game-based-learning
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- PaperBritish Journal of Educational Technology3 Jul 2026
A balance between stability and flexibility: Adaptive patterns of self‐regulated learning processes shape game‐based learning
Elizabeth B. Cloude, Stefan E. Huber, Jingwei Wei, Bianca Esmanhoto et al.
This study investigated self-regulated learning (SRL) as a complex system in game-based learning (GBL) by analyzing patterns and interactions among cognitive, affective, metacognitive, and motivational processes. Using multimodal data (think-alouds, facial expressions, skin conductance), the researchers found that learning outcomes were best predicted by a balance between stability and flexibility in cognitive, affective, and metacognitive state transitions, explaining 31% of variability in learning. Physiological measures of motivation alone did not predict learning performance, suggesting that motivation's role is mediated through cognitive and metacognitive engagement.
Original abstract
To ensure learning efficiency in game‐based learning (GBL), learners must regulate cognitive, affective, metacognitive and motivational (CAMM) processes, collectively known as self‐regulated learning (SRL). SRL is dynamic and non‐linear, characterized by regulatory patterns and CAMM interactions that lead to macro‐level SRL behaviours. In this paper, we explore SRL as a complex system by analysing patterns and interactions among CAMM processes during GBL and examine their relation to learning outcomes. Thirty‐seven ( n = 37) healthy adults used Antidote COVID‐19, a GBL environment designed to increase emotional engagement and biology knowledge. To define momentary CAMM processes, subjective measures of cognitive, affective and metacognitive states via think‐ and emote‐alouds were synchronized with two physiological correlates of motivation: intensity of facial expressions (arousal) and skin conductance response. Sequence analysis and recurrence quantification defined the regularity of patterns within CAMM components, and transfer entropy estimated concurrent interactions between CAMM components. Our results indicated a quadratic relationship between the turbulence (a measure of regularity) of cognitive, affective and metacognitive (CAM) state transitions (via think‐ and emote‐aloud methods) provided the best fit, explaining 31% of variability in learning. This highlights an optimal balance between stability and flexibility in CAM state transitions that were beneficial for learning. However, physiological correlates of motivation were not predictive regarding learning performance. This lack of predictive capability of the considered measures may reflect their limitations in capturing nuanced motivation dynamics relevant to learning or suggest that motivation's role in learning is predominantly mediated through cognitive and metacognitive engagement rather than directly influencing outcomes. Practitioner notes What is already known about this topic Game‐based learning (GBL) is a promising intervention for improving science knowledge, but it places high self‐regulatory demands on learners. Learners must manage cognitive, affective, motivational and metacognitive (CAMM) processes to ensure GBL efficiency; otherwise known as self‐regulated learning (SRL). Prior research has shown that CAMM processes are each important for learning in games, but they are often studied in isolation rather than as interacting processes over time. What this paper adds This study conceptualizes SRL in GBL as a complex system utilizing mixed multimodal data, emphasizing patterns and interactions among CAMM processes rather than static averages. Results show that learning is maximized when learners exhibit an optimal balance between stability and flexibility in their cognitive, affective and metacognitive state transitions, neither overly rigid nor overly random regulation patterns. Cognitive, affective and metacognitive processes (assessed via think–/emote‐alouds) were strongly related to learning gain, whereas physiological indicators of motivation alone were not predictive. Implications for practice and/or policy Designers of GBL environments should support adaptive regulation, encouraging learners to flexibly shift SRL strategies and emotions while maintaining coherence in their learning process. Educators and researchers should be cautious about relying solely on physiological measures (eg, arousal) for assessing learning effectiveness, as these may not capture meaningful regulatory dynamics. Policies and evaluation frameworks for educational games should prioritize tools and analytics that capture process‐level SRL patterns over time, rather than focusing exclusively on outcomes or isolated behavioural indicators.
- PaperLanguage Teaching Research1 Jul 2026
Gamified Language Learning: The Impact of Role-Playing Games on Beginner Japanese Acquisition
Yi-Fan Wang, Mei-Hua Hsu, Max Yue-Feng Wang
A quasi-experimental study with 60 undergraduates found that a theory-driven role-playing game (Nihongo Quest) significantly improved vocabulary (d=1.45) and grammar (d=1.05) gains in beginner Japanese learners compared to traditional textbook instruction. The game also increased intrinsic motivation and reduced language learning anxiety. These effects are attributed to the alignment with SLA frameworks including input-interaction-output theory, self-determination theory, and sociocultural theory.
Original abstract
This study examined the effectiveness of Nihongo Quest, a theory-driven role-playing game (RPG) developed for beginner learners of Japanese. Sixty undergraduate students participated in an eight-week quasi-experimental study comparing a game-based learning (GBL) condition with a traditional textbook-based condition. Results indicated that participants in the GBL group demonstrated significantly greater gains in vocabulary ( d = 1.45) and grammar ( d = 1.05) than those in the textbook group. In addition, the GBL group reported higher levels of intrinsic motivation ( η ² = .25) and lower levels of language learning anxiety ( η ² = .16). These outcomes are attributed to the game’s systematic alignment with core second language acquisition (SLA) frameworks, including the input–interaction–output (IIO), self-determination theory (SDT), willingness to communicate (WTC), and sociocultural theory (SCT). Through the integration of multimodal literacy tasks, interactive dialogues, and culturally situated scenarios, Nihongo Quest reduced cognitive load while supporting both linguistic and pragmatic development. The findings suggest that theory-informed RPG-based instruction can effectively enhance beginner-level Japanese language acquisition and offer a transferable instructional design model for other complex-script languages, such as Chinese and Korean.