Animating eco-education: To see, feel, and discover in an augmented reality-based experiential learning environment
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Interactive technology can help make the learning process more enjoyable for students.
“This fi nding con fi rms previous results (Huang et al., 2016) that interactive technology can help make the learning process more enjoyable for students.”
From Religious diversity education: raising children’s awareness of religious diversity through augmented reality (2023) · cites this paper as Huang et al., 2016
Cites
Fredrickson, B. L. (2001) · American Psychologist · doi:10.1037//0003-066x.56.3.218 · Cited by 3,512 · see citing works
Emotional design in multimedia learning: Effects of shape and color on affect and learning
Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014) · Learning and Instruction · doi:10.1016/j.learninstruc.2013.02.006 · Cited by 550 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
The state of immersive technology research: A literature analysis
Suh, A., & Prophet, J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.019 · Cited by 841 · 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
A meta-analysis of virtual reality training programs for social skill development
Howard, M. C., & Gutworth, M. B. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103707 · Cited by 234 · see citing works
Czok, V., Krug, M., Müller, S., Huwer, J., & Weitzel, H. (2023) · Sustainability · doi:10.3390/su152115313 · Cited by 44 · see citing works
Li, X.-Z., Chen, C.-C., & Kang, X. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02123-w · Cited by 14 · see citing works
Self-citationKim, J. W., Wang, K., Dorneich, M. C., Winer, E., Brown, L. J., & Whitehurst, G. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3667033
- Authors
- Huang, T.-C., Chen, C.-C., & Chou, Y.-W.
- Affiliations
- National Taichung University of Science and Technology (TW)
- National Chung Hsing University (TW)
- Corresponding author
- Chen, Chia-Chen
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 96
- Pages
- 72-82
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2285948776
- Cited by
- 453
- References
- 2
- Citations by year
- 2026: 23 · 2025: 42 · 2024: 54 · 2023: 63 · 2022: 61 · 2021: 44 · 2020: 66 · 2019: 39 · 2018: 37 · 2017: 20 · 2016: 4
- FWCI
- 20.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- Funders
- Ministry of Science and Technology of the Republic of China (MOST 104-2511-S-025-002-MY3, MOST 104-2511-S-005-003, MOST 103-2511-S-025-001-MY3, MOST 103-2622-S-005-001-CC3)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Information Systems › Multimedia Learning Systems
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental and sustainability education
- Keywords
- environmental education, augmented reality, experiential learning, Kolb's learning cycle, field-based learning, botanical gardens, positive emotions, emotional attachment to nature, junior high school students, learning outcomes, technology-enhanced learning
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
