The learning management system (LMS) has dominated Internet-based education for the past two decades. However, the traditional LMS is failing to keep pace with advances in Internet technologies and social interactions online. To support technological diversity, current frameworks such as the E-Learning Framework (ELF), the IMS Abstract Framework, and the Open Knowledge Initiative (OKI) have defined the initial steps toward service-oriented e-learning platforms. Nextgeneration platforms will be based on these service-oriented visions. Here, the authors discuss LMS evolution and present core challenges that must be addressed to achieve information interoperability in next-generation e-learning platforms. As Internet technologies proliferate into our daily lives, we come closer to realizing new and exciting online opportunities. One such opportunity is in e-learning, in which more dynamic platforms are emerging and replacing traditional, passive ones. Active e-learning employs a broad range of Internet technologies, such as personalization, simulation, and mobility, to achieve pedagogic scenarios otherwise inaccessible to traditional forms of learning.1 Thus, today's elearning platforms must deal with an increasing set of requirements.The demand for modularized and personalizable e-learning platforms is growing. 2 Traditional platforms can't support architectural flexibility due to their monolithic designs. E-learning vendors are addressing this demand by providing toolkits that support customization or by making their source code available for modification under various open source licenses. This indicates an emerging shift from generic solutions to specific applications. Future e-learning platforms will support a wider range of needs by providing interoperability architectures for various existing and emergent services. These needs include federated exchange among services (information and control), various levels of interoperability (intradomain and interdomain), and service composition (orchestration and choreography). Howev- er, these next-generation platforms also introduce wide-ranging issues from numerous research areas, including the Semantic Web, adaptive hypermedia, dynamic services, and federated modeling. Here, we explore e-learning platforms' evolution and illustrate some key challenges to information interoperability in next-generation platforms. E-Learning PlatformsTraditional e-learning platforms, or learning management systems (LMSs), provide holistic environments for delivering and managing educational experiences. They present suites of tools that support online course creation, maintenance, and delivery, student enrollment and management, education administration, and student performance reporting. We can group LMSs into two main categories: Open source LMSs are typically built on extensible frameworks that let implementers adjust and modify the systems to suit their specific needs. Although the proprietary sector hasn't widely adopted it, this approach is emerging through such initiatives as W...

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Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users' social media data.

A current problem with the research of adaptive systems is the inconsistency of evaluation applied to the adaptive systems. However, evaluating an adaptive system is a difficult task due to the complexity of such systems. Evaluators need to ensure correct evaluation methods and measurement metrics are used. This paper reviews a variety of evaluation techniques applied in adaptive and user-adaptive systems. More specifically, it focuses on the user-centred evaluation of adaptive systems such as personalised recommender systems and adaptive information retrieval systems. The review tackles the question of 'How have user-centred evaluations of adaptive and user-adaptive systems been conducted and how can these evaluation practices be improved?' Based on the analysed results of the: (a) evaluation approaches, (b) user-centred evaluation techniques, and (c) evaluation metrics, we propose an evaluation framework for end-user experience in evaluating adaptive systems (EFEx).

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