This chapter introduces the reader to Part IV of the book, proposing and discussing a hybrid approach that may serve, not only to synthesize and represent knowledge obtained from the data, but also to explore possible future online learning environment (OLE) states, given different management, policy or environmental scenarios. Pragmatically, this chapter explores the potentiality of the quality of collaboration (QoC) within an Internet-based computer-supported collaborative learning environment and quality of interaction (QoI) with a LMS, both involving fuzzy logic-based modeling, as vehicles to improve the personalization and intelligence of an OLE. Furthermore, QoC and QoI can form the basis for a more pragmatic approach of OLEs under the perspective of semantic Web 3.0, within the context of Higher Education. Finally, a potential case study of the examined hybrid modeling, referring to the “i-TREASURES” European FP7 Programme, is discussed, to explore its applicability and functionality under pragmatic learning scenarios.
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Apparently, the future of the Web will become more humanized and intelligent with the Web 3.0 (Liu, 2014; Paquette, 2014) by tagging online information and creating links between interconnected pieces that will be entirely understood by both humans and computers. However, the definitions of Web 3.0, also known as the Semantic Web, vary considerably. Moreover, the term “Semantic Web” is used inconsistently by academic researchers, holding a landscape of different fields, technologies, concepts and applications. From one point of view, Semantic Web technology could play an important role in the particular context of Learning Management Systems (LMSs), giving the possibility to organize information for easy retrieval, reuse, and exchange between different learning systems/tools. From another, synchronized with the concept of intelligent LMS (iLMS), blended (b)-learning scenarios can offer a number of learning tools, in a wide range of interaction, collaboration and sophistication (Dias et al., 2014; Dias, 2014). Lukasiewicz and Straccia (2008), more pragmatically, have examined five of the most important challenges facing Semantic Web, namely: vastness, vagueness, uncertainty, inconsistency, and deceit. However, nowadays, the central challenge would be to provide adapted and personalized solutions/alternatives, where intelligent models could contribute, involving artificial intelligence (AI) and incertitude modeling, e.g., via Fuzzy Logic (FL). As it was made clear from Parts II and III, FL is an efficient field that is suitable for dealing with vagueness. In addition, it is considered a form of continuous multi-valued logic allowing “computing with words” and modeling complex systems, characterized by imprecise and vague behaviors by means of a linguistic approach (Zadeh, 1965, 1968, 1971). In general, the whole point of Web 3.0 is to provide accessible information to people and computers at anytime from anywhere. Furthermore, with new technological innovations for applying intelligent agents (Web 4.0), cloud computing services has been coined as an umbrella term to describe a category of sophisticated on-demand computing services, initially offered by commercial providers (such as Amazon, Google, and Microsoft) (Voorsluys et al., 2011). By embedding the cloud computing within iLMS, access to large amount of data and different computational learning resources/environments becomes feasible.
Based on the aforementioned perspectives, this chapter examines the potentiality of the quality of collaboration (QoC) within an Internet-based computer-supported collaborative learning (CSCL) environment and quality of interaction (QoI) with a LMS, both involving FL-based modeling as a vehicle to improve the personalization and intelligence of an online learning environment (OLE). Furthermore, the combined measures presented here (i.e., QoC and QoI) can form the basis for a more pragmatic approach of OLEs via Web analytics and Web controlling/monitoring, within the concept of semantic Web and the associated Web 3.0 features. A detailed description of this idea is explored and described in the succeeding section, towards a hybrid modeling approach.