Connectivism has been proposed to explain the impact of new technologies on learning. According to this approach, learning may occur even outside the individual within an organization or a system. Learning objectives are not defined in advance and learning requires the ability to form connections and use networks to find the required knowledge. The connections by which individuals can learn are more important than what they currently know. The purpose of this paper is to investigate if a measure, rating the importance of concepts, can be derived from a network representation of the learning domain and if highly connected concepts – with high importance value – can describe whether information is explored in such ways as assumed by connectivism.
The authors empirically examined if the proposed measure can provide insight on the role of connections in learning and explain the reasons behind passing certain parts of a test using a linear regression model.
The results are twofold. First, an implementation of the information exploration principle of connectivism has been introduced, applying semantic technologies and the importance measure. Second, although no significant effects could be isolated, trends in performance improvement concerning highly important concepts were identified.
However, connectivism has been known since 2005, it is still lacking for successful implementations. The presented approach of a concept importance measure is a promising starting point by providing means of connected learning, enabling individuals to effectively improve their personal abilities to better fit job demand.
Vas, R., Weber, C. and Gkoumas, D. (2018), "Implementing connectivism by semantic technologies for self-directed learning", International Journal of Manpower, Vol. 39 No. 8, pp. 1032-1046. https://doi.org/10.1108/IJM-10-2018-0330Download as .RIS
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