OpenLAP: a user-centered open learning analytics platform

During the last few years, Learning Analytics (LA) has gained the interest of researchers in the field of Technology Enhanced Learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. It shares a movement from data to analysis to action to learning. Recently, the demand for self-organized, networked, and lifelong learning opportunities has increased. Therefore, there is a need to provide an understanding of how different learners learn in these open learning settings and how learners, educators, institutions, and researchers can best support this process. Moreover, this openness should be reflected in the conceptualization and development of innovative LA approaches in order to achieve more effective learning experiences. Open Learning Analytics (OLA) is an emerging research field that has the potential to deal with these challenges in open learning environments. However, the concrete solutions and implementations that can deliver an effective and efficient OLA are still lacking. Most solutions currently available does not continuously involve end-users in the LA process and follow design patterns which make it difficult to adopt new user requirements. Furthermore, the available implementations are designed and developed for specific scenarios, which address the requirements of a specific set of stakeholders by relying on a predefined set of questions and indicators. These limitations restrict the scope of such solutions and implementations in the context of OLA targeting various stakeholders with different needs. The aim of this dissertation is to introduce personalization in the LA process by investigating the design of an effective user-centered Open Learning Analytics Platform (OpenLAP) and providing its conceptual, implementation, and evaluation details. OpenLAP provides a user-friendly interface that supports an interactive, exploratory, and real-time user experience to allow the end-users to dynamically define new indicators that meet their goals. Moreover, OpenLAP is designed to be modular and extensible allowing easy integration of new data sources, analytics methods, and visualization techniques at runtime to adopt the new requirements of multiple stakeholders and deliver an ecosystem for OLA. The main contributions of this dissertation include (1) a comprehensive analysis of the currently available LA tools and solutions with respect to their support for openness and personalization, (2) a theoretically sound design of a user-centered OpenLAP based on the requirements gathered from the empirical analysis of the literature, (3) a concrete implementation of OpenLAP providing an interface to self-define the indicators and an extensible mechanism to easily integrated new data sources, analytics methods, and visualization techniques, and (4) a thorough evaluation of OpenLAP in a pilot study at RWTH Aachen University to assess it in terms of usability, usefulness, extensibility, and modularity