Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://eric.ed.gov/?id=EJ1355016 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Did Library Learners Benefit from M-Learning Strategies? Research-Based Evidence from a Co-Citation Network Analysis of the Literature Tang, Kai-Yu Chen, Chao-Chen Hwang, Gwo-Jen Tu, Yun-Fang Learning Strategies Handheld Devices Educational Technology Computer Oriented Programs Libraries Museums Independent Study Inquiry Learner Engagement Performance Social Networks Network Analysis 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.