Concluding Remarks and Probing Further

Concluding Remarks and Probing Further

Copyright: © 2015 |Pages: 17
DOI: 10.4018/978-1-4666-8705-9.ch016
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Arriving to the final destination of the journey started in chapter 1, this concluding chapter represents a brief reflection of the key considerations/contributions of the book and, simultaneously, provides a guidance for future research directions. From an ecological standpoint, the key purpose of this book was to systemically understand the essential issues related to the trends and fuzzy logic-based modeling perspectives of collaborative and blended learning. In addition, the emancipation of collaborative and blended learning environments here is established as a potential contribution to the 21st century learning contexts. In this vein, comprehension of the potentialities of the proposed fuzzy logic-based modeling approaches and the way they could be transferred to tackle real problems in the educational context, contributes to the establishment of a learning ecology for reflection and rethinking upon the intelligence of the online learning environments as current and future constructs.
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At A Glimpse

Based on updated literature review, theoretical issues and current trends, related with the concepts of: Online Learning Environments (OLEs), Computer-Supported Collaborative Learning, Collaborative learning, Blended (b)-learning, Learning Management Systems (LMSs), Cloud Learning Environments, Semantic Web 3.0 and Ontologies, were discussed in the first part (Part I) of the present book (chapters 1-5). Then, in Part II, fuzzy-logic essentials and inference systems were followed, presenting useful background for entering the mathematical expression of the knowledge representation in the fuzzy world (chapters 6-8). Next, the following chapters (chapters 9-14), exhibited how the adoption of the fuzzy logic essentials can provide flexibility, to capture and model human subjectivity in collaborative/metacognitive and LMS-based users’ interaction attitudes, extending the capabilities of tracking the “fine-grained” processes of effective OLE-based collaboration and interaction within b-learning context, revealing clear robustness against highly dispersed input data. Finally, in Part IV (chapters 15 and 16), the concept of hybrid modeling was envisioned, based on a fusion of the modeling approach presented in Part III, along with the potentialities of the OLEs presented in Part I. Apparently, human interaction modeling and learning are evolutionary and, almost, perpetualness processes; hence, the trajectory inscribed with this book could not be concluded without a glimpse to the future. Nevertheless, prior probing to the future, a recap, related with the functionality and contribution of each model discussed in Part III (chapters 10-14), is summarized in the succeeding subsections.

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