Applications of augmented reality-based natural interactive learning in magnetic field instruction
Abstract
Educators must address several challenges inherent to the instruction of scientific disciplines such as physics -- expensive or insufficient laboratory equipment, equipment error, difficulty in simulating certain experimental conditions. Augmented reality (AR) can be a promising approach to address these challenges. In this paper, we discuss the design and implementation of an AR and motion-sensing learning technology that teaches magnetic fields in a junior high school physics course. The purpose of this study is to explore the effects of using natural interaction on students’ physics learning and deep understanding compared to traditional learning tools. The 38 eighth graders who participated in this study were assigned to either an experimental group or a control group. Analysis of the results shows that the AR-based motion-sensing software can improve students’ learning attitude and learning outcome. This study provides a case for the application of AR technology in secondary physics education.
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Children might face more difficulties in interacting with these new methods.
“Children might face more difficulties in interacting with these new methods [92], [93].”
From Extracting Design Guidelines for Augmented Reality Serious Games for Children (2022) · cites this paper as [92]
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Czok, V., Krug, M., Müller, S., Huwer, J., & Weitzel, H. (2023) · Sustainability · doi:10.3390/su152115313 · Cited by 44 · see citing works
Extracting Design Guidelines for Augmented Reality Serious Games for Children
Li, J., Spek, E. V. D., Hu, J., & Feijs, L. (2022) · IEEE Access · doi:10.1109/access.2022.3184775 · Cited by 29 · see citing works
- Authors
- Cai, S., Chiang, F.-K., Sun, Y., Lin, C., & Lee, J. J.
- Affiliations
- Beijing Normal University (CN)
- Columbia University (US)
- Corresponding author
- Cai, Su
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2016-05-24
- Volume
- 25
- Issue
- 6
- Pages
- 778-791
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W2398239086
- Cited by
- 253
- References
- 0
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 35 · 2023: 40 · 2022: 41 · 2021: 32 · 2020: 27 · 2019: 24 · 2018: 17 · 2017: 4
- FWCI
- 9.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Learning Environments (publishedVersion)
- Funders
- Comprehensive Discipline Construction Fund of Faculty of Education (2015int04)
- Fundamental Research Funds for National Engineering Research Center for Educational Technology (201501)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Information Systems › Multimedia Learning Systems
- Social Sciences › Education › Educational Methods and Outcomes
- Keywords
- augmented reality, magnetic field, physics education, motion sensing, natural interaction, hands-on learning, junior high school students, learning attitude, learning outcomes, deep understanding
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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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