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
Abstract
:1. Introduction
2. Literature Review
2.1. Digital Game-Based Vocabulary Learning
2.2. Digital Game-Based Vocabulary Learning and Flow Theory
2.3. Usage Frequency and Flow Experiences in Digital Game-Based Vocabulary Learning
2.4. Research on Chinese EFL Learners
3. Methodology
3.1. Participants
3.2. Instrument
3.3. Data Collection and Analysis
4. Results
4.1. Measurement Model
4.2. Structural Model
4.3. Mediation Model Analysis
4.4. Multi-Group Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Flow Factors | Constructs | Questionnaire Items | |
---|---|---|---|
Flow antecedents | Learner factors | Balance 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 factors | Feedback (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 contents | Concentration (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 outcomes | Perceived 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. |
Factor | Item | Standard Loading | CR | AVE | Cronbach’s α |
---|---|---|---|---|---|
BSC | BSC1 BSC2 BSC3 | 0.746 0.764 0.748 | 0.797 | 0.567 | 0.797 |
CG | CG1 CG2 CG3 | 0.816 0.726 0.647 | 0.775 | 0.537 | 0.767 |
FB | FB1 FB2 FB3 | 0.836 0.787 0.735 | 0.830 | 0.620 | 0.826 |
PB | PB1 PB2 PB3 | 0.849 0.720 0.802 | 0.834 | 0.628 | 0.830 |
CON | CON1 CON2 CON3 | 0.832 0.751 0.817 | 0.843 | 0.641 | 0.839 |
IM | IM1 IM2 IM3 IM4 | 0.833 0.736 0.759 0.731 | 0.850 | 0.587 | 0.846 |
ENJ | ENJ1 ENJ2 ENJ3 ENJ4 | 0.887 0.801 0.749 0.739 | 0.873 | 0.634 | 0.868 |
SAT | SAT1 SAT2 SAT3 | 0.862 0.732 0.727 | 0.819 | 0.623 | 0.807 |
PL | PL1 PL2 PL3 PL4 | 0.882 0.775 0.760 0.776 | 0.876 | 0.640 | 0.873 |
BSC | CG | FB | PB | CON | IM | ENJ | SAT | PL | |
---|---|---|---|---|---|---|---|---|---|
BSC | 0.753 | ||||||||
CG | 0.416 *** | 0.733 | |||||||
FB | 0.370 *** | 0.317 *** | 0.787 | ||||||
PB | 0.320 *** | 0.485 *** | 0.370 *** | 0.792 | |||||
CON | 0.487 *** | 0.438 *** | 0.387 *** | 0.421 *** | 0.801 | ||||
IM | 0.396 *** | 0.407 *** | 0.403 *** | 0.430 *** | 0.502 *** | 0.766 | |||
ENJ | 0.425 *** | 0.517 *** | 0.364 *** | 0.453 *** | 0.334 *** | 0.388 *** | 0.796 | ||
SAT | 0.361 *** | 0.381 *** | 0.408 *** | 0.373 *** | 0.450 *** | 0.532 *** | 0.443 *** | 0.789 | |
PL | 0.314 *** | 0.356 *** | 0.324 *** | 0.265 *** | 0.429 *** | 0.393 *** | 0.521 *** | 0.415 *** | 0.800 |
Path | b | β | SE | C.R. | p |
---|---|---|---|---|---|
BSC→CON | 0.371 | 0.303 | 0.091 | 4.083 | <0.001 |
BSC→IM | 0.237 | 0.197 | 0.088 | 2.698 | 0.007 |
BSC→ENJ | 0.238 | 0.187 | 0.089 | 2.687 | 0.007 |
CG→CON | 0.209 | 0.181 | 0.089 | 2.334 | 0.020 |
CG→IM | 0.190 | 0.168 | 0.088 | 2.153 | 0.031 |
CG→ENJ | 0.365 | 0.306 | 0.091 | 3.985 | <0.001 |
FB→CON | 0.167 | 0.164 | 0.069 | 2.407 | 0.016 |
FB→IM | 0.211 | 0.211 | 0.069 | 3.052 | 0.002 |
FB→ENJ | 0.137 | 0.130 | 0.069 | 1.987 | 0.047 |
PB→CON | 0.194 | 0.181 | 0.078 | 2.477 | 0.013 |
PB→IM | 0.227 | 0.216 | 0.078 | 2.925 | 0.003 |
PB→ENJ | 0.219 | 0.198 | 0.078 | 2.797 | 0.005 |
CON→PL | 0.271 | 0.263 | 0.069 | 3.900 | <0.001 |
CON→SAT | 0.130 | 0.140 | 0.062 | 2.101 | 0.036 |
IM→PL | 0.180 | 0.171 | 0.069 | 2.611 | 0.009 |
IM→SAT | 0.286 | 0.300 | 0.062 | 4.612 | <0.001 |
ENJ→PL | 0.262 | 0.264 | 0.065 | 4.037 | <0.001 |
ENJ→SAT | 0.156 | 0.173 | 0.058 | 2.672 | 0.008 |
PL→SAT | 0.250 | 0.275 | 0.061 | 4.089 | <0.001 |
β | p | [95% CI] | |
---|---|---|---|
Predictor variable: BSC | |||
Total indirect | 0.179 | 0.001 | [0.079, 0.278] |
BSC→CON→PL→SAT | 0.022 | 0.001 | [0.007, 0.053] |
BSC→CON→SAT | 0.042 | 0.019 | [0.006, 0.097] |
BSC→IM→PL→SAT | 0.009 | 0.017 | [0.001, 0.031] |
BSC→IM→SAT | 0.059 | 0.015 | [0.013, 0.131] |
BSC→ENJ→PL→SAT | 0.014 | 0.009 | [0.003, 0.037] |
BSC→ENJ→SAT | 0.032 | 0.018 | [0.005, 0.079] |
Predictor variable: CG | |||
Total indirect | 0.172 | 0.003 | [0.068, 0.278] |
CG→CON→PL→SAT | 0.013 | 0.030 | [0.001, 0.040] |
CG→CON→SAT | 0.025 | 0.046 | [0.000, 0.078] |
CG→IM→PL→SAT | 0.008 | 0.045 | [0.000, 0.030] |
CG→IM→SAT | 0.051 | 0.062 | [−0.003, 0.126] |
CG→ENJ→PL→SAT | 0.022 | <0.001 | [0.008, 0.053] |
CG→ENJ→SAT | 0.053 | 0.005 | [0.015, 0.116] |
Predictor variable: FB | |||
Total indirect | 0.140 | 0.002 | [0.056, 0.221] |
FB→CON→PL→SAT | 0.012 | 0.020 | [0.001, 0.034] |
FB→CON→SAT | 0.023 | 0.038 | [0.001, 0.068] |
FB→IM→PL→SAT | 0.010 | 0.011 | [0.002, 0.033] |
FB→IM→SAT | 0.063 | 0.006 | [0.016, 0.138] |
FB→ENJ→PL→SAT | 0.009 | 0.026 | [0.001, 0.029] |
FB→ENJ→SAT | 0.022 | 0.040 | [0.001, 0.067] |
Predictor variable: PB | |||
Total indirect | 0.162 | 0.001 | [0.075, 0.252] |
PB→CON→PL→SAT | 0.013 | 0.010 | [0.003, 0.034] |
PB→CON→SAT | 0.025 | 0.028 | [0.002, 0.074] |
PB→IM→PL→SAT | 0.010 | 0.012 | [0.002, 0.030] |
PB→IM→SAT | 0.065 | 0.007 | [0.016, 0.133] |
PB→ENJ→PL→SAT | 0.014 | 0.001 | [0.004, 0.035] |
PB→ENJ→SAT | 0.034 | 0.007 | [0.007, 0.082] |
Path | B | χ2 | Δχ2 | p Value for Δχ2 | Post Hoc Comparison | ||
---|---|---|---|---|---|---|---|
1 Seldom (N = 78) | 2 Sometimes (N = 103) | 3 Always (N = 125) | |||||
BSC→CON | |||||||
bs equal for all | 0.402 *** | 0.402 *** | 0.402 *** | 1374.859 | 2.991 | 0.224 | - |
bs free to differ | 0.764 * | 0.477 ** | 0.245 | 1371.868 | |||
BSC→IM | |||||||
bs equal for all | 0.225 * | 0.225 * | 0.225 * | 1373.673 | 1.805 | 0.406 | - |
bs free to differ | 0.364 | 0.073 | 0.319 * | 1371.868 | |||
BSC→ENJ | |||||||
bs equal for all | 0.312 *** | 0.312 *** | 0.312 *** | 1382.282 | 10.414 ** | 0.005 | 1 = 2 > 3 |
bs free to differ | 0.514 | 0.734 *** | 0.104 | 1371.868 | |||
CG→CON | |||||||
bs equal for all | 0.184 * | 0.184 * | 0.184 * | 1376.275 | 4.407 | 0.110 | - |
bs free to differ | 0.120 | 0.006 | 0.424 ** | 1371.868 | |||
CG→IM | |||||||
bs equal for all | 0.197 * | 0.197 * | 0.197 * | 1373.330 | 1.462 | 0.481 | - |
bs free to differ | 0.218 | 0.089 | 0.328 * | 1371.868 | |||
CG→ENJ | |||||||
bs equal for all | 0.307 *** | 0.307 *** | 0.307 *** | 1375.107 | 3.238 | 0.198 | - |
bs free to differ | 0.384 * | 0.065 | 0.400 *** | 1371.868 | |||
FB→CON | |||||||
bs equal for all | 0.153 * | 0.153 * | 0.153 * | 1372.775 | 0.907 | 0.635 | - |
bs free to differ | −0.018 | 0.186 | 0.177 | 1371.868 | |||
FB→IM | |||||||
bs equal for all | 0.230 *** | 0.230 *** | 0.230 *** | 1376.465 | 4.597 | 0.100 | - |
bs free to differ | −0.068 | 0.407 ** | 0.185 | 1371.868 | |||
FB→ENJ | |||||||
bs equal for all | 0.109 | 0.109 | 0.109 | 1371.993 | 0.125 | 0.939 | - |
bs free to differ | 0.057 | 0.140 | 0.108 | 1371.868 | |||
PB→CON | |||||||
bs equal for all | 0.220 ** | 0.220 ** | 0.220 ** | 1376.146 | 4.278 | 0.118 | - |
bs free to differ | 0.103 | 0.416 ** | 0.059 | 1371.868 | |||
PB→IM | |||||||
bs equal for all | 0.204 ** | 0.204 ** | 0.204 ** | 1372.771 | 0.903 | 0.637 | - |
bs free to differ | 0.312 * | 0.201 | 0.112 | 1371.868 | |||
PB→ENJ | |||||||
bs equal for all | 0.184 * | 0.184 * | 0.184 * | 1374.444 | 2.576 | 0.276 | - |
bs free to differ | 0.076 | 0.354 ** | 0.119 | 1371.868 | |||
CON→PL | |||||||
bs equal for all | 0.252 * | 0.252 * | 0.252 * | 1376.650 | 4.782 | 0.092 | - |
bs free to differ | 0.302 ** | 0.008 | 0.396 *** | 1371.868 | |||
CON→SAT | |||||||
bs equal for all | 0.141 * | 0.141 * | 0.141 * | 1375.350 | 3.482 | 0.175 | - |
bs free to differ | −0.006 | 0.118 | 0.304 ** | 1371.868 | |||
IM→SAT | |||||||
bs equal for all | 0.316 *** | 0.316 *** | 0.316 *** | 1374.563 | 2.695 | 0.260 | - |
bs free to differ | 0.253 ** | 0.477 *** | 0.236 * | 1371.868 | |||
IM→PL | |||||||
bs equal for all | 0.142 * | 0.142 * | 0.142 * | 1373.673 | 1.268 | 0.530 | - |
bs free to differ | 0.030 | 0.220 | 0.190 | 1371.868 | |||
ENJ→PL | |||||||
bs equal for all | 0.307 *** | 0.307 *** | 0.307 *** | 1374.356 | 2.487 | 0.288 | - |
bs free to differ | 0.170 | 0.431 *** | 0.257 | 1371.868 | |||
ENJ→SAT | |||||||
bs equal for all | 0.095 * | 0.095 * | 0.095 * | 1384.106 | 12.238 ** | 0.002 | 1 = 2 < 3 |
bs free to differ | 0.079 | −0.089 | 0.469 *** | 1371.868 | |||
PL→SAT | |||||||
bs equal for all | 0.298 *** | 0.298 *** | 0.298 *** | 1387.887 | 16.019 *** | <0.001 | 1 = 2 > 3 |
bs free to differ | 0.392 ** | 0.517 *** | −0.034 | 1371.868 |
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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
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 StyleWang, 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 StyleWang, 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