Design fit in gamified online programming learning environment

The strategy for designing online programming learning through a gamified learning environment and its effects on learning outcomes remains a subject of debate. Using Task-Technology Fit (TTF) theory, this study investigates the impact of design fit on learning programming that supports game charact...

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Published in:Learning and instruction Vol. 96; p. 102087
Main Authors: Wang, Wei-Tsong, Sari, Mega Kartika
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2025
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ISSN:0959-4752
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Abstract The strategy for designing online programming learning through a gamified learning environment and its effects on learning outcomes remains a subject of debate. Using Task-Technology Fit (TTF) theory, this study investigates the impact of design fit on learning programming that supports game characteristics on learning outcomes. The technique of component-based structural equation modeling was used to examine the research hypotheses. Learning performance is influenced by cognitive load and student perception of TTF. Additionally, task complexity affects students' applicability of learning strategies and perceptions on TTF. The research findings can be used as guidelines for developing gamified online programming learning platforms by considering the features of the learning contexts to enhance students’ learning outcomes. •Attaining the fit of gamification design is critical to effective learning outcomes.•Task complexity and learning strategies are critical to design fit.•Task complexity indirectly affects design fit via learning strategies.•Cognitive load mediates the effect of TTF on learning performance.•Achieving design fit can enhance learning outcomes.
AbstractList The strategy for designing online programming learning through a gamified learning environment and its effects on learning outcomes remains a subject of debate. Using Task-Technology Fit (TTF) theory, this study investigates the impact of design fit on learning programming that supports game characteristics on learning outcomes. The technique of component-based structural equation modeling was used to examine the research hypotheses. Learning performance is influenced by cognitive load and student perception of TTF. Additionally, task complexity affects students' applicability of learning strategies and perceptions on TTF. The research findings can be used as guidelines for developing gamified online programming learning platforms by considering the features of the learning contexts to enhance students’ learning outcomes. •Attaining the fit of gamification design is critical to effective learning outcomes.•Task complexity and learning strategies are critical to design fit.•Task complexity indirectly affects design fit via learning strategies.•Cognitive load mediates the effect of TTF on learning performance.•Achieving design fit can enhance learning outcomes.
ArticleNumber 102087
Author Wang, Wei-Tsong
Sari, Mega Kartika
Author_xml – sequence: 1
  givenname: Wei-Tsong
  orcidid: 0000-0002-1448-7433
  surname: Wang
  fullname: Wang, Wei-Tsong
  email: wtwang@mail.ncku.edu.tw
  organization: Department of Industrial and Information Management, National Cheng Kung University, 1st University Road, Tainan, 701, Taiwan
– sequence: 2
  givenname: Mega Kartika
  orcidid: 0000-0002-9030-3253
  surname: Sari
  fullname: Sari, Mega Kartika
  email: megakartika18@gmail.com
  organization: Institute Information Management, National Cheng Kung University, 1st University Road, Tainan, 701, Taiwan
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Keywords Gamified online programming learning
Task-technology fit
Learning performance
Cognitive load
Design fit
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Snippet The strategy for designing online programming learning through a gamified learning environment and its effects on learning outcomes remains a subject of...
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SubjectTerms Cognitive load
Design fit
Gamified online programming learning
Learning performance
Task-technology fit
Title Design fit in gamified online programming learning environment
URI https://dx.doi.org/10.1016/j.learninstruc.2025.102087
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