Knowledge flow analysis: the quantitative method for knowledge stickiness analysis in online course

Iveta Daugule, Atis Kapenieks

Abstract


The aim of this study is to better understand the feasibility of using technological solutions to detect specific knowledge descriptors in online learning systems. Understanding the relevance of specific knowledge in the context of online courses is important for the architecture of the content of the course and for improving the effectiveness of the learning process. By identifying the specific nature of the knowledge flow in a timely manner, it is possible to better adapt the course content to the needs of the student and to ensure that the time spent on learning is used effectively. In the case of this tailored course content, it is expected that in a given course, it would be possible to learn more content than a time-like course where knowledge stickiness has not been taken into account. By using calculations and excluding the possibility of a subjective view as far as possible, authors are clarifying the nature of knowledge for each of the competencies. Automated evaluation of knowledge properties would significantly facilitate the learning process by allowing better management of the knowledge flow. Improving the effectiveness of preparing training materials would be a significant benefit from the development of such a solution.

Keywords


E-learning; Knowledge flow analysis; Knowledge properties; Knowledge stickiness; Online courses; Student motivation

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References


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DOI: http://dx.doi.org/10.21533/pen.v7i1.358

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Copyright (c) 2019 Iveta Daugule, Atis Kapenieks

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License