analytics concepts to support end-users in self-defining indicators that meet their needs (Muslim et al., 2017, 2016). However, the final visualization of the indicator on the client-side is static. The aim of this thesis is to investigate how the currently generated static indicators in the form of HTML and JavaScript can be evolved into more interactive indicators, which can be embedded in the client applications to support more exploratory visualizations.