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Did library learners benefit from m-learning strategies? Research-based evidence from a co-citation network analysis of the literature

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Abstract

Mobile learning strategies have been employed for social learning activities, including library- and museum-supported learning. Previous studies have reviewed the literature from the technological aspect. However, a retrospective study from the perspective of bibliometric and network structure has not yet been provided. The aim of this study was therefore to systematically review journal papers on library-supported mobile learning (LibML). A coding framework including library types, mobile learning strategies, and research issues was adopted based on the literature and was used to screen and categorize the research papers. A co-citation network analysis was then adopted to analyze and visualize the structural relationships among the papers. A total of 53 eligible articles with 1370 citations in follow-up studies were collected from the Scopus database. The results showed that two main research streams of LibML were identified from the overall network structure, including library- and museum-supported mobile learning. In terms of the mobile learning strategy, library-supported research mainly focused on self-directed learning, whereas museum-supported research emphasized inquiry-based learning. In terms of research issues, most library-supported research focused on patrons’ affective engagement, whereas museum-supported research emphasized learning performance. This study provides a citation-based approach to reveal the research trends and mainstream LibML research. The main contribution of combining co-citation and social network analysis is to provide a visualized network diagram of LibML research. Limitations of the methodological approach are noted. Discussion and future directions from the follow-up study are provided.

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Acknowledgements

This paper has benefited from the comments and suggestions of the Editor and four anonymous reviewers. Partial financial support from the Ministry of Science and Technology, Taiwan, under Grant Numbers MOST 110-2511-H-130-001, MOST 110-2410-H-033-034, and MOST 109-2511-H-011-002-MY3, is also acknowledged.

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Appendix: A list of the most influential co-citation pairs in the network

Appendix: A list of the most influential co-citation pairs in the network

#

Most influential co-citation pairs in the network

Example of the follow-up studies

 

Article 1

Article 2

Co-cites

1

#1-L9-ACE

#3-L8-CB

17 (red)

(Chin et al., 2019) Effects of an augmented reality-based mobile system on students’ learning achievements and motivation for a liberal arts course

2

#3-L8-CB

#5-L8-A

16 (red)

(Hsu et al., 2018) CoboChild: A blended mobile game-based learning service for children in museum contexts

3

#2-L5-AC

#16-L8-AC

11 (blue)

(Kabassi, 2019) Evaluating museum websites using a combination of decision-making theories

4

#2-L5-AC

#15-L8-AC

10 (blue)

(Law, 2018) Augmenting the experience of a museum visit with a geo-located AR app for an associated archaeological site

5

#3-L8-CB

#16-L8-AC

10 (blue)

(Louhab et al., 2019) Smart enhanced context-aware for flipped mobile learning: SECA-FML

6

#1-L9-ACE

#12-L8-CB

9 (blue)

(Chung et al., 2019) A review of experimental mobile learning research in 2010–2016 based on the activity theory framework

7

#1-L9-ACE

#14-L9-AC

9 (blue)

(Adnan et al., 2019) Cloud-supported machine learning system for context-aware adaptive M-learning

8

#2-L5-AC

#9-L8-B

9 (blue)

(Moorhouse et al., 2019) An experiential view to children learning in museums with Augmented Reality

9

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Tang, KY., Chen, CC., Hwang, GJ. et al. Did library learners benefit from m-learning strategies? Research-based evidence from a co-citation network analysis of the literature. Education Tech Research Dev 70, 1719–1753 (2022). https://doi.org/10.1007/s11423-022-10136-6

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