Arham Muslim

A rule-based indicator definition tool for personalized learning analytics

In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). Generally, LA deals with the development of methods that harness educational data sets to support the learning process. Recently, the concept of open learning analytics (OLA) has received a great deal of attention from LA community, due to the growing demand for self-organized, networked, and lifelong learning opportunities. A key challenge in OLA is to follow a personalized and goal-oriented LA model that tailors the LA task to the needs and goals of multiple stakeholders. Current implementations of LA rely on a predefined set of questions and indicators. There is, however, a need to adopt a personalized LA approach that engages end users in the indicator definition process by supporting them in setting goals, posing questions, and self-defining the indicators that help them achieve their goals. In this paper, we address the challenge of personalized LA and present the conceptual, design, and implementation details of a rule-based indicator definition tool to support flexible definition and dynamic generation of indicators to meet the needs of different stakeholders with diverse goals and questions in the LA exercise.

CONFab: component based optimization of WSN protocol stacks using deployment feedback

Wireless sensor networks are characterized by a large number of non-standardized protocols and varying application requirements. This creates need for a systematic approach to rapidly design and optimize deployment specific protocol stacks. We employ component based optimization as a candidate solution, and use it as a basis for an extensible software framework called CONFab. We treat a particular protocol stack as a collection of interdependent configurable components. CONFab captures a deployment scenario description, relates it to the desired performance metrics, and correspondingly suggests suitable protocol stacks and parameter settings. It utilizes ontology centric knowledge base to select components from a pool of alternatives, reason on their compatibility and, thus create appropriate protocol stacks. The framework is equipped with a number of additional plugins that allow, for instance, to incorporate feedback from deployed systems and user inputs to anticipate network performance. We use a set of well-known MAC and routing protocols to validate the framework on the Indriya testbed in different user specified application and deployment conditions. The results indicate that CONFab with its component based approach helps in obtaining suitable protocol stacks and thereby achieving high performance characteristics.

Toward an Open Learning Analytics Ecosystem

In the last few years, there has been a growing interest in learning analytics (LA) in technology-enhanced learning (TEL). LA approaches share a movement from data to analysis to action to learning. The TEL landscape is changing. Learning is increasingly happening in open and networked learning environments, characterized by increasing complexity and fast-paced change. This should be reflected in the conceptualization and development of innovative LA approaches in order to achieve more effective learning experiences. There is a need to provide understanding into how learners learn in these environments and how learners, educators, institutions, and researchers can best support this process. In this chapter, we discuss open learning analytics as an emerging research field that has the potential to deal with the challenges in open and networked environments and present key conceptual and technical ideas toward an open learning analytics ecosystem.

The Goal - Question - Indicator Approach for Personalized Learning Analytics

Open learning analytics (OLA) is a relatively new branch of learning analytics (LA) which emerged due to the growing demand for self-organized, networked, and lifelong learning opportunities. OLA deals with learning data collected from various learning environments and contexts, analyzed with a range of analytics methods, and for different stakeholders with diverse interests and objectives. This diversity in different dimensions of OLA is a challenge which needs to be addressed by adopting a personalized learning analytics (PLA) model. Current implementations of LA mainly rely on a predefined set of questions and indicators which is not suitable in the context of OLA where the indicators are unpredictable. In this paper we present the goal question indicator (GQI) approach for PLA and provide the conceptual, design, implementation and evaluation details of the indicator engine component of the open learning analytics platform (OpenLAP) that engages end users in the indicator generation process by supporting them in setting goals, posing questions, and self-defining

CONFab: Ontology and component based optimization of WSN protocol stacks with deployment feedback

Abstract Extreme diversity of application requirements and a large number of different available protocols are key characteristics of Wireless Sensor Networks (WSNs). There is a need for a systematic approach to rapidly compose and optimize application specific protocol stacks in an automated fashion. In this article we present the design, implementation and performance evaluation of CONFab, a framework for automatic protocol stack composition founded on the component based optimization approach. We treat a protocol stack as a collection of interdependent configurable components and have a goal to find the most suitable composition of components, as well as optimal parameters selection of individual components in an optimal fashion. CONFab captures a deployment scenario description, relates it to the desired performance metrics, and suggests suitable protocol stacks and parameter settings. It utilizes an ontology centric knowledge base to select components from a pool of alternatives and reason on their compatibility, thus creating appropriate protocol stacks. The framework is equipped with a number of additional plugins that allow, for instance, incorporating feedback from deployed systems and user inputs to anticipate network performance. The plugin mechanism also enables incorporating further advanced optimization routines, such as genetic algorithms, which can be used for optimization of component parameters and efficient exploration of the corresponding state space. We use a set of well-known medium access control and routing protocols to validate the framework on the Indriya testbed in different user specified application and deployment conditions. Our experimental results show that CONFab framework with its component based design is a powerful enabler in obtaining protocol stacks that suit application requirements and thereby achieving high performance characteristics for the network.