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Visualizing the Evolution of Mobile Learning Research

Visualizing the Evolution of Mobile Learning Research

Hüseyin Özçınar
Copyright: © 2019 |Pages: 21
ISBN13: 9781522593515|ISBN10: 1522593519|ISBN13 Softcover: 9781522593522|EISBN13: 9781522593539
DOI: 10.4018/978-1-5225-9351-5.ch006
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MLA

Özçınar, Hüseyin. "Visualizing the Evolution of Mobile Learning Research." Advancing Mobile Learning in Contemporary Educational Spaces, edited by Dominic Mentor, IGI Global, 2019, pp. 160-180. https://doi.org/10.4018/978-1-5225-9351-5.ch006

APA

Özçınar, H. (2019). Visualizing the Evolution of Mobile Learning Research. In D. Mentor (Ed.), Advancing Mobile Learning in Contemporary Educational Spaces (pp. 160-180). IGI Global. https://doi.org/10.4018/978-1-5225-9351-5.ch006

Chicago

Özçınar, Hüseyin. "Visualizing the Evolution of Mobile Learning Research." In Advancing Mobile Learning in Contemporary Educational Spaces, edited by Dominic Mentor, 160-180. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-9351-5.ch006

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Abstract

The aim of this chapter is to investigate the scope and change of m-learning literature over a period of thirteen years. The chapter takes an author co-citation approach where patterns are revealed in mlearning publications between the years 2002 and 2015. The author co-citation analysis is a kind of bibliometrics, and Power Flow Network Library (PFNET) analysis method to obtain the intellectual structure of the m-learning field. A PFNET is a psychometric scaling method where a network of associative nodes is generated using specific values. The method is based on graph theory and has been used to explore knowledge connections. The method is referred to as a pathfinder network for representing segments of knowledge in a branched manner. The method can also be used for developing consensus building or recognizing expertise in a specific area. In this study, it was found that the m-learning research focus on design and effectiveness of m-learning environments, developing a pedagogic framework for m-learning, adoption and proliferation of m-learning-assisted language learning, collaborative m-learning, and augmented reality. Assessing the results of factor analysis together with the PFNET graphics, the subfields show the fundamental orientations of the field, and the main themes will be the focus of future research.

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