Supporting Interactive Indicators in OpenLAP

 

Overview

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 which 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.

 

Tasks

• Enhance the visualization customization capabilities of the indicator editor in OpenLAP
• Investigate and develop a mechanism to enable user-control and interactivity with the indicators on the client side

 

Requirements

• Good programming skills in Web Technologies
• Prior knowledge in visual analytics is helpful
• Interest in data science and learning technologies

 

Contact

Dr. Arham Muslim

 

References

• Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In Big Data and Learning Analytics in Higher Education (pp. 195-219). Springer International Publishing.
• Muslim, A., Chatti, M. A., Bashir, M. B., Barrios Varela, O. E., and Schroeder, U. (2018). A Modular and Extensible Framework for Open Learning Analytics. Journal of Learning Analytics, 5(1):92–100.
• Muslim, A., Chatti, M. A., Mughal, M., and Schroeder, U. (2017). The Goal - Question - Indicator approach for personalized learning analytics. In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU (pp. 371-378). ScitePress.
• Muslim, A., Chatti, M. A., Mahapatra, T., and Schroeder, U. (2016). A rule-based indicator definition tool for personalized learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pages 264–273, New York, NY, USA. ACM.