Mohamed Amine Chatti

The future of e-learning: a shift to knowledge networking and social software

The main aim of Knowledge Management (KM) is to connect people to quality knowledge as well as people to people in order to peak performance. This is also the primary goal of Learning Management (LM). In fact, in the world of e-learning, it is more widely recognised that how learning content is used and distributed by learners might be more important than how it is designed. In the last few years, there has been an increasing focus on social software applications and services as a result of the rapid development of Web 2.0 concepts. In this paper, we argue that LM and KM can be viewed as two sides of the same coin, and explore how Web 2.0 technologies can leverage knowledge sharing and learning and enhance individual performance whereas previous models of LM and KM have failed, and present a social software driven approach to LM and KM.

A reference model for learning analytics

Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning TEL. Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics LA is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments what?, stakeholders who?, objectives why? and methods how?. We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.

Toward a Personal Learning Environment Framework

Over the past decade, it has been argued that technology-enhanced learning TEL could respond to the needs of the new knowledge society and transform learning. However, despite isolated achievements, TEL has not succeeded in revolutionizing education and learning processes. Most current TEL initiatives still take a centralized technology-push approach in which learning content is pushed to a predefined group of learners in closed environments. A fundamental shift toward a more open and learner-pull model for learning is needed. Recently, the Personal Learning Environment PLE concept has emerged to open new doors for more effective learning and overcome many of the limitations of traditional TEL models. In this paper, the authors present theoretical, design, implementation, and evaluation details of PLEF, a framework for mashup personal learning environments. The primary aim of PLEF is to help learners create custom learning mashups using a wide variety of digital media and data.

The Web 2.0 Driven SECI Model Based Learning Process

Nonaka and his knowledge transformation model SECI revolutionized the thinking about organizations as social learning systems. He introduced technical concepts like hypertext into organizational theory. Now, after 15 years Web 2.0 concepts seem to be an ideal fit with Nonakas SECI approach opening new doors for more personal, dynamic, and social learning on a global scale. In this paper, we present an extended view of blended learning which includes the combination of formal and informal learning, knowledge management, and Web 2.0 concepts into one integrated solution, by discussing what we call the Web 2.0 driven SECI model based learning process.

What Drives a Successful MOOC? An Empirical Examination of Criteria to Assure Design Quality of MOOCs

Massive Open Online Courses (MOOCs) have gained a lot of attention in the last years as a new technology enhanced learning (TEL) approach in higher education. MOOCs provide more educational opportunities to a massive number of learners to attend free online courses around the globe. Discussions around MOOCs have been focusing on the potential, social, institutional, technological, relevance, and marketing issues and less on the quality design of MOOC environments. Several studies have reported a high drop-out rate in average of 95% of course participants and other pedagogical problems concerning assessment and feedback. Thus, the quality of MOOCs design is worth additional investigation. Although several studies identified a large set of criteria to the successful design of TEL systems in general, not all of them can be used in the MOOC context, due to some unique features of MOOCs. This study is a first step towards identifying specific criteria that need to be considered when designing and implementing MOOCs. The results of this empirical study are based on a large survey targeting learners as well as professors, both with MOOC experience. As a result, we identified and rated 74 indicators classified into our two main dimensions of pedagogical and technological criteria distributed over six categories. From these, the learning analytics and assessment categories were found to be the key features for effective MOOCs.

GeRoMe : a generic role based metamodel for model management

The goal of Model Management is the development of new technologies and mechanisms to support the integration, evolution and matching of models. Such tasks are to be performed by means of a set of model management operators which work on models and their elements, without being restricted to a particular metamodel (e.g. the relational or UML metamodel). We propose that generic model management should employ a generic metamodel (GMM) which serves as an abstraction of the features of particular metamodels while preserving the semantics of its different elements. A naive generalization of the elements of concrete metamodels in generic metaclasses would loose some of the specific features of the metamodels, or yield a prohibitive number of metaclasses in the GMM. To avoid these problems, we propose the Generic Role Based MetamodelGeRoMe in which each model element is decorated with a set of role objects that represent specific properties of the model element. Roles may be added to or removed from elements at any time, which enables a very flexible and dynamic yet accurate definition of models. Roles constitute to operators different views on the same model element. Thus, operators concentrate on features which affect their functionality but may remain agnostic about other features. Consequently, these operators can use polymorphism and have to be implemented only once using GeRoMe, and not for each specific metamodel. We verified our results by implementing GeRoMe and a selection of model management operators using our metadata system ConceptBase.

Knowledge management: a personal knowledge network perspective

Purpose – This article aims to introduce the personal knowledge network (PKN) model as an alternative model to knowledge management (KM) and to discuss whether personal knowledge management (PKM) is better adapted to the demands of the new knowledge environments. The PKN model views knowledge as a personal network and represents a knowledge ecological approach to KM.Design/methodology/approach – KM and PKM have attracted attention over the past two decades and are considered as important means to increase organizational and individual performance. In this article, the author reviews previous models of KM and PKM and explores their failure to address the problem of knowledge worker performance and to cope with the constant change and critical challenges of the new knowledge era. The author further highlights the crucial need for new KM models that have the potential to overcome the shortcomings of previous models. In light of these shortcomings, the article introduces and discusses the PKN model as an alte...

Mobile Web Services for Collaborative Learning

Since learning nowadays is conceptualized as a social system within communities of practice, the best way to learn is with others, in groups. In the past few years, there has been an increasing focus on social software applications as a result of the rapid development of new web technologies. Furthermore, mobile and ubiquitous technologies have provided capabilities for more sophisticated open social systems, where mobile knowledge sharing is the norm. In this paper, we explore the use of these concepts for learning and present a smart phone driven mobile Web Services architecture for collaborative learning.

LaaN: Convergence of Knowledge Management and Technology-Enhanced Learning

Knowledge Management (KM) and Technology-Enhanced Learning (TEL) have attracted attention over the past two decades and are meanwhile considered as important means to increase individual and organizational performance. There is, however, a wide agreement that traditional KM and TEL models have failed to cope with the fast-paced change and critical challenges of the new knowledge era. In this paper, we propose a vision for future KM/TEL approaches which aims to fulfill the needs of the new knowledge landscape by introducing the Learning as a Network (LaaN) theory as a new learning theory characterized by the convergence of KM and TEL within a learner-centric knowledge environment. We further discuss a possible implementation of the LaaN theory based on the personal learning environment (PLE) concept.

Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments

The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning. One concern with a PLE-driven knowledge-pull approach to learning, however, is information overload. Recommender systems can provide an effective mechanism to deal with the information overload problem in PLEs. In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings. We implement 16 different tag-based collaborative filtering recommendation algorithms, memory based as well as model based, and compare them in terms of accuracy and user satisfaction. The results of the conducted offline and user evaluations reveal that the quality of user experience does not correlate with high-recommendation accuracy.