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Article

Examining the Influential Mechanism of English as a Foreign Language (EFL) Learners’ Flow Experiences in Digital Game-Based Vocabulary Learning: Shedding New Light on a Priori Proposed Model

1
School of Foreign Studies, Northwestern Polytechnical University, Xi’an 710072, China
2
School of Foreign Languages, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(2), 125; https://doi.org/10.3390/educsci15020125
Submission received: 20 October 2024 / Revised: 9 December 2024 / Accepted: 20 January 2025 / Published: 22 January 2025
(This article belongs to the Section Technology Enhanced Education)

Abstract

:
Over the last ten years, continuous attention has been paid to the use of digital games in vocabulary learning. Their effectiveness and availability have been widely discussed. However, the experiences of language learners and the underlying patterns of their engagement while using digital games for vocabulary learning remain underexplored. In order to fill this significant gap, this study aimed to examine the influential mechanism of English as a Foreign Language (EFL) learners’ flow experiences in digital game-based vocabulary learning (DGBVL). The sample consisted of 306 Chinese EFL learners who had DGBVL app usage experience, and data collection was based on a DGBVL flow experience instrument employed through an online platform. Structural equation modeling (SEM) was employed to assess the reliability and validation of the existing scale for various DGBVL apps. A multi-group analysis was then conducted, revealing that the influential mechanism was a process in which the effects of antecedents on outcomes could be mediated by flow experiences. In addition, the role of usage frequency was also explored, and three paths were found to differ across three usage frequency levels (i.e., seldom, sometimes, and always): the effect of balance of skill and challenge on enjoyment, the effect of enjoyment on satisfaction, and the effect of perceived learning on satisfaction. These findings provide new insights for the influential mechanism of flow experiences and will assist EFL learners in optimizing their learning outcomes in digital game-based vocabulary learning.

1. Introduction

Learning vocabulary knowledge has attracted significant attention in the second language (L2) context over the last decade due to its significant role in enhancing language ability (Stewart et al., 2024; Teng & Zhang, 2024; Uchihara & Clenton, 2023). However, enhancing vocabulary breadth and depth poses challenges for both teaching and learning experiences (Chang & Renandya, 2019; Webb & Nation, 2017), and inadequate vocabulary knowledge remains a common issue among L2 learners (Iravi & Malmir, 2023; Pellicer-Sánchez et al., 2022). The reason for this is that vocabulary knowledge is a complex construct involving multilayered cognitive processes, and the retention of vocabulary is not easy and may even decline over time (Nation, 2001; Schmitt, 2014). The retention of vocabulary knowledge requires frequent exposure to and repetition of lexical items, as well as individualized instruction (Cervetti et al., 2023; Chiang et al., 2023; Yeldham, 2023). However, in previous studies, these high-concentration activities were found to be boring or time-consuming, and some L2 learners even showed resistance to them (Nation, 2021; Yamamoto, 2014; Q. Yang et al., 2020). Amid the rapid development of educational technologies that can provide well-perceived, effective, and enjoyable environments for L2 learners, digital game-based vocabulary learning (DGBVL) is increasingly recognized as a potential solution (Foroutan Far & Taghizadeh, 2022; Kazu & Kuvvetli, 2023; Zou et al., 2021).
DGBVL, a form of educational technology combining digital game-based learning (DGBL) with vocabulary learning, has been found to be beneficial for L2 vocabulary knowledge and retention not only in terms of learning outcomes but also learning procedures (Zhang et al., 2023, 2024a). This is because DGBVL can facilitate L2 learners to engage in tasks that require high degrees of involvement (Rasti-Behbahani & Shahbazi, 2022), and it has been shown that it has a positive impact on various aspects of vocabulary learning behavior, learning attitude, motivation, affective schemata, and psychological states (R. Li, 2021; Raeisi-Vanani & Baleghizadeh, 2022; Zhang et al., 2024b). Whilst DGBVL has been widely discussed, more studies are still needed because of the following reasons: Firstly, recent studies focus on games on personal computers (PCs) (Lai & H. Chen, 2023) or websites (Kazu & Kuvvetli, 2023; Soyoof et al., 2024), whilst a limited number focus on those on mobile-assisted equipment. Secondly, most studies on DGBVL are reviews (H. Hung et al., 2018; Peterson, 2023; Vnucko & Klimova, 2023; Zou et al., 2021) or meta-analyses (M. Chen et al., 2018; Tsai & Tsai, 2018) rather than empirical studies, and even empirical studies focus on effectiveness (H. Chen & Hsu, 2020), attitudes (Gao & Pan, 2023), or young children (Hwang & Wang, 2016) rather than on the process of adult learners.
Therefore, it is necessary to explore DGBVL’s influential mechanism in the vocabulary learning process of adult learners, as its psychological, cognitive, and behavioral procedures are multilayered and interact frequently (M. Chen et al., 2018; Rasti-Behbahani & Shahbazi, 2022; Tsai & Tsai, 2018). Flow theory, a psychological framework regarding learners’ immersion when engaging in learning activities, has recently attracted attention in this regard. This is because it aligns with the complex learning process of DGBVL, which encompasses the effects of learner-related and contextual factors and can be used to enhance the understanding of DGBVL’s influential mechanism (H. Chen & Hsu, 2020; Gao & Pan, 2023; Tai et al., 2022). R. Li et al. (2021) developed measurement and structural models that describe EFL learners’ flow experiences in DGBVL. However, these models focus primarily on one type of learning application platform, and the factors that affect flow experiences remain relatively exclusive; therefore, a broader investigation of the topic is necessary. This study was therefore designed with two main aims: to validate whether the previously proposed models can be used in a broader application context and to investigate how usage frequency impacts EFL learners’ flow experiences in DGBVL.

2. Literature Review

2.1. Digital Game-Based Vocabulary Learning

Digital game-based vocabulary learning (DGVBL) is one branch of digital game-based learning (DGBL) in the field of vocabulary learning. DGBL refers to a digital activity involving play that contains educational objectives and assessments (H. Hung et al., 2018). Two bases of DGBL are digital learning and game-based learning. Digital learning breaks away from the time and space limitations of traditional teacher-based learning, and instead, it emphasizes learning everywhere via diversified learning forms that satisfy learners’ personal requirements (Tsai & Tsai, 2018). As for game-based learning, it can be understood as applying the concept of gamification to learning. Gamification is defined as using game mechanics, esthetic theories, and game minds to attract and encourage people to learn and solve problems.
DGBVL integrates several game elements, such as goals, competition rules, time, rewards, and feedback, into vocabulary learning (Buil et al., 2018, 2019). Through these means, learners’ motivations for, attitudes toward, satisfaction with, and involvement in vocabulary learning and their learning outcomes can all be improved (Buil et al., 2018). Many studies in this field have shown the positive influence of DGBVL on general and professional vocabulary learning. However, no consensus on the types of digital gamification learning has been reached. Digital games can be classified as simulation games, adventure games, role-playing games, and strategy games (Boyle et al., 2016). Another classification includes eight types: immersive games, tutorial games, exergames, simulation games, adventure games, music games, board games, and alternate-reality games (H. Hung et al., 2018). Among these eight types, immersive games and tutorial games are the top two common types. Tutorial games are defined as “games that include an identifiable teaching presence for improving learning through drill and practice, question and answer, quizzes or puzzles” (H. Hung et al., 2018, p. 96).
One representative study in this field was conducted by Tsai and Tsai (2018), who collected 26 empirical studies and conducted a meta-analysis on them. Based on the four conditions of Mayer’s (2015) taxonomy of research designs for DGBL, experimental and control groups were set. These groups were compared in different contexts of several video games, and it was found that playing digital games benefits learners’ learning effect. Another meta-analysis of DGBVL was conducted by M. Chen et al. (2018), who adopted Csikszentmihalyi’s (1991) flow theory and explored the effectiveness of DGBVL in 10 studies. A large effect size was found concerning the pooled effect of DGBVL, with game design being the only significant moderator that could account for the effects of DGVL, as game-related factors were formally linked to the level of game challenge (M. Chen et al., 2018). These studies support the use and importance of DGBVL in vocabulary learning.

2.2. Digital Game-Based Vocabulary Learning and Flow Theory

Flow experience, a key notion of flow theory, can be viewed as the optimal status of experience, describing a mental state where the brain is completely occupied with an energetic sense of participation and the enjoyment that comes with engaging in an activity (Csikszentmihalyi, 1975). It pertains to the distinctive occurrence of intense concentration on a task, with individuals experiencing a sense of time distortion, pleasure, and other related sensations. Nine characteristics of flow experiences have been identified: clear goals, immediate feedback, personal skills well suited to given challenges, the merging of action and awareness, concentration on the task at hand, a sense of potential control, a loss of self-consciousness, an altered sense of time, and an experience that becomes autotelic (Csikszentmihalyi, 1975). These nine characteristics have been grouped into three stages comprising the antecedents of flow, the experience of flow, and the effects of flow (G. Chen et al., 1999). Flow experiences have been found to positively impact perceived learning and satisfaction, which further reflects its positive impact on learning outcomes (Bashori et al., 2022; Buil et al., 2018; C. Hung et al., 2015). The flow process has various factors. Shin (2006) and Joo et al. (2011) found that concentration and intrinsic motivation both had a significant impact on satisfaction. Buil et al. (2018) and Kamis et al. (2010) found that enjoyment was highly related to satisfaction; that is, enjoyment had a positive and direct impact on satisfaction. Both learner factors and contextual factors have a positive and direct influence on flow experiences (Buil et al., 2018, 2019; Kiili et al., 2014).
In a study conducted by R. Li et al. (2021), flow theory and DGBVL were combined, with the aim of identifying the influential mechanism of the vocabulary learning process. The study examined in what ways flow experiences impact the learning outcomes of DGBVL learners using Baicizhan, a widely used Chinese DGBVL application that can be installed on digital devices. A model describing the productive process of flow experiences in DGBVL was proposed. It comprises nine constructs, and it was designed following the three flow stages. In the model, four aspects are classified into learner factors and contextual factors. Endogenous learner factors include the balance of the learners’ skill and the learning challenge and a clear goal. Contextual factors include feedback and playability, referring to users’ feelings and game design, stimulation, and interest in the process of DGBVL provided by digital platforms such as vocabulary learning applications or websites. A seven-point Likert survey was developed based on the above model, fully revealing the factors influencing Chinese EFL learners’ learning outcomes. It was validated through structural equation modeling (SEM) based on data from Chinese EFL learners who had used a popular and specific Chinese application designed for English vocabulary learning and featuring digital games. R. Li et al.’s (2021) study revealed that EFL learners’ learning outcomes are positively and directly affected by concentration, intrinsic motivation, and enjoyment. Their research results help to conceptualize how flow experiences function in EFL DGBVL; however, they focused on only one type of DGBVL app, and todays’ situation is significantly different, as more than hundreds of similar apps have been released.

2.3. Usage Frequency and Flow Experiences in Digital Game-Based Vocabulary Learning

The relationship between the frequency of exposure and vocabulary learning has received long-standing attention (Cobb & Laufer, 2021; Dang et al., 2022; Reynolds, 2020). It has been discovered that the more times a word is encountered, the longer and more deeply it is likely to be retained (Pellicer-Sánchez et al., 2021; Yanagisawa & Webb, 2021). This is because vocabulary knowledge is often not acquired automatically but rather requires repeated exposure, practice, and usage (Boers, 2022; Laufer & Rozovski-Roitblat, 2011; Uchihara et al., 2019). With the widespread and deepening of digital learning, discussions on frequency now not only focus on traditional modes such as paper-based reading, classroom instruction, and exercises but also include the frequency of using digital games for learning (Reynolds, 2017). This is because, in addition to DGBL offering interesting and enjoyable learning experiences, repeatedly playing a game also increases exposure (i.e., frequency) to words, thereby facilitating vocabulary acquisition (Soyoof et al., 2024). Previous research indicates that there are significant differences in the usage frequency of digital game language learning and that teachers generally hold a positive attitude towards its integration (Aydin, 2013; Belda-Medina & Calvo-Ferrer, 2022; Blume, 2020). Through a multidimensional exploration of Swedish teenage English learners, Sundqvist and Wikström (2015) found that students with the highest frequency of use of digital vocabulary games had the highest essay scores, advanced vocabulary, and higher overall English grades. Jensen and Cadierno (2022) further explained that the frequency of digital vocabulary game usage can contribute to receptive and productive vocabulary knowledge learning. Therefore, the frequency of using digital game vocabulary learning might have an impact on second language learning and vocabulary acquisition, although the mechanisms of this impact remain underexplored. This study, utilizing the existing flow theory, aims to explore the vocabulary learning process at different frequencies to explain the impact of frequency on learning in EFL DGVBL.

2.4. Research on Chinese EFL Learners

DGBVL has been discussed in Chinese EFL contexts both theoretically and practically. Li (2024) reviewed the theories and pedagogies of four Chinese apps designed for English vocabulary learning and found that the learning strategies, task features, and user interfaces were different, each inspired by their theories and pedagogies. Studies on user perspective suggested that performance goal orientations (J. Yang et al., 2022), self-regulated learning strategies (Y. Yang et al., 2024), and motivation (Zhang et al., 2024b) influence Chinese EFL learners’ vocabulary knowledge when using DGBVL. Zhang et al. (2023) investigated learner engagement in DGBVL and its effect on vocabulary knowledge through eye-tracker experiments, tests, questionnaires, and interviews with 50 Chinese EFL learners. Three dimensions of engagement were involved, namely, behavioral, cognitive, and emotional dimensions, and they had positive effects on vocabulary development, even though their underlying mechanisms differed. Another user-focused study examined the effect of note engagement in DGBVL on vocabulary development, and it found that different types of notes when using DGBVL had different influences on vocabulary knowledge (Zhang et al., 2024a).
In light of the above studies, it is found that DGBVL on different apps has been widely used by and explored in Chinese EFL learners. Focusing on engagement experiences represents a significant research shift in the field of DGBVL; however, a relatively small size of participants was recruited. Therefore, this study aimed to first test the previously proposed model on DGBVL in a broad user app context and then explore the role of usage frequency (an important indicator of engagement).

3. Methodology

3.1. Participants

A total of 306 Chinese EFL university learners were recruited from seven universities in North China. This was a convenience sample, as the participants were selected from English major and minor students who were required to attend vocabulary courses as part of their curriculum and had had DGVBL experience in the 6 months prior to this study (English major students n = 233, 76%; English minor students n = 73, 24%). Vocabulary courses are taught once a week for 2 credit hours at each university, for a total of 16 to 20 weeks, resulting in a minimum of 32 credit hours and a maximum of 40 credit hours. Students selecting these courses had accumulated sufficient experiences in vocabulary learning. The participants consisted of 134 males (43.79%) and 172 females (56.21%), aged from 20 to 26 years (M = 22.07, SD = 1.19). All participants signed an ethical consent form before participating in the survey.

3.2. Instrument

The questionnaire used in this study was developed and verified in the research conducted by R. Li et al. (2021). The questionnaire comprises three sections. The first section is an introduction, which describes the intention of the survey, the usage of the collected results, and the concept of digital game-based vocabulary learning. The second section collects participants’ basic information, including gender, age, major, and university. A separate question corresponds to the usage frequency by asking, “how often do you use digital game-based vocabulary learning?” There were three frequency levels: “seldom” (learners use it less than once a month), “sometimes” (more than once a month but less than once a week), and “always” (more than once a week). The third section comprises 30 questionnaire items, which corresponded to the nine constructs in the model. The specific descriptions of the constructs are provided in Table 1.

3.3. Data Collection and Analysis

SEM, a highly recognized approach used to test validity, analyze paths, and support multi-group comparisons in relevant psychological research (Woody, 2011), was employed in this study to achieve its two main aims: first, in order to examine the reliability and validity of the models in the broader application context, measurement, structural, and mediation model analyses were conducted to test the previously proposed model. Measurement and structural models were used to test composite reliability, measurement validity (i.e., convergent validity and discriminant validity), and structural validity. The total indirect and specific indirect effects of the mediation model were also examined to investigate which mediating paths were significant in this study. Second, in order to explore how usage frequency impacts EFL learners’ flow experiences in DGBVL, multi-group analyses were conducted to explore the differences in the usage frequency of the parameters of the above model.

4. Results

This section begins by reporting the reliability and validation of the previously proposed model for EFL learners utilizing various DGBVL apps. Subsequently, mediation effects within the model are analyzed to further investigate variable intervention, causal relationships, and model fit. Finally, multi-group analyses are conducted to examine potential differences in path coefficients between variables across three distinct frequency groups.

4.1. Measurement Model

The reliability and validity of the DGBVL flow experience instrument were tested, and the results of a confirmatory factor analysis are presented in Table 2 and Figure 1. Specifically, all scales exhibited internal consistency and reliability, with Cronbach’s α and composite reliability (CR) scores above 0.70. Convergent validity was assessed by examining the factor loadings, which ranged from 0.647 to 0.887, and the average variance extracted (AVE) values of each variable, which ranged from 0.537 to 0.641, surpassing the threshold of 0.500. Therefore, the scales demonstrated strong convergent validity.
Discriminant validity is used to assess the degree of difference between latent variables. In this study, the AVE method was used to test the discriminant validity of the model. If the square root of the AVE for a latent variable is greater than the correlation coefficient value between all other variables, then it indicates that the model has good discriminant validity. The discriminant validity analysis results are shown in Table 3. It can be seen that the square root of the AVE for each variable exceeded the correlations between the variable and other variables, indicating that the scale had good discriminant validity.

4.2. Structural Model

The structural model was conducted in two steps. First, the fit of the model to the observed data was tested. Second, the prediction hypotheses established in the previously proposed model were tested. The following indices were employed to assess the structural model fit in this study: the chi-square ratio (χ2/df), root-mean-square error of approximation (RMSEA), goodness-of-fit index (GFI), Tucker–Lewis index (TLI), and comparative fit index (CFI). A good fit to the data is indicated when χ2/df is below 3; the RMSEA is below 0.080; and the GFI, TLI, and CFI are above 0.900 (Hoyle & Panter, 1995; Hu & Bentler, 1999). The previously proposed model fitted well with the observed data among EFL learners utilizing various DGBVL apps in the analysis: χ2(df) = 518.271(380), RMSEA [90% CI] = 0.035 [0.027, 0.042], GFI = 0.902, TLI = 0.964, CFI = 0.969.
The results of the hypotheses are summarized in Figure 2 and Table 4. Regarding the effects of antecedents, BSC positively predicted CON (β = 0.371, p < 0.001), IM (β = 0.237, p = 0.007), and ENJ (β = 0.238, p = 0.007); CG positively predicted CON (β = 0.209, p = 0.020), IM (β = 0.190, p = 0.031), and ENJ (β = 0.365, p < 0.001); FB positively predicted CON (β = 0.167, p = 0.016), IM (β = 0.211, p = 0.002), and ENJ (β = 0.137, p = 0.047); and PB positively predicted CON (β = 0.194, p = 0.013), IM (β = 0.227, p = 0.003), and ENJ (β = 0.219, p = 0.005). These results indicate that the constructs of flow antecedents positively predicted the constructs of flow contents.
Regarding the effects of flow experiences, CON was associated with PL (β = 0.271, p < 0.001) and SAT (β = 0.130, p = 0.036). IM was related to PL (β = 0.180, p = 0.009) and SAT (β = 0.286, p < 0.001). ENJ predicted PL (β = 0.262, p < 0.001) and SAT (β = 0.156, p = 0.008). These results indicate that the constructs of flow contents positively predicted the constructs of flow outcomes. Regarding the effects of outcomes, there was a positive relationship between PL and SAT (β = 0.250, p < 0.001).

4.3. Mediation Model Analysis

A bootstrap procedure utilizing 5000 samples was adopted to test the hypothesized mediation model in accordance with the guidelines outlined by Hayes (2013). Both the total indirect and specific indirect effects of the mediation model are summarized in Table 5. Within this model, there were six mediating paths between BSC and SAT: (1) BSC→CON→PL→SAT (β = 0.022, p = 0.001, 95% CI = [0.007, 0.053]), (2) BSC→CON→SAT (β = 0.042, p = 0.019, 95% CI = [0.006, 0.097]), (3) BSC→IM→PL→SAT (β = 0.009, p = 0.017, 95% CI = [0.001, 0.031]), (4) BSC→IM→SAT (β = 0.059, p = 0.015, 95% CI = [0.013, 0.131]), (5) BSC→ENJ→PL→SAT (β = 0.014, p = 0.009, 95% CI = [0.003, 0.037]), and (6) BSC→ENJ→SAT (β = 0.032, p = 0.018, 95% CI = [0.005, 0.079]). These results indicated that all specific indirect effects between the balance of skill and challenge and satisfaction were significant, and the total direct effect of BSC on SAT was significant (β = 0.179, p = 0.001, 95% CI = [0.079, 0.278]).
Further, there were six mediating paths between CG and SAT: (1) CG→CON→PL→SAT (β = 0.013, p = 0.030, 95% CI = [0.001, 0.040]), (2) CG→CON→SAT (β = 0.025, p = 0.046, 95% CI = [0.000, 0.078]), (3) CG→IM→PL→SAT (β = 0.008, p = 0.045, 95% CI = [0.000, 0.030]), (4) CG→ENJ→PL→SAT (β = 0.022, p < 0.001, 95% CI = [0.008, 0.053]), (5) CG→ENJ→SAT (β = 0.053, p = 0.005, 95% CI = [0.015, 0.116]), and (6)CG→IM→SAT (β = 0.051, p = 0.062, 95% CI = [−0.003, 0.126]). These results indicate that, except for the “CG→IM→SAT” path, all other specific indirect effects between clear goals and satisfaction were significant, and the total direct effect was significant (β = 0.172, p = 0.003, 95% CI = [0.068, 0.278]).
Additionally, there were six mediating paths between FB and SAT: (1) FB→CON→PL→SAT (β = 0.012, p = 0.020, 95% CI = [0.001, 0.034]), (2) FB→CON→SAT (β = 0.023, p = 0.038, 95% CI = [0.001, 0.068]), (3) FB→IM→PL→SAT (β = 0.010, p = 0.011, 95% CI = [0.002, 0.033]), (4) FB→IM→SAT (β = 0.063, p = 0.006, 95% CI = [0.016, 0.138]), (5) FB→ENJ→PL→SAT (β = 0.009, p = 0.026, 95% CI = [0.001, 0.029]), and (6) FB→ENJ→SAT (β = 0.022, p = 0.040, 95% CI = [0.001, 0.067]). These results indicate that all specific indirect effects between feedback and satisfaction were significant, and the total direct effect was significant (β = 0.140, p = 0.002, 95% CI = [0.056, 0.221]).
Finally, there were six mediating paths between PB and SAT: (1) PB→CON→PL→SAT (β = 0.013, p = 0.010, 95% CI = [0.003, 0.034]), (2) PB→CON→SAT (β = 0.025, p = 0.028, 95% CI = [0.002, 0.074]), (3) PB→IM→PL→SAT (β = 0.010, p = 0.012, 95% CI = [0.002, 0.030]), (4) PB→IM→SAT (β = 0.065, p = 0.007, 95% CI = [0.016, 0.133]), (5) PB→ENJ→PL→SAT (β = 0.014, p = 0.001, 95% CI = [0.004, 0.035]), and (6) PB→ENJ→SAT (β = 0.034, p = 0.007, 95% CI = [0.007, 0.082]). These results indicated that all specific indirect effects between playability and satisfaction were significant, and the total direct effect was significant (β = 0.162, p = 0.001, 95% CI = [0.075, 0.252]).

4.4. Multi-Group Analysis

Based on usage frequency, participants were categorized into three groups: “seldom”, “sometimes”, and “always”. Multi-group analyses were conducted to investigate the differences in the usage frequency of the parameters of the previously proposed model. First, an unconstrained model was run, allowing for the parameters to be freely estimated across the three groups. Second, each parameter was constrained one by one to be invariant across groups. Chi-square difference tests between the unconstrained and constrained models were carried out to determine whether there were significant group differences for each parameter. Third, post hoc comparisons were conducted on paths that exhibited significant differences in the chi-square test in order to compare the effect sizes of the three groups on those paths.
The results are presented in Table 6. Specifically, only three paths showed differences among the three groups: “BSC→ENJ” (Δχ2 = 10.414, p = 0.005), “ENJ→SAT” (Δχ2 = 12.238, p = 0.002), and “PL→SAT” (Δχ2 = 16.019, p < 0.001). Furthermore, regarding the predictive effect of BSC on ENJ, the “always” group exhibited a significantly lower positive predictive effect than the other two groups. Specifically, there was no significant effect of BSC on ENJ in the “always” group. Regarding the relationship between ENJ and SAT, the “always” group demonstrated a significantly higher positive predictive effect than the other two groups. Moreover, there was a significant predictive effect of ENJ on SAT in the “always” group, whereas no significant relationship was observed in the “seldom” and “sometimes” groups. Regarding the predictive effect of PL on SAT, the “always” group exhibited a significantly lower positive predictive effect than the “seldom” and “sometimes” groups. Notably, a significant relationship between PL and SAT was observed in the “seldom” and “sometimes” groups, while no significant relationship was found in the “always” group.

5. Discussion

This study aimed to model the influencing factors of EFL learners’ flow experiences in digital game-based vocabulary learning. Specifically, first, this study tested whether the previously proposed model provided by R. Li et al. (2021) is valid in a broader application context, and second, it investigated the effect of usage frequency on EFL learners’ flow experiences in DGBVL.
This study tested the previously proposed model in two main stages. Firstly, it investigated the direct effects among variables. Secondly, it explored the mediation effects within the model. The findings indicated that the previously proposed model fit well with the observed data in the analysis in terms of fit indices and path coefficients. Different from R. Li et al.’s (2021) study, which showed that seven hypothesis paths were not significantly related to each other, all hypothesis path coefficients demonstrated significance among direct predictions. This might be due to the fact that R. Li et al.’s (2021) study only focused on one DGBVL app, whose playability might be simplified due to easy interaction, while this study involved various DGBVL apps that had enriched approaches to playing and learning. This is meaningful because, with the development of technology, the trend in the literature is that DGBVL has been implemented in flexible and various apps in Chinese EFL contexts (C. Li, 2024). Models, hypotheses, or theories need to be generalized because one specific app cannot cater to all learners’ needs and situations. In addition, the indirect effects of the mediation model showed that, except for the “clear goal → intrinsic motivation → satisfaction” path, which was not significant, all other paths were significant. A mediation model analysis was not conducted in R. Li et al.’s (2021) study, perhaps because there were some insignificant hypothesis paths within the model. The validation findings further support the notion that DGBVL can improve EFL learners’ flow experiences, perceived learning, and satisfaction (Feng & Hong, 2022; Foroutan Far & Taghizadeh, 2022; Gao & Pan, 2023; Kazu & Kuvvetli, 2023). The mediation analysis findings suggest that DGBVL’s influential mechanism is a process in which the effects of antecedents on outcomes can be mediated by flow experiences (Foroutan Far & Taghizadeh, 2022; R. Li et al., 2021).
In terms of the impact of usage frequency on flow experiences, the three influencing paths, namely, “the effect of balance of skill and challenge on enjoyment”, “the effect of enjoyment on satisfaction”, and “the effect of perceived learning on satisfaction”, showed differences among the three usage frequency groups (“seldom”, “sometimes”, and “always”). Regarding the paths of “the effect of balance of skill and challenge on enjoyment” and “the effect of perceived learning on satisfaction”, these relationships were not statistically significant in the “always” group but were statistically significant in the “seldom” and “sometimes” groups. The “always” group showed significantly lower prediction than the other two groups, meaning that, for those who use DGBVL more than three times a week, their enjoyment and satisfaction are not influenced by their ability to use smartphones or handle applications. A high usage frequency implies a high level of familiarity with this technology, and as time goes by, the feelings of novelty and curiosity may wear off, resulting in less patience and tolerance for the problems encountered when operating those platforms. Thus, the “always” group might obtain maximum enjoyment and satisfaction through the extensive usage of DGBVL. Conversely, for the path of “enjoyment to satisfaction”, the “always” group showed a difference. This can be explained as such: the more time learners devote to DGBVL, the more enjoyment that they experience, and ultimately, they feel more content with the process and effect. Moreover, for the other paths, according to the multi-group analyses, no significant difference was found, which further indicates that, regardless of how often EFL learners engage in DGBVL, those paths will not be affected. These findings are in line with the fact that usage frequency should be considered in digital game-based learning activities (Belda-Medina & Calvo-Ferrer, 2022; Leung & Choi, 2024; Sundqvist & Wikström, 2015).

6. Conclusions

This study examined the influential mechanism of EFL learners’ flow experiences in digital game-based vocabulary learning by conducting SEM and multi-group analyses on previously proposed measurement and structural models. Regarding the validation analysis, this study found that the proposed models could describe EFL learners’ flow experiences in DGBVL, even in a broad context, but they presented different statistics on the influencing paths. This indicates that the influential mechanism is a process in which the effects of antecedents on outcomes can be mediated by flow experiences. In addition, the role of usage frequency was also explored, and this study found that three paths, namely, “balance of skill and challenge → enjoyment”, “enjoyment → satisfaction”, and “perceived learning → satisfaction”, differed across three usage frequency levels (i.e., seldom, sometimes, and always).
This study provides some insights into how Chinese EFL learners can improve their learning outcomes in digital game-based vocabulary learning. That is, the learning outcomes of using digital game-based vocabulary learning are not limited to traditional English learning methods such as repeatedly reading and reciting vocabulary; they can be enhanced by improving concentration, motivating oneself, and involving oneself with enjoyment in terms of flow experiences. For foreign language teachers, this study offers a student’s perspective on how students engage in vocabulary learning activities, and students’ DGBVL may become more enjoyable and effective by setting clear goals before starting to learn vocabulary digitally, creating certain “no distraction” rules when using DGVBL, rewarding students after completing one-round tasks, and encouraging students to increase behavioral engagement.
Despite its contributions, this study has the following limitations. First, only quantitative data were utilized to study participants’ flow experiences in DGBVL. The experiences self-reported by the participants may not depict all procedures in reality, as they may have failed to recall some details when completing the questionnaire. Second, the subjects were selected from among Chinese undergraduates, who are not the only group using DGBVL. Therefore, although the sample demonstrated representativeness and generalizability statistically, it was still relatively small. Hence, it is recommended that further studies employ mixed methods or use more qualitative data to explore EFL learners’ flow experiences in DGBVL and the reasons why the established relationships were presented in this study. Further studies can also expand the sample, especially by including EFL learners at various educational stages or in different contexts, which will assist in comprehensively understanding the inner influential mechanism of the model on flow experiences in DGBVL.

Author Contributions

Conceptualization, X.W. and L.F.; methodology, X.W. and L.F.; formal analysis, L.F.; writing—original draft preparation, L.F.; writing—review and editing, X.W.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NPU Research Scheme for Education and Teaching Reform, grant number 2024JGWZ09.

Institutional Review Board Statement

This study was conducted based on the guidelines of the Measures for the Ethical Review of Biomedical Research Involving Humans released by the National Health and Family Planning Commission (China), and consent was received from the Academic Committee of the School of Foreign Studies, Northwestern Polytechnical University. Approval sequence no.: Group3No10; approval date: 9 March 2022.

Informed Consent Statement

Informed consent was obtained from all the participants in this study.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Confirmatory factor analysis.
Figure 1. Confirmatory factor analysis.
Education 15 00125 g001
Figure 2. Structural model hypothesized inter-correlations between variables to be tested.
Figure 2. Structural model hypothesized inter-correlations between variables to be tested.
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Table 1. Summary of the DGBVL flow experience instrument.
Table 1. Summary of the DGBVL flow experience instrument.
Flow FactorsConstructsQuestionnaire Items
Flow antecedentsLearner factorsBalance of skill and challenge (BSC)I believed my skills would allow me to meet the challenge in the DGBVL.
I considered the challenge of the DGBVL and my skills to be at an equally high level.
I felt I was competent enough to meet the high challenging demands of the DGBVL.
Clear goal (CG)When I am learning vocabularies with the DGBVL, the goals were clearly defined.
When I am learning vocabularies with the DGBVL, I knew what I had to do.
When I am learning vocabularies with the DGBVL, I knew what I had to achieve.
Contextual factorsFeedback (FB)While I am learning vocabularies with the DGBVL, I receive feedback about the learning progress.
While I am learning vocabularies with the DGBVL, I am notified about the results of my decision-making.
While I am learning vocabularies with the DGBVL, I receive information on my score of performance.
Playability (PB)The rules, goals and design of DGBVL are clear and easy to follow.
The graphic design and interactive scenes of DGBVL are smooth.
The user interface and game control are of high quality.
Flow contentsConcentration (CON)I can completely focus on learning vocabularies with the DGBVL.
My attention was focused entirely on what I was doing with the DGBVL.
When I am learning vocabularies with the DGBVL, I am totally immersed in it and forget everything else around me.
Intrinsic motivation (IM)I would still learn vocabularies with the DGBVL, even if I was not rewarded instantly for it.
I also want to learn vocabularies with the DGBVL in my free time.
I learn vocabularies with the DGBVL because I enjoy it.
I get my motivation from learning vocabularies with the DGBVL, and not from the benefits I can obtain by using it.
Enjoyment (ENJ) Learning vocabularies with the DGBVL gives me a good feeling.
I get a lot of enjoyment from learning vocabularies with the DGBVL.
I feel happy while learning vocabularies with the DGBVL.
I feel cheerful when I learn vocabularies with the DGBVL.
Flow outcomesPerceived learning (PL)The DGBVL was useful for my vocabulary learning.
The DGBVL helped me learn vocabularies well.
The DGBVL facilitated my understanding of vocabulary usages during vocabulary learning.
My stock of vocabularies was enlarged with the use of DGBVL.
Satisfaction (SAT)I found learning vocabularies with the DGBVL valuable.
I was very satisfied with learning vocabularies with the DGBVL.
I had a very positive learning experience during learning vocabularies with the DGBVL.
Note. All items used a 7-point response scale (i.e., 1 = strongly disagree; 7 = strongly agree).
Table 2. Reliability and convergent validity of each construct (N = 306).
Table 2. Reliability and convergent validity of each construct (N = 306).
FactorItemStandard LoadingCRAVECronbach’s α
BSCBSC1
BSC2
BSC3
0.746
0.764
0.748
0.7970.5670.797
CGCG1
CG2
CG3
0.816
0.726
0.647
0.7750.5370.767
FBFB1
FB2
FB3
0.836
0.787
0.735
0.8300.6200.826
PBPB1
PB2
PB3
0.849
0.720
0.802
0.8340.6280.830
CONCON1
CON2
CON3
0.832
0.751
0.817
0.8430.6410.839
IMIM1
IM2
IM3
IM4
0.833
0.736
0.759
0.731
0.8500.5870.846
ENJENJ1
ENJ2
ENJ3
ENJ4
0.887
0.801
0.749
0.739
0.8730.6340.868
SATSAT1
SAT2
SAT3
0.862
0.732
0.727
0.8190.6230.807
PLPL1
PL2
PL3
PL4
0.882
0.775
0.760
0.776
0.8760.6400.873
Table 3. Discriminant validity (N = 306).
Table 3. Discriminant validity (N = 306).
BSCCGFBPBCONIMENJSATPL
BSC0.753
CG0.416 ***0.733
FB0.370 ***0.317 ***0.787
PB0.320 ***0.485 ***0.370 ***0.792
CON0.487 ***0.438 ***0.387 ***0.421 ***0.801
IM0.396 ***0.407 ***0.403 ***0.430 ***0.502 ***0.766
ENJ0.425 ***0.517 ***0.364 ***0.453 ***0.334 ***0.388 ***0.796
SAT0.361 ***0.381 ***0.408 ***0.373 ***0.450 ***0.532 ***0.443 ***0.789
PL0.314 ***0.356 ***0.324 ***0.265 ***0.429 ***0.393 ***0.521 ***0.415 ***0.800
Note: Diagonally arranged values are the square roots of AVE; *** p < 0.001.
Table 4. Results of structural model.
Table 4. Results of structural model.
PathbβSEC.R.p
BSC→CON0.3710.3030.0914.083<0.001
BSC→IM0.2370.1970.0882.6980.007
BSC→ENJ0.2380.1870.0892.6870.007
CG→CON0.2090.1810.0892.3340.020
CG→IM0.1900.1680.0882.1530.031
CG→ENJ0.3650.3060.0913.985<0.001
FB→CON0.1670.1640.0692.4070.016
FB→IM0.2110.2110.0693.0520.002
FB→ENJ0.1370.1300.0691.9870.047
PB→CON0.1940.1810.0782.4770.013
PB→IM0.2270.2160.0782.9250.003
PB→ENJ0.2190.1980.0782.7970.005
CON→PL0.2710.2630.0693.900<0.001
CON→SAT0.1300.1400.0622.1010.036
IM→PL0.1800.1710.0692.6110.009
IM→SAT0.2860.3000.0624.612<0.001
ENJ→PL0.2620.2640.0654.037<0.001
ENJ→SAT0.1560.1730.0582.6720.008
PL→SAT0.2500.2750.0614.089<0.001
Table 5. Summary of indirect effects of mediation model.
Table 5. Summary of indirect effects of mediation model.
βp[95% CI]
Predictor variable: BSC
Total indirect0.1790.001[0.079, 0.278]
BSC→CON→PL→SAT0.0220.001[0.007, 0.053]
BSC→CON→SAT0.0420.019[0.006, 0.097]
BSC→IM→PL→SAT0.0090.017[0.001, 0.031]
BSC→IM→SAT0.0590.015[0.013, 0.131]
BSC→ENJ→PL→SAT0.0140.009[0.003, 0.037]
BSC→ENJ→SAT0.0320.018[0.005, 0.079]
Predictor variable: CG
Total indirect0.1720.003[0.068, 0.278]
CG→CON→PL→SAT0.0130.030[0.001, 0.040]
CG→CON→SAT0.0250.046[0.000, 0.078]
CG→IM→PL→SAT0.0080.045[0.000, 0.030]
CG→IM→SAT0.0510.062[−0.003, 0.126]
CG→ENJ→PL→SAT0.022<0.001[0.008, 0.053]
CG→ENJ→SAT0.0530.005[0.015, 0.116]
Predictor variable: FB
Total indirect0.1400.002[0.056, 0.221]
FB→CON→PL→SAT0.0120.020[0.001, 0.034]
FB→CON→SAT0.0230.038[0.001, 0.068]
FB→IM→PL→SAT0.0100.011[0.002, 0.033]
FB→IM→SAT0.0630.006[0.016, 0.138]
FB→ENJ→PL→SAT0.0090.026[0.001, 0.029]
FB→ENJ→SAT0.0220.040[0.001, 0.067]
Predictor variable: PB
Total indirect0.1620.001[0.075, 0.252]
PB→CON→PL→SAT0.0130.010[0.003, 0.034]
PB→CON→SAT0.0250.028[0.002, 0.074]
PB→IM→PL→SAT0.0100.012[0.002, 0.030]
PB→IM→SAT0.0650.007[0.016, 0.133]
PB→ENJ→PL→SAT0.0140.001[0.004, 0.035]
PB→ENJ→SAT0.0340.007[0.007, 0.082]
Table 6. Results from multi-group analysis testing for group differences.
Table 6. Results from multi-group analysis testing for group differences.
Path Bχ2Δχ2p Value for Δχ2Post Hoc Comparison
1 Seldom
(N = 78)
2 Sometimes
(N = 103)
3 Always
(N = 125)
BSC→CON
bs equal for all0.402 ***0.402 ***0.402 ***1374.8592.9910.224-
bs free to differ0.764 *0.477 **0.2451371.868
BSC→IM
bs equal for all0.225 *0.225 *0.225 *1373.6731.8050.406-
bs free to differ0.3640.0730.319 *1371.868
BSC→ENJ
bs equal for all0.312 ***0.312 ***0.312 ***1382.28210.414 **0.0051 = 2 > 3
bs free to differ0.5140.734 ***0.1041371.868
CG→CON
bs equal for all0.184 *0.184 *0.184 *1376.2754.4070.110-
bs free to differ0.1200.0060.424 **1371.868
CG→IM
bs equal for all0.197 *0.197 *0.197 *1373.3301.4620.481-
bs free to differ0.2180.0890.328 *1371.868
CG→ENJ
bs equal for all0.307 ***0.307 ***0.307 ***1375.1073.2380.198-
bs free to differ0.384 *0.0650.400 ***1371.868
FB→CON
bs equal for all0.153 *0.153 *0.153 *1372.7750.9070.635-
bs free to differ−0.0180.1860.1771371.868
FB→IM
bs equal for all0.230 ***0.230 ***0.230 ***1376.4654.5970.100-
bs free to differ−0.0680.407 **0.1851371.868
FB→ENJ
bs equal for all0.1090.1090.1091371.9930.1250.939-
bs free to differ0.0570.1400.1081371.868
PB→CON
bs equal for all0.220 **0.220 **0.220 **1376.1464.2780.118-
bs free to differ0.1030.416 **0.0591371.868
PB→IM
bs equal for all0.204 **0.204 **0.204 **1372.7710.9030.637-
bs free to differ0.312 *0.2010.1121371.868
PB→ENJ
bs equal for all0.184 *0.184 *0.184 *1374.4442.5760.276-
bs free to differ0.0760.354 **0.1191371.868
CON→PL
bs equal for all0.252 *0.252 *0.252 *1376.6504.7820.092-
bs free to differ0.302 **0.0080.396 ***1371.868
CON→SAT
bs equal for all0.141 *0.141 *0.141 *1375.3503.4820.175-
bs free to differ−0.0060.1180.304 **1371.868
IM→SAT
bs equal for all0.316 ***0.316 ***0.316 ***1374.5632.6950.260-
bs free to differ0.253 **0.477 ***0.236 *1371.868
IM→PL
bs equal for all0.142 *0.142 *0.142 *1373.6731.2680.530-
bs free to differ0.0300.2200.1901371.868
ENJ→PL
bs equal for all0.307 ***0.307 ***0.307 ***1374.3562.4870.288-
bs free to differ0.1700.431 ***0.2571371.868
ENJ→SAT
bs equal for all0.095 *0.095 *0.095 *1384.10612.238 **0.0021 = 2 < 3
bs free to differ0.079−0.0890.469 ***1371.868
PL→SAT
bs equal for all0.298 ***0.298 ***0.298 ***1387.88716.019 ***<0.0011 = 2 > 3
bs free to differ0.392 **0.517 ***−0.0341371.868
Note: * p < 0.05, ** p < 0.01, *** p < 0.001.
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Wang, X.; Feng, L. Examining the Influential Mechanism of English as a Foreign Language (EFL) Learners’ Flow Experiences in Digital Game-Based Vocabulary Learning: Shedding New Light on a Priori Proposed Model. Educ. Sci. 2025, 15, 125. https://doi.org/10.3390/educsci15020125

AMA Style

Wang X, Feng L. Examining the Influential Mechanism of English as a Foreign Language (EFL) Learners’ Flow Experiences in Digital Game-Based Vocabulary Learning: Shedding New Light on a Priori Proposed Model. Education Sciences. 2025; 15(2):125. https://doi.org/10.3390/educsci15020125

Chicago/Turabian Style

Wang, Xuan, and Linfei Feng. 2025. "Examining the Influential Mechanism of English as a Foreign Language (EFL) Learners’ Flow Experiences in Digital Game-Based Vocabulary Learning: Shedding New Light on a Priori Proposed Model" Education Sciences 15, no. 2: 125. https://doi.org/10.3390/educsci15020125

APA Style

Wang, X., & Feng, L. (2025). Examining the Influential Mechanism of English as a Foreign Language (EFL) Learners’ Flow Experiences in Digital Game-Based Vocabulary Learning: Shedding New Light on a Priori Proposed Model. Education Sciences, 15(2), 125. https://doi.org/10.3390/educsci15020125

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