Analysing Game Data to Evaluate Children’s Learning Progress with the EDURINO App Article Swipe
Ning Ding
,
Katherine Chen
,
Max Van Radecke
,
Irene Klemm
,
Franziska Meyer
,
Nicola S. Clayton
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.34190/ecgbl.18.1.2814
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.34190/ecgbl.18.1.2814
Game data offers a unique way to examine children’s learning progress in a gameplay context. Despite its relevance in evaluating the efficacy of game-based learning, this area is less researched than engagement and usability study. This paper presents a quantitative investigation of EDURINO, an educational gaming app targeting children aged 4-8. We implemented learning metrics to track children’s performance in EDURINO games. Specifically, the measurements included standardised variables from educational and experimental psychology, such as error count and duration. Here we reported data from a total of 18 EDURINO minigames in Literacy and Maths. Game data was collected by the learning metrics tracking system. A total of 66,463 players’ data was analysed using generalised linear mixed models with the lm4 package in R. Overall, children have shown improvements while playing the EDURINO app. We examined the relationships between children’s game performance, player demographics and game-specific features and discovered different patterns. First, the significant main effects of age and gender indicated that EDURINO’s educational content was developmentally sensitive and age appropriate. Second, children made fewer errors and spent less time when they were in the later rounds within a game or have played the same game more than once. Third, higher difficulty level undermined game performance. Worth noting is that the effect of game-specific factors varied among different educational content, and such discrepancy could be attributed to the nature of game design. When presented with engaging and interactive content, children may have chosen to deliberately make mistakes for fun effects. These findings are discussed in relation to game-based assessment for learning in EdTech research and industry. We also considered the interpretation of learning progress in EDURINO’s gameplay context while highlighting the need for further efficacy research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.34190/ecgbl.18.1.2814
- https://papers.academic-conferences.org/index.php/ecgbl/article/download/2814/2540
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403247442Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.34190/ecgbl.18.1.2814Digital Object Identifier
- Title
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Analysing Game Data to Evaluate Children’s Learning Progress with the EDURINO AppWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-10-07Full publication date if available
- Authors
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Ning Ding, Katherine Chen, Max Van Radecke, Irene Klemm, Franziska Meyer, Nicola S. ClaytonList of authors in order
- Landing page
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https://doi.org/10.34190/ecgbl.18.1.2814Publisher landing page
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https://papers.academic-conferences.org/index.php/ecgbl/article/download/2814/2540Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://papers.academic-conferences.org/index.php/ecgbl/article/download/2814/2540Direct OA link when available
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Educational game, Context (archaeology), Game design, Demographics, Usability, Psychology, Computer science, Mathematics education, Relevance (law), Tracking (education), Literacy, Multimedia, Human–computer interaction, Pedagogy, Political science, Biology, Sociology, Demography, Paleontology, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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