An Augmented-Reality-Based Concept Map to Support Mobile Learning for Science
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Learning mathematical objects with AR proved more effective than teacher drawings or verbal descriptions, and students faced no difficulties in visualizing objects in three-dimensional space.
“Through this research, it was determined that learning mathematical objects proved more effective than teacher drawings or verbal descriptions, and students faced no difficulties in visualizing objects in three-dimensional space.”
From Augmented Reality and Student Motivation: A Systematic Review (2013-2024) (2025) · cites this paper as Chen et al. (2016)
Cites
An analysis of variance test for normality (complete samples)
SHAPIRO, S. S., & WILK, M. B. (1965) · Biometrika · doi:10.1093/biomet/52.3-4.591 · Cited by 23,480 · see citing works
Novak, J. D., Gowin, D. B., & Kahle, J. B. (1984) · doi:10.1017/cbo9781139173469 · Cited by 5,199 · see citing works
Development and use of the ARCS model of instructional design
Keller, J. M. (1987) · Journal of Instructional Development · doi:10.1007/bf02905780 · Cited by 2,332 · see citing works
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Augmented reality for STEM learning: A systematic review
Ibáñez, M.-B., & Delgado-Kloos, C. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.05.002 · Cited by 1,036 · see citing works
Systematic review and meta-analysis of augmented reality in educational settings
Garzón, J., Pavón, J., & Baldiris, S. (2019) · Virtual Reality · doi:10.1007/s10055-019-00379-9 · Cited by 562 · see citing works
Meta-analysis of the impact of Augmented Reality on students’ learning gains
Garzón, J., & Acevedo, J. (2019) · Educational Research Review · doi:10.1016/j.edurev.2019.04.001 · Cited by 491 · see citing works
Pellas, N., Fotaris, P., Kazanidis, I., & Wells, D. (2019) · Virtual Reality · doi:10.1007/s10055-018-0347-2 · Cited by 364 · see citing works
The Effectiveness of Concept Maps on Students’ Achievement in Science: A Meta-Analysis
Anastasiou, D., Wirngo, C. N., & Bagos, P. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09877-y · Cited by 36 · see citing works
Wang, X.-M., Wang, J.-L., Xu, S.-Y., & Xu, S.-J. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00554-2 · Cited by 14 · see citing works
“…the rapid development of information technology, the form of combining concept mapping has expanded from traditional books (Uzuntiryaki & Geban, 2005) to computer platforms (Kwon & Cifuentes, 2007) and further incorporated emerging technologies such as augmented reality (AR) (Chen et al., 2016).”
Augmented Reality and Student Motivation: A Systematic Review (2013-2024)
Zuo, R., Wenling, L., & Xuemei, Z. (2025) · Journal of Computers for Science and Mathematics Learning · doi:10.70232/jcsml.v2i1.23 · Cited by 13 · see citing works
- Authors
- Chen, C.-H., Chou, Y.-Y., & Huang, C.-Y.
- Affiliations
- National Cheng Kung University (TW)
- Corresponding author
- Chen, Chien-Hsu
- ORCID
- Chen, Chien-Hsu
- Venue
- The Asia-Pacific Education Researcher
- Published
- 2016-02-23
- Volume
- 25
- Issue
- 4
- Pages
- 567-578
- ISSN
- 0119-5646 (ISSN-L)
- 2243-7908
- OpenAlex
- W2281750100
- Cited by
- 149
- References
- 3
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 18 · 2023: 13 · 2022: 24 · 2021: 21 · 2020: 24 · 2019: 14 · 2018: 14 · 2017: 6 · 2016: 1
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The Asia-Pacific Education Researcher (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Physical Sciences › Computer Science › Information Systems › Multimedia Learning Systems
- Keywords
- augmented reality, concept maps, mobile learning, science education, instructional scaffolding, elementary school students, learning outcomes, student motivation, content organization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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