Over the past 40 years, the field of educational technology research has undergone significant development, evolving from a specialized niche area to a prominent subfield within education. Alongside this evolution, the focus of research has shifted from an almost sole emphasis on the effect of technology on learning outcomes to a broader examination of different aspects of educational technology (e.g., usage, evaluation). One such aspect that has garnered increasing attention in recent years is gamification (Domínguez et al., 2013; Huang et al., 2020). Coined in 2008 (Marczewski, 2013), gamification refers to using elements from games in non-gaming environments (Deterding et al., 2011; Garland, 2015). It is important to distinguish gamification from game-based learning, as the former as a broader concept only utilizes components of games in real-world situations, while the latter employs full-featured games to deliver skill or knowledge (Kapp, 2012; Wijaya et al., 2022). Gamification has found widespread application in various domains (Caponetto et al., 2014), including business (Moradian et al., 2014), math (Yıldırım, 2017), and science (Chen et al., 2018). In recent years, there has been a notable increase in studies exploring gamification in educational contexts (Seaborn and Fels, 2015). The key challenges facing modern education are often attributed to students’ lack of engagement and motivation to actively participate in the learning process. One potential solution to address this issue involves introducing rewards and recognition for students’ efforts and accomplishments, thereby fostering increased motivation and participation. This approach is rooted in the utilization of game elements to enhance the learning experience (Kiryakova et al., 2014). In education, gamification is not a standalone product, but rather a creative and structured process of incorporating game elements into educational settings with the aim of motivating learner behavior and promoting academic achievement (Educause, 2011; Jacobs, 2013). There is a growing body of evidence suggesting that gamification has become widely recognized as an effective tool in promoting learning outcomes in various educational settings (Groening and Binnewies, 2019; Lopez and Tucker, 2019).

Research has shown many advantages of gamification in educational contexts. For example, recent studies have validated the potential of gamification to improve student motivation, engagement, and interaction in education, while allowing them to immerse themselves in experiential learning (Lopez and Tucker, 2019). This is particularly important in the context of psychology, where motivation is seen as the driving force behind the behavior with a dynamic relationship between internal and intrinsic forces and affective processes leading to personal, social, and psychological well-being (Sailer et al., 2017). Gamification was found to increase extrinsic and intrinsic motivation by getting learners involved in tasks through ludic activities (Buckley and Doyle, 2016), and this is particularly evident in the educational context, where gamification promotes student participation in the classroom and stimulates direct interaction between students and teachers (Alsawaier, 2018). The relationship between gamification and learning in education is further explained by theories such as the theory of gamified learning, which suggests that gamification has the potential to positively impact both instructional content and learning outcomes (Landers, 2014). Manzano-León et al. (2021) also highlighted gamification as a highly effective tool in promoting the development of curricular, and cognitive competencies.

Despite the growing popularity and potential benefit of gamification, its effectiveness of it in improving academic performance remains uncertain. The previous empirical literature has produced mixed results. Some studies have reported positive effects of gamification with varying effect sizes (Chen and Chiu, 2016; Homer et al., 2018), while others have found no effects (Rachels and Rockinson-Szapkiw, 2018). These conflicting results make it difficult to draw conclusions about the effectiveness of gamification in enhancing learning outcomes. In order to shed more light on this issue, efforts need to be made to quantify the impact of gamification on student achievement in education. In this research, we conduct a meta-analytic study that thoroughly explores the issue through moderator analysis. The analysis includes a number of variables: user type, educational discipline, design principles for educational gamification, duration of “gameful” experience, learning environment, measurement of student outcomes and publication type. The subsequent section of this paper will delve into each of these moderators in detail.

Implementing gamification in educational settings can be a complicated endeavor, requiring collaboration and resources from various professionals. Therefore, it is crucial to examine the effectiveness of this instructional approach and understand which design features are effective under different circumstances. The current meta-analysis focused on student learning outcomes in gamification studies to shed light on the effectiveness of gamification and the moderators that improve learning outcomes, contributing to optimizing its use in educational settings.

To achieve this, this meta-analysis had two main objectives: (a) to estimate the overall effect size of gamification on learning outcomes, and (b) to identify factors contributing to variations in the effect sizes across studies through moderator analysis. Our results indicated a large effect size (g = 0.822 [0.567 to 1.078]) for the first objective. This effect size is much higher than that reported in prior research (0.40–0.60) (Bai et al., 2020; Huang et al., 2020; Sailer and Homner, 2020), providing strong evidence to support the use of gamification as an instructional approach in educational contexts.

For the second objective, the moderator analysis has shed light on the potential factors that may impact the magnitude of the effect. Regarding the user types, a significant moderating effect was found (Q_between = 21.126, p = 0.000), suggesting the effectiveness of gamification on student achievement varies across user types. Primary school users yielded a significantly larger effect size than secondary school users (Q_between = 10.010, p = 0.002). One possible reason could be related to differences in their motivation levels. Primary school learners may be more intrinsically motivated to learn (Vygotsky, 2016), and less focused on external rewards, which could enhance their engagement with gamification activities and lead to better academic achievement. In contrast, secondary learners may be more extrinsically motivated, and more focused on grades, which could limit their engagement with gamification activities and thus contribute to a lower effect size. Another possible explanation is that primary school users may have fewer preconceptions about traditional teaching methods and be more open to alternative approaches (e.g., gamified approaches) learning (Murillo and Martinez-Garrido, 2014). Furthermore, users at the higher education level exhibited a significantly larger effect size than secondary school users (Q_between = 15.757, p = 0.000). This difference could be attributed to their higher intrinsic motivation to learn, which gamification activities can sustain over the long term. Another possible explanation concerns the greater familiarity of higher education users with technology and digital tools (Kim and Castelli, 2021), which makes them more receptive to gamified learning activities that incorporate these resources. This familiarity can also make it easier for them to engage with the various features and functions of gamified learning activities, which may be more complex or multifaceted than those used in secondary school contexts.

The educational discipline in which gamification is applied significantly impacts its effectiveness in promoting student achievement. Science showed the strongest effect size, followed by math, engineering/computing, social science, and business, with significant differences identified between science and business (Q_between = 10.297, p = 0.001), engineering/computing (Q_between = 7.065, p = 0.008), and social science (Q_between = 11.428, p = 0.001). These results are consistent with previous studies that have demonstrated the effectiveness of gamification in science education (Kapp, 2012; Chen et al., 2018). Several potential factors may contribute to the variation across different subject disciplines. One possible reason is the nature of the subject matter. Science education is generally known to involve complex concepts and problem-solving tasks that may be more challenging for students to grasp (Hmelo-Silver, 2004), making gamification an effective way to increase engagement and motivation. In addition, hands-on learning opportunities in science education may lend themselves well to gamification activities. Pedagogical approaches may also play a part, with inquiry-based and problem-based learning commonly used in science education aligning well with gamified learning (Lai and Bower, 2020). In contrast, business education may be more lecture-based and centered on theoretical knowledge (Carriger, 2015), making it less suitable for gamified learning approaches that rely on exploration and experimentation. These findings may have practical implications for instructional designers and educators, who should consider the subject discipline when designing and utilizing gamification in educational contexts.

The current meta-analysis provides evidence that design principles for educational gamification can be effective for improved learning outcomes in educational contexts. However, the effectiveness of gamification seems to be contingent on the particular design principle. The highest effect size (Hedges’ g = 1.285) was seen in the “mechanics + dynamics + esthetics” subcategory, suggesting that combining three may be the most effective design principle for promoting gamified learning outcomes. The possible reason for this could be that such a combination offers a well-rounded approach that caters to both the measurable and intangible aspects of the gaming experience while also addressing subjective factors. Mechanics are defined as the quantifiable components of the game (e.g., rewards, prizes, points, and rankings). These elements are concrete and can be easily measured and tracked. Dynamics, on the other hand, designates to the behavior of the game mechanics over time and how they interact with each other and with the player (Hakulinen et al., 2013). Dynamics are the intangible aspects of the game that can create a unique experience for each player (e.g., events, tasks, feedback, and competition) (Hakulinen et al., 2013). Esthetics are the emotional responses that players experience when engaging with the game system, which may encompass elements of sensations, narrative, companionship, expression, or entertainment (Hakulinen et al., 2013). Our finding aligns with the MDA framework, which underscores the significance of the cohesive interplay between all three components in generating successful learning outcomes. This may be due to the clear goals and feedback provided by mechanics, as well as the engaging and immersive experience created by esthetics. Moreover, the observed difference in effect size between the combination of three and “dynamics” suggests that including mechanics and esthetics in gamified design may result in better learning outcomes. This could be attributed to mechanics offering explicit goals and feedback for the player, while esthetics contributes to a captivating and immersive game experience. Conversely, the combination of dynamics and esthetics alone may hinder the effectiveness of gamification concerning the identified negative effect size (Hedges’ g = −3.162). The possible explanations for this adverse effect size may pertain to the working mechanism of the MDA framework (Hakulinen et al., 2013). From the designers’ perspective, the MDA framework suggests that game mechanics should be established before moving on to dynamics and esthetics. This sequence guarantees that the structure of game elements is grounded in rules and systems before attending to the subjective aspects of player experience. Therefore, the negative effect size of “dynamics + esthetics” could be ascribed to the lack of clear goals and feedback provided by the mechanics, resulting in reduced learning outcomes. On the other hand, from the users’ perspective, esthetics may be the first aspect of the gamified learning they encounter, followed by dynamics and then mechanics. This sequence implies that learners may prioritize the experiential aspects of the game elements (e.g., immersion, engagement) over the more concrete elements (e.g., goals, feedback). Thus, if the mechanics fail to captivate the learners, they might not be motivated to persist in playing and learning, leading to a negative effect size for “dynamics + esthetics.”

The duration of “gameful” experience was revealed to be a significant moderator (Q_between = 15.512, p = 0.008). The duration of “gameful” experience lasting “>1 semester” were found to have a significantly larger effect size than gamified interventions lasting “1 month-3 months,” “1 week-1 month,” and “3 months-1 semester”. The findings add to the existing literature (Beemer et al., 2019; Sailer and Homner, 2020) on the duration of “gameful” experience, highlighting the importance of longer-term interventions in enhancing learning outcomes. Several potential causes may contribute to these findings. First, longer “gameful” experiences may give students more time to consolidate their learning, leading to higher retention of knowledge and improved learning outcomes. Moreover, prolonged exposure to the game mechanics in longer interventions may promote a deeper understanding of the learning content. As learners become more proficient with the game mechanics, they may be more capable of concentrating on the learning material and achieving better performance. Another possible explanation for the larger effect size observed in longer “gameful” experiences is that they allow learners to explore diverse strategies and techniques for navigating the game mechanics. With more time devoted to practice, learners may be able to more effectively apply their knowledge to different situations, resulting in improved learning outcomes. It is worth noting that the “gameful” experience lasting “<1 week” did not have a statistically significant effect size compared to other types. However, it still had a larger effect size than interventions lasting “1 month-3 months,” “1 week-1 month,” and “3 months-1 semester.” This finding may be ascribed to the phenomenon of hyperbolic discounting, which refers to people’s cognitive tendency to prefer short-term rewards over long-term rewards (Ainslie, 1975; Standage et al., 2005; Kim and Castelli, 2021).

Regarding the learning environment, our analysis revealed statistically significant moderation effects (Q_between = 180.408, p = 0.000), indicating its importance in the effectiveness of gamification in educational settings. The offline learning environment was found to have a significantly greater effect size compared to both online and hybrid learning environments. Three explanations may account for this unexpected outcome. The first one pertains to tangible learning experiences. Offline learning environments may offer more hands-on or tangible learning experiences (e.g., awarding real gifts or badges), which can better engage and motivate learners and improve their performance (Çakıroğlu et al., 2017). Second, personalized feedback may play a role. The “offline” condition may offer more opportunities for learners to receive personalized feedback and guidance from instructors. At the same time, digital learning environments may provide only automated feedback without a detailed explanation, often not tailored to individual needs. According to Ebadi et al. (2021), technical delays and the high pace of the game are among the most frequently reported issues in digital learning, which may lead to learner demotivation and poor performance. Third, social interaction may possibly contribute to the variation. Offline learning environments allow for more face-to-face interaction and collaboration among learners, leading to improved learning outcomes. Online education, on the other hand, may leave students feeling isolated from instructors and peers, which can hinder their learning achievement (Manner, 2003). In addition, online support groups are continuously open to new membership and may experience fluctuations in membership, making it difficult for them to engage in effective group work and ultimately leading to lower performance (Gary and Remolino, 2000). Therefore, it is essential for educators or instructors to carefully design the learning environment to maximize the effectiveness of gamification in promoting academic achievement.

In relation to the measurement of student outcomes, no significant moderating effect was observed (Q_between = 8.416, p = 0.015). This implies that the effectiveness of gamification in educational settings remains consistent across different measures employed to assess student outcomes. However, there were variances in effect sizes observed among these measures. Among them, motivation showed the largest effect size (Hedges’ g = 2.206). There are several potential reasons that could account for this finding. Firstly, gamification has the capacity to create an environment that allows for diverse and divergent thinking, leading to the exchange of multiple perspectives among students. Such an environment promotes collective learning and collaboration, thereby enhancing intrinsic motivation (Stansbury and Earnest, 2016). Secondly, the social aspect of gamified environments can also contribute to increased motivation. Many gamifications incorporate elements of competition and collaboration, allowing students to compete against each other or work together towards a common goal. This social interaction and sense of community can foster a sense of belonging and purpose, which are essential drivers of motivation (Buckley and Doyle, 2016; Koivisto and Hamari, 2019). Furthermore, the use of game elements in gamified environments also serves as a motivational affordance, simulating learners’ learning behavior (Inocencio, 2018). By incorporating game mechanics into the educational experience, gamification may tap into individuals’ intrinsic motivation to achieve goals and overcome challenges (Manzano-León et al., 2021). These elements create a sense of accomplishment and progress, which can be highly motivating for students. On the other hand, the weakest effect size was observed for engagement (Hedges’ g = 0.383), potentially due to learners’ perceived value of education. González-Fernández et al. (2022) suggested that learners’ behavioral engagement can be influenced by the importance they attach to their education. If gamification elements fail to address and encourage these factors sufficiently, it can negatively impact the observed levels of engagement. Therefore, game elements should be carefully designed to promote positive engagement and tailored to users’ needs and preferences, underscoring the necessity for more rigorous primary study designs to effectively mitigate alternative explanations for variations in learning outcomes across varying conditions (Sailer and Homner, 2020).

Concerning publication type, no significant moderating effect was observed (Q_between = 1.841, p = 0.389). However, we observed variations in effect size across different kinds of publications. Specifically, journal articles had the largest effect size (Hedges’ g = 0.936), followed by theses/dissertations (Hedges’ g = 0.722), and conference proceedings had the smallest effect size (Hedges’ g = 0.585). While these differences were not statistically significant, they may be indicative of underlying factors that influenced the effectiveness of gamification in different types of studies. One possible explanation for the observed differences in effect size is that certain types of publications may be more likely to include rigorous research designs and methods, which could lead to larger effect sizes. For example, journal articles typically undergo a rigorous peer-review process and are held to high standards of research quality and validity. The findings may suggest that the quality and rigor of the research may play a role in determining the effectiveness of gamification in educational contexts. Besides, further research is needed to fully understand the factors that influence the effectiveness of gamification in different types of publications.

There are several limitations to be considered in this research. Firstly, it is important to acknowledge the possibility of variability in the quality and reliability of the included studies. Despite extensive efforts to adhere to stringent inclusion criteria and methodological rigor, inherent disparities within the study design or sample sizes among the selected studies may persist. These variations could have the potential to introduce heterogeneity into the analysis and may possibly affect the generalizability of results to some extent. Nonetheless, we made a concerted effort to carefully consider and address these potential variations in study characteristics within the scope of this research.

Additionally, this study investigated the moderating effect of several important variables based on a comprehensive review of the literature. However, given the relative novelty and rapid evolution of the field of gamification, we also identified additional emerging variables that could potentially impact gamification research and represent valuable avenues for further investigation. These variables include player characteristics (Van Berlo et al., 2023), the role of user experience (Anim et al., 2023), reliability of outcome measures (Inocencio, 2018), teaching role of the family (Gözüm and Kandır, 2020, 2021; Papadakis et al., 2022). Unfortunately, despite thoroughly examining and coding variables from the identified studies, we encountered challenges in obtaining sufficient data that met the necessary inclusion and exclusion criteria. Consequently, we were unable to include these variables in our analysis. It is our hope that future empirical investigations in related areas will consider yielding the requisite data to enable their incorporation into future meta-analyses.

This study has produced valuable findings regarding the impact of gamification on student learning outcomes, which may contribute to advancing our knowledge of its potential in educational contexts. First, our study revealed a significant overall effect size of 0.822 [0.567 to 1.078] for gamification on student learning outcomes, implying that gamification may hold promise as a viable approach for promoting teaching and learning in diverse educational contexts. Second, we discovered that several factors had a moderating impact, including the user type (e.g., primary school users demonstrated the greatest effect size), educational discipline (e.g., science students exhibited a considerably higher effect size than those in the disciplines of business, engineering/computing, and social science), design principles for educational gamification (e.g., the largest effect size observed in the design principle of “mechanics + dynamics + esthetics”), duration of “gameful” experience (e.g., the gamified interventions lasting more than one semester showed the greatest impact), and learning environment (e.g., the “offline” condition produced the largest effect size). These findings aid researchers in pinpointing the variables that impact gamification’s effectiveness in improving learning outcomes. We expect these results will encourage greater interest in gamification research and more inquiry into its potential for enhancing teaching and learning in educational contexts.

The findings of this study provide significant insights into the use of gamification to enhance student learning outcomes. Based on the results, the following recommendations are proposed to optimize the effectiveness of gamification in educational contexts:

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

ML: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review & editing. SM: Conceptualization, Data curation, Investigation, Resources, Supervision, Validation, Visualization, Writing – review & editing, Software. YS: Project administration, Resources, Validation, Visualization, Writing – review & editing, Investigation, Conceptualization, Software.