Abstract
<p class="3">Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present. As manual metadata generation is time-costly and often eschewed by the authors of the social web resources, automatic generation is a fertile area for research as several kinds of metadata, such as author or topic, can be generated or extracted from the contents of a document. In this paper we propose a novel metadata generation system aimed at automatically tagging distance learning resources. This system is based on a recently-created intelligent pattern classifier; specifically, it trains on a corpus of example documents and then predicts the topic of a new document based on its text content. Metadata is generated in order to achieve a better integration of the web resources with the social networks. Experimental results for a two-class problem are promising and encourage research geared towards applying this method to multiple topics.</p>
Highlights
In recent years social networks have grown considerably
In the context of social networks, distance learning environments allow for unlimited access to materials that can be completed at a work rate that is comfortable for the individual and the ability for constant group feedback; behavior change is reinforced by social group support and distance learning strategies
To take on the problem of metadata generation, we propose the application of a novel pattern classifier to automatically predict the topic of social web content
Summary
By establishing relationships within educational environments, these networks become a much more effective medium of communication among learning. Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources García-Floriano, Ferreira-Santiago, Yáñez-Márquez, Camacho-Nieto, Aldape-Pérez, and Villuendas-Rey peers. Emerging social networks bear social capital as inherent value, manifested in the common interests that help foster near instant contact between affine members. According to Willis, Szabo-Reed, Ptomey, Steger, Honas, Al-Hihi, Lee, Vansaghi, Washburn, and Donnelly (2016), the dramatic growth in technology and online social networks has generated great interest in open and distance learning. In the context of social networks, distance learning environments allow for unlimited access to materials that can be completed at a work rate that is comfortable for the individual and the ability for constant group feedback; behavior change is reinforced by social group support and distance learning strategies
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