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Tailored gamification in education: A literature review and future agenda
Open accessAbstract
Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students’ needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students’ gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.
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“Such discrepancies may stem from variations in implementation (González-Fernández et al., 2022), student characteristics, or subject areas (Oliveira et al., 2023).”
From Gamification on Mathematics Engagement and Motivation in Secondary School and Higher Education: A Systematic Review and Meta-Analysis (2026) · cites this paper as Oliveira et al., 2023
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Metadata
- Authors
- Oliveira, W., Hamari, J., Shi, L., Toda, A. M., Rodrigues, L., Palomino, P. T., & Isotani, S.
- Affiliations
- Tampere University (FI)
- Durham University (GB)
- Universidade de São Paulo (BR)
- Corresponding author
- Oliveira, Wilk
- ORCID
- Venue
- Education and Information Technologies
- Published
- 2022-06-29
- Volume
- 28
- Issue
- 1
- Pages
- 373-406
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4293060971
- Cited by
- 341
- References
- 90
- Citations by year
- 2026: 65 · 2025: 97 · 2024: 101 · 2023: 73 · 2022: 5
- FWCI
- 105.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- Durham Research Online (Durham University) (publishedVersion, other-oa)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo (2020/02801-4, 2018/15917-0)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- gamification, personalized learning, student motivation, student engagement, flow experience, gamification design, learning performance
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 ›
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