At STEMPlay Labs, we take fun seriously. In Part I of this series, we shared some inspiration and research that guides our game design and development. Here we are digging deeper into the technical approaches on the cutting edge of game design research.
Measuring fun in educational game research reveals a field still struggling with the basics. “Fun” is often used interchangeably with enjoyment, engagement, and even flow—but these concepts are theoretically distinct and require different measurement approaches.
The Intrinsic Motivation Inventory (IMI), with its Interest/Enjoyment subscale, remains the gold standard for measuring whether an activity is intrinsically motivating. The scale is well-validated across contexts and directly addresses the affective experience researchers typically mean by “fun.” For educational games specifically, EGameFlow (measuring concentration, challenge, autonomy, immersion, and social interaction across 27 items) and MEEGA+ (with excellent internal consistency at α = 0.927) offer comprehensive evaluation frameworks.
For measuring flow state specifically, the Flow Short Scale outperforms alternatives in gaming contexts. Notably, the Dispositional Flow Scale developed for sports fails to replicate its factor structure in gaming contexts—a warning about borrowing instruments from other domains without validation.
For children ages 5-10, the Fun Toolkit developed by Read and MacFarlane offers developmentally appropriate measures including the Smileyometer (visual analog scale with faces), the Fun Sorter for ranking products, and the Again-Again Table (“Would you play again?”). These tools address the practical challenge that young children struggle with abstract Likert scales.
Physiological measures offer objectivity but limited practicality. Eye-tracking can indicate attention and cognitive load; EEG measures brain activity correlating with emotion; galvanic skin response tracks arousal. However, these approaches require laboratory settings that limit ecological validity.
The most significant methodological warning from the literature concerns the popular Game Experience Questionnaire (GEQ): despite widespread use, it was never formally peer-reviewed published, and a systematic review found no evidence supporting its purported seven-factor structure. Researchers should proceed with caution or use validated alternatives.
The fun-learning relationship proves more complex than expected
Meta-analyses consistently show positive effects of game-based learning on cognitive outcomes (g = 0.49), motivational outcomes (g = 0.36), and behavioral outcomes (g = 0.25) according to Sailer and Homner’s 2020 synthesis. Other meta-analyses report effect sizes ranging up to g = 0.82 for student achievement. These are meaningful effects—but the pathway from fun to learning is neither direct nor guaranteed.
Iten and Petko’s critical 2016 study tested whether “fun playing the game is a predictor of learning success” and found that while anticipated enjoyment correlated with willingness to play, game enjoyment did not reliably predict actual test results. Self-reported cognitive gains related to enjoyment, but measured learning showed more complex patterns. Their conclusion: “Enjoying the learning game does not automatically mean learning success.”
Even more counter-intuitively, adding explicit “learning instructions” to educational games increased cognitive load and decreased learning outcomes. Playing “just for fun” enhanced effectiveness—suggesting that instrumentalizing fun may undermine it. This finding has profound implications for how teachers frame educational game use.
Flow and situational interest—two constructs often conflated with fun—turn out to be “cousins rather than twins.” A study by Kiili and colleagues found that learning gains were positively related to situational interest but not directly to flow. This suggests designers and researchers should distinguish between immersive absorption (flow) and curiosity-driven interest (which may matter more for learning).
Demographic differences reveal surprising patterns
Age differences in game-based learning effectiveness contradict common assumptions. Meta-analyses find that primary school students achieve higher learning outcomes and experience games as more motivating than secondary students. This may reflect the general decrease in academic interest common during adolescence, but it challenges assumptions that older students benefit more from sophisticated game-based approaches.
Gender differences prove equally surprising. Multiple studies show that female students benefit more from educational games than males—the opposite of what gaming gender stereotypes might predict. Girls show higher enjoyment in several studies and achieve higher learning gains on transfer problems. Researchers hypothesize that educational games may reduce stereotype threat for girls in math by de-emphasizing the mathematical nature of tasks. Importantly, a study using multidimensional gender measures found that gender-typed characteristics predict game preferences better than binary gender identity—suggesting individual variation matters more than gender categories.
Socioeconomic status may moderate effectiveness in concerning ways: preliminary evidence suggests game-based learning may be less beneficial for students with low SES, though more research is needed to understand why.
Design principles that connect fun to learning
The evidence base supports several actionable guidelines for creating educational games that are both enjoyable and effective:
Integration over coating: Learning objectives should emerge from gameplay, not be added to it. The best educational games make mastering content the primary source of engagement. When students feel competence from understanding the subject matter itself, motivation becomes self-sustaining.
Support all three SDT needs: Design for autonomy (meaningful choices, customization, player-driven paths), competence (appropriate challenge, clear feedback, skill progression), and relatedness (connection to characters, other players, or meaningful purposes). Games satisfying all three needs produce deeper engagement than those addressing only one.
Maintain flow through dynamic balance: Match challenge to skill level continuously using adaptive difficulty. Provide clear goals, immediate feedback, and minimize distractions that break immersion. Design both microflow loops and macroflow progression curves.
Leverage multiple keys to fun: Don’t rely solely on challenge. Incorporate exploration and curiosity (Easy Fun), meaningful impact (Serious Fun), and social elements (People Fun) alongside achievement-focused Hard Fun.
Optimize cognitive load: Minimize extraneous processing (unnecessary game elements), manage intrinsic load through scaffolding, and maximize germane processing (schema-building). Paradoxically, don’t over-instrument—playing “for fun” sometimes works better than explicit learning goals.
Design intrinsic fantasies: Game narratives should connect inherently to learning content, not serve as superficial themes pasted over drill exercises. When fantasy is “intrinsically” tied to skills being taught, it enhances rather than distracts from learning.
Conclusion: Fun as a design challenge, not a simple goal
The research reveals that “fun” in educational games is not a unitary construct that can be maximized through simple formulas. It encompasses multiple distinct experiences—immediate pleasure, productive struggle, flow states, curiosity satisfaction, social connection—that relate differently to learning outcomes and vary across learner populations.
Research gaps remain substantial. The field lacks consensus on how to define and measure fun operationally; longitudinal studies on sustained engagement are rare; and the mechanisms connecting enjoyment to learning transfer need clarification. But the existing evidence makes clear that understanding fun in educational games requires taking seriously what students, designers, and researchers each bring to this deceptively simple word.
About the Autor
Mia Barrett, MEd, is the Director of Education Research and Development at STEMPlay Labs, where she leads the research behind the games. With a background in educational technology and program evaluation, Mia ensures every STEMPlay Labs product is grounded in evidence-based practices and delivers measurable impact for students. She is passionate about creating learning experiences that help kids — especially those who’ve been told they “aren’t math people” — discover their inner STEM star. LinkedIn: linkedin.com/in/mia-barrett-med – Learn more about technology innovations in sex education at dfusioninc.com