An enjoyable learning experience in personalising learning based on knowledge management: A case study
Open accessAbstract
Background:Many attempts at personalisation have been made in education. They all collect learning data and analyse learning behaviours, and ultimately achieve personalised learning dynamically. However, further research is needed on the ways to effectively access and analyse information about learning within an enjoyable environment and with positive results when realising personalised learning.Material and methods:In order to solve this problem, we connect the time in class and after class with semantic knowledge and combine these elements with gamification and a better interaction experience. We explore whether this teaching method can offer students a better learning experience and positive learning outcomes.Results:Our approach plays an obvious role in personalised learning.Conclusions:Our results indicate that a teaching method which connects the two parts of a class with gamification and a means of interaction in AR (augmented reality) produces novel and enjoyable feelings, stimulates students' enthusiasm and improves the learning effects when they do personalised learning.
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?
Song et al.'s application increased students' learning outcomes, engagement, and satisfaction and offered more personalized learning opportunities.
“the specific application managed to increase students' learning outcomes, engagement, and satisfaction and offered more personalized learning opportunities.”
From Integrating Augmented Reality, Gamification, and Serious Games in Computer Science Education (2023) · cites this paper as [39]
Cites
Ryan, R. M., & Deci, E. L. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.68 · Cited by 27,815 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Integrating Augmented Reality, Gamification, and Serious Games in Computer Science Education
Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2023) · Education Sciences · doi:10.3390/educsci13060618 · Cited by 94 · see citing works
- Authors
- Song, D., Xu, H., Yu, T., & Tavares, A.
- Affiliations
- Jilin University (CN)
- Centro ALGORITMI (PT)
- Corresponding author
- Song, Donglei
- Venue
- EURASIA Journal of Mathematics, Science and Technology Education
- Publisher
- Modestum Publishing Ltd
- Published
- 2017-06-15
- Volume
- 13
- Issue
- 7
- ISSN
- 1305-8215 (ISSN-L)
- 1305-8223
- OpenAlex
- W2616918992
- Cited by
- 21
- References
- 1
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 4 · 2017: 2
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Eurasia Journal of Mathematics Science and Technology Education (publishedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Keywords
- personalized learning, knowledge management, gamification, augmented reality, learning experience, student engagement, learning outcomes, semantic knowledge
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