The Potential of a FrameVR.io Metaverse Environment to Enhance Cognitive Engagement, Reduce Digital Burnout, and Develop Creative Writing Skills

The Potential of a FrameVR.io Metaverse Environment to Enhance Cognitive Engagement, Reduce Digital Burnout, and Develop Creative Writing Skills | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Potential of a FrameVR.io Metaverse Environment to Enhance Cognitive Engagement, Reduce Digital Burnout, and Develop Creative Writing Skills Mohamed Mekheimer, Zeinab Amin, Mohamed Hasan Khalaf, Eman Mahdy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8831860/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Drawing on a quasi-experimental pretest–posttest design with intact undergraduate EFL classes (enrolled N = 73; complete-case N = 70; 35 FrameVR.io/metaverse, 35 control), this study examined whether a FrameVR.io browser-based social 3D creative-writing intervention enhances cognitive engagement and creative writing performance while reducing digital burnout. Over six weeks (90 minutes/week), both groups addressed comparable creative-writing objectives, differing primarily in learning ecology (FrameVR.io immersive, avatar-mediated collaboration vs. conventional classroom tasks). Primary effects were estimated using ANCOVA models predicting each posttest outcome from group while adjusting for the corresponding pretest score; heteroscedasticity-robust (HC3) standard errors were used, and Holm-adjusted p values controlled familywise error across the three confirmatory outcomes. Adjusted posttest means favored the FrameVR.io group for cognitive engagement (Δ_adj = 28.34, p < .001, partial η² = .928) and creative writing performance (Δ_adj = 8.23, p < .001, partial η² = .866), and indicated lower digital burnout (Δ_adj = − 24.52, p < .001, partial η² = .773). Creative-writing scripts were scored by trained independent raters blinded to group and timepoint, strengthening internal validity for performance assessment. Semi-structured interviews with metaverse participants (n = 18) converged with quantitative results, attributing benefits to immersion, collaboration, and idea visualization while noting intermittent technical friction. Interpretation is tempered by restricted score variability in some posttest measures and the nonrandomized intact-class design; replication with delayed posttests, item-level validation, and exposure metrics (e.g., time-on-task) is recommended. metaverse FrameVR.io EFL cognitive engagement digital burnout creative writing Figures Figure 1 Figure 2 1. Introduction Proficient English creative writing is increasingly essential for academic success, professional mobility, and nuanced intercultural communication (Andrews & Smith, 2011; Kellogg, 2018). Yet, in many undergraduate EFL contexts, instructional conditions (e.g., large classes, limited individualized feedback, and a product-over-process emphasis) can constrain opportunities for sustained practice, iterative revision, and authentic audience-oriented writing—conditions that are central to developing high-level creative writing. These constraints are closely tied to cognitive engagement, which in creative writing entails more than participation: it includes sustained attentional focus on craft, metacognitive regulation of planning–drafting–revising, active participation in compositional tasks, willingness to attempt creative challenge, and persistent effort investment in improving written output. At the same time, digitally mediated learning has expanded rapidly, creating new opportunities alongside new risks. One prominent risk is digital burnout, commonly reflected in emotional exhaustion, cynicism, and reduced perceived academic efficacy linked to prolonged, demanding, or poorly structured digital interaction (Maslach & Leiter, 2011; Salmela-Aro et al., 2017). For cognitively demanding tasks such as creative writing—which require sustained ideation, linguistic monitoring, and revision—digital burnout may reduce persistence, constrain willingness to attempt challenging writing moves, and undermine perceived competence. Against this background, the metaverse—understood here as a persistent, shared 3D social environment enabling embodied interaction through avatars and collaborative creation—has been proposed as a pedagogical frontier with potential to increase engagement and enrich task authenticity. Importantly, many educational studies use “metaverse” loosely to refer to general VR/3D tools. In this study, the term is used narrowly and operationally: the intervention was implemented in FrameVR.io, a browser-based social 3D environment that supports synchronous collaboration, avatar interaction, and shared virtual workspaces. This is pedagogically relevant to creative writing because it allows learners to (a) inhabit richly cued settings that stimulate description, (b) role-play character interactions via avatars, and (c) co-construct plots using shared whiteboards and artifacts—affordances that align directly with the cognitive and social processes involved in narrative composition. The study therefore investigates whether a FrameVR.io-based creative-writing design can simultaneously (i) enhance cognitive engagement, (ii) reduce digital burnout, and (iii) improve creative writing performance—while documenting implementation fidelity and scoring integrity in ways that meet contemporary transparency standards. 1.1. Research gaps and problem statement Despite growing interest in metaverse-enabled learning, several gaps remain—particularly in EFL creative writing: Engagement specificity (granularity). Many studies report “engagement” broadly without testing domain-relevant dimensions of cognitive engagement in writing (e.g., attention allocation, self-regulation during composing, and sustained effort investment). More fine-grained evidence is needed to determine which engagement facets are most influenced by immersive, collaborative 3D learning. Skill transfer to creative writing outcomes. Empirical work often emphasizes attitudes, satisfaction, or general language outcomes. There is comparatively less evidence linking metaverse-supported engagement to measurable improvements in creative writing using rubric-based performance assessment. Digital burnout as an outcome. Research rarely tests whether immersive, agentic collaboration can reduce digital burnout—or whether such environments introduce new strain (e.g., cognitive overload, technostress). This question is especially consequential for writing tasks that demand sustained cognitive effort. Design transparency and methodological rigor. Reviewers increasingly expect (a) consistent, prespecified analyses (e.g., ANCOVA for baseline adjustment), (b) clear participant accounting (enrollment vs complete-case), (c) blinded scoring where outcomes are rater-scored, (d) multiplicity control across multiple outcomes, and (e) implementation evidence (fidelity logs, participation logs, and screenshots). Problem statement. The field lacks sufficiently rigorous, transparent evidence on whether a FrameVR.io-based metaverse creative-writing design can enhance cognitive engagement, reduce digital burnout, and improve creative writing performance among university EFL learners, and through which proximal mechanisms these effects plausibly operate. 1.2. Research questions RQ1. To what extent does participation in FrameVR.io-based creative writing influence students’ overall cognitive engagement and its dimensions (attentional allocation, metacognitive self-regulation, active scholarly participation, propensity for creative challenge, and cognitive effort investment) compared with traditional instruction? RQ2. To what extent does participation in FrameVR.io-based creative writing influence students’ digital burnout compared with traditional instruction? RQ3. What is the impact of FrameVR.io-based creative writing on creative writing performance (originality, coherence, descriptive language, narrative structure) compared with traditional instruction? RQ4 (revised). Within each group, what are the associations between change scores (post–pre) in (a) cognitive engagement dimensions, (b) digital burnout, and (c) creative writing performance? Additionally, do these associations differ by group (interaction/exploratory moderation)? RQ5. Which features or experiences in the FrameVR.io environment do students perceive as most influential (positively or negatively) for engagement, burnout, and writing development? 1.3. Hypotheses (tightened; burnout efficacy direction clarified) H1. Students in the FrameVR.io condition will show higher posttest cognitive engagement (overall and by dimension) than controls, adjusting for pretest scores. H2. Students in the FrameVR.io condition will report lower posttest digital burnout than controls, adjusting for pretest scores (lower exhaustion and cynicism; higher academic efficacy where applicable and correctly scored). H3. Students in the FrameVR.io condition will demonstrate greater improvement in rubric-scored creative writing performance than controls, adjusting for pretest scores. H4a. Within groups, increases in cognitive engagement (post–pre) will be positively associated with gains in creative writing performance. H4b. Within groups, increases in digital burnout (post–pre) will be negatively associated with gains in creative writing performance. 2. Literature Review 2.1. Metaverse-enabled learning: affordances relevant to writing Educational discussions of the metaverse commonly highlight immersion, interactivity, social presence, and shared creation. For writing pedagogy, these affordances matter when they translate into task-relevant cognition (e.g., generating descriptive detail, sustaining attention, and engaging in iterative drafting) and socially mediated composing (e.g., peer feedback, co-construction of narratives). In contrast to isolated VR experiences, a metaverse environment—as operationalized here—emphasizes persistent shared space, synchronous collaboration, and avatar-based social interaction, which may better support peer-supported writing processes. 2.2. Cognitive engagement in immersive and collaborative environments Cognitive engagement involves sustained mental effort, strategic learning, and deep processing—particularly critical in writing, where learners must plan, draft, monitor language, and revise. Immersive environments may influence cognitive engagement by providing (a) contextual cues that support ideation and elaboration, (b) interactive tasks that sustain attention, and (c) collaborative routines that encourage self-regulation and persistent effort. However, evidence remains uneven in terms of whether immersive engagement translates into measurable writing performance , and which engagement dimensions are most responsive to metaverse designs. 2.3. Digital burnout: why immersive learning may reduce—or intensify—it Digital burnout is increasingly recognized as a barrier to sustained learning in technology-mediated contexts. It can arise from prolonged exposure, fragmented attention, poorly structured tasks, and technostress. A metaverse environment might reduce burnout if it shifts learners from passive, fatigue-inducing screen time toward agentic, socially supported, intrinsically motivating activity . Conversely, if immersive environments add complexity or excessive stimulation, they may increase extraneous cognitive load and contribute to exhaustion. This makes burnout a necessary empirical outcome rather than an assumed benefit. 2.4. Creative writing pedagogy in digital contexts Creative writing development depends on iterative composing, strategic revision, feedback, and sustained engagement with craft. Digital contexts can support these processes when they provide structured prompts, authentic audiences, collaboration, and multimodal inspiration. A metaverse environment may offer distinctive support by letting writers inhabit settings , enact character perspective through avatars, and visualize plot/world constraints in shared spaces. Yet, robust evidence remains limited, especially using (a) baseline-adjusted designs, (b) blinded rater-scored outcomes, and (c) transparency artifacts documenting implementation fidelity. Synthesis. The literature supports plausible mechanisms for metaverse-supported engagement and writing development, but it does not yet provide sufficiently rigorous, transparent evidence for whether (and how) metaverse environments can improve EFL creative writing while also reducing digital burnout. This study addresses those gaps using baseline-adjusted analyses, robust inference, and implementation documentation. 3. Theoretical Framework (streamlined so it reads as one integrated model—not “too many theories”) This study adopts an integrated mechanism framework (Fig. 1 ) to explain how a FrameVR.io metaverse intervention may influence cognitive engagement, digital burnout, and creative writing performance. Rather than treating multiple theories as competing explanations, the framework uses them as complementary lenses mapped to distinct parts of the causal chain: Motivational mechanism (SDT + Flow) : explains why immersive, collaborative writing may increase engagement. Demand–resource mechanism (CLT + JD-R) : explains why the same environment may reduce or exacerbate burnout depending on design quality. 3.1. Motivation and engagement mechanisms (SDT and Flow) Self-Determination Theory proposes that autonomy, competence, and relatedness support high-quality engagement and well-being. In FrameVR.io, autonomy can be supported through choices in navigation and narrative development; competence through scaffolded prompts and visible progress; and relatedness through synchronous collaboration and social presence. These conditions are also consistent with Flow Theory, which predicts deep absorption when challenge matches skill, goals are clear, and feedback is timely. The intervention’s weekly design (setting → character → plot/world-building) was structured to progressively increase complexity while maintaining clear task goals, aiming to support sustained attentional allocation and effort investment during composing. 3.2. Load and burnout mechanisms (CLT and JD-R) Cognitive Load Theory distinguishes intrinsic load (inherent difficulty) from extraneous load (imposed by poor design). Metaverse environments can reduce extraneous load when interfaces are intuitive and prompts are well-scaffolded; they can increase it when technical friction, overstimulation, or unclear instructions divert working memory away from writing. The JD-R model complements this by framing burnout as the product of demands (sustained effort + technostress) exceeding resources (peer support, autonomy, feedback, and meaningful task structure). In this study, the metaverse is expected to function as a net resource if the instructional design minimizes extraneous load and supports social and motivational resources; otherwise, it may create additional demand. 3.3. Conceptual framework (Fig. 1 ) and interpretation guidance Figure. A framework linking FrameVR.io creative writing to engagement, burnout, and performance via motivational and demand-resource mechanisms Figure 1 summarizes the study’s integrated theoretical framework as a mechanism-based causal pathway . The model begins with the FrameVR.io metaverse creative writing intervention (input), operationalized through affordances such as immersion, interactivity, social presence, embodied avatar participation, and structured collaborative tasks. These affordances are expected to trigger four complementary mechanisms: (a) Self-Determination Theory (SDT) via greater autonomy, competence, and relatedness; (b) Flow Theory via clear goals, balanced challenge, and immediate feedback that support deep absorption; (c) Cognitive Load Theory (CLT) via reduced extraneous load when the interface and scaffolding are well designed; and (d) the Job Demands–Resources (JD-R) model via a shift in the demands–resources balance, where instructional supports and peer collaboration function as resources that buffer strain. Through these mechanisms, the framework predicts two proximal outcomes . First, the intervention should enhance cognitive engagement , reflected in stronger attentional allocation, metacognitive self-regulation during composing, active scholarly participation, a greater propensity for creative challenge, and higher cognitive effort investment. Second, the intervention should reduce digital burnout , expressed as lower emotional exhaustion and cynicism and, where the scale permits, higher academic efficacy. The framework then specifies a distal pathway: when engagement is strengthened and burnout is reduced, students are expected to show measurable gains in creative writing performance , indexed by originality, coherence, descriptive language, and narrative structure. Crucially, Fig. 1 also highlights instructional design quality as a boundary condition . The metaverse environment is expected to operate as a net academic resource only when task sequencing, scaffolding, and usability minimize extraneous cognitive load and technostress while maximizing meaningful interaction. Under these conditions, the model predicts the strongest improvements in engagement and writing outcomes, alongside the greatest reductions in burnout. 4. Methodology 4.0. Design Overview This study adopted a sequential explanatory mixed-methods design (QUAN → QUAL), in which quantitative effects of the intervention were estimated first and then elaborated through post-intervention interviews (Creswell & Plano Clark, 2018). The quantitative strand used a quasi-experimental pretest–posttest non-equivalent groups design with intact undergraduate EFL classes (metaverse vs. traditional instruction). The qualitative strand used semi-structured interviews with a purposive subsample of metaverse participants to explain how and why the intervention influenced engagement, burnout, and writing outcomes and to document implementation experiences (e.g., affordances, constraints, and technical issues). 4.1. Participants, Setting, and Ethical Compliance Participants were undergraduate EFL students enrolled in the English Department at a Faculty of Education (public university). Two intact classes were allocated to: Experimental (Metaverse/FrameVR.io) : n = 37 at baseline Control (Traditional classroom instruction) : n = 36 at baseline Because the design used intact classes, random assignment was not feasible. Therefore, baseline differences were handled analytically (see Section 4.5 ) rather than treated as “solved” by pretest t -tests. Final analytic sample and participant flow. Of the 73 students who completed baseline measures, 70 provided complete pre–post data on the three primary outcomes and submitted scorable pre/post writing tasks (complete-case dataset: N = 70; 35 metaverse / 35 control). Three cases were excluded from primary analyses due to posttest incompleteness and/or missing scorable outcome artifacts ( see Appendix F: Participant Flow Diagram ). All analyses reported in the Results section are based on this complete-case dataset unless otherwise indicated. Ethics. Institutional approval was obtained from the relevant ethics/IRB committee, and all participants provided informed consent. Participation was voluntary; students could withdraw without penalty. Data were anonymized prior to analysis and reported in aggregate. 4.2. Instructional Design and Intervention Conditions (Six Weeks; 90 Minutes/Week) The intervention was designed to operationalize the affordances in Fig. 1 — immersion, interactivity, social presence, embodiment, and agency —through constructivist and task-based creative writing pedagogy. The experimental condition used FrameVR.io (web-based; no specialized headset required), selected for (a) accessibility, (b) stable avatar-based collaboration, and (c) embedded interactive tools (whiteboards, objects, spatialized prompts). Both groups covered the same curricular writing targets (setting, atmosphere, character development, plot/world-building, descriptive language), with equivalent instructional time (6 × 90 minutes). The difference was delivery mode and task ecology (metaverse tasks vs. conventional classroom tasks). 4.2.1. Experimental Group (FrameVR.io) Weeks 1–2: Setting & Atmosphere (Immersive description). Students navigated thematically rich virtual environments (e.g., “enchanted forest,” “futuristic city”) and produced multi-sensory setting descriptions anchored to spatial cues (visual, spatial relations, object affordances). Weeks 3–4: Character Development (Embodied role-play). Students customized avatars and conducted structured “character interviews” to develop voice, backstory, motivation, and conflict. These tasks operationalized social presence and embodied interaction. Weeks 5–6: Plot & World-Building (Collaborative narrative construction). Small groups co-created narratives using interactive whiteboards and 3D objects to outline plot arcs, place symbolic objects, and draft scenes with real-time peer feedback. The instructor acted as a facilitator (task sequencing, prompts, feedback, and technical support) to reduce extraneous cognitive load and maintain a balance between challenge and skill (Fig. 1 : CLT + Flow conditions). 4.2.2. Control Group (Traditional Classroom) The control class completed the same writing objectives using lectures, textbook-based prompts, individual drafting, paper-based worksheets, and in-class discussion. Writing tasks were completed under standardized classroom conditions without FrameVR.io activities. 4.2.3. Minimal Implementation Evidence Package (for transparency) To address implementation/fidelity expectations for intact-class designs, A participant flow summary is provided in the Appendix to address transparency requirements. Teacher fidelity checklist confirming weekly coverage of the same core components in both groups. Attendance/participation log summarizing session attendance and engagement proxies (e.g., artifacts/posts in FrameVR; handwritten drafts in control). Visual evidence (anonymized screenshots of FrameVR.io and classroom photos) aligned to the six-week sequence. 4.3. Measures and Instruments All quantitative instruments were administered pre- and post-intervention . Students in both groups completed the same survey battery. Writing tasks were collected at both time points under standardized conditions. 4.3.1. Cognitive Engagement in English Creative Writing Cognitive engagement was measured using a multi-dimensional scale aligned to the construct definition in this study (five subdimensions): Attentional Allocation Metacognitive Self-Regulation Active Scholarly Participation Propensity for Creative Challenge Cognitive Effort Investment Scores were computed as (a) subscale scores and (b) an overall engagement composite . Content validity was established via expert review (TESOL and educational technology). Internal consistency was estimated for the overall scale and subscales (reported in Results/Appendix). Item-level evidence: Factor-analytic validation (EFA/CFA) requires item-level response matrices. If item-level responses are available, we will report dimensionality evidence in an expanded supplement; otherwise, dimensionality will be stated as a limitation and treated cautiously. Scoring and range. Cognitive engagement was computed as the sum of 30 Likert-type items, with higher scores indicating greater engagement. The possible total score range is 30–150. Five subscales were computed by summing their respective items; each subscale ranges 6–30. 4.3.2. Digital Burnout Digital burnout was assessed using an adapted student burnout instrument with three subscales: Emotional Exhaustion Cynicism/Detachment Academic Efficacy Scoring and directionality. To interpret burnout in the conventional direction (higher = worse), Academic Efficacy was reverse-keyed when computing a total burnout score . Thus, lower total burnout indicates less burnout . Subscale results were also examined to avoid masking divergent patterns (e.g., efficacy behaving differently). 4.3.3. Creative Writing Skills (Analytic Rubric – Instrument 3) Creative writing performance was scored using a structured analytic rubric assessing: Originality/Creativity Coherence/Narrative organization Descriptive language/Imagery Narrative structure/Plot development Characterization/Voice Mechanics Scoring and range. Creative writing performance was assessed using a rubric comprising seven criteria (Originality, Coherence, Imagery, Structure, Voice, Mechanics, Impact), each rated on a 1–5 scale. Criterion scores were summed to form a total score with a possible range of 7–35, where higher scores indicate stronger creative writing performance. Blinded independent rating. Two trained raters scored scripts independently and were blinded to group allocation and timepoint (pre/post). Blinding was implemented by anonymizing scripts, removing any class identifiers, and randomizing script order prior to scoring; pre/post labels were not provided to raters. Discrepancies were resolved through rubric-guided reconciliation after independent scoring where required (details in Appendix). Inter-rater reliability was evaluated on a randomly selected subset (reported as ICC in Results/Appendix, consistent with the study’s scoring protocol). 4.3.4. Semi-Structured Interview Protocol A semi-structured interview guide was developed to address RQ5, focusing on: Features that increased or decreased engagement (immersion, collaboration, agency, task prompts) Experiences linked to fatigue/relief (burnout-related perceptions) Perceived changes in writing process and outcomes Technical constraints and usability issues Comparative reflections versus conventional instruction Eighteen students from the metaverse group participated (n = 18), selected purposively to reflect variation in participation and outcome change patterns. 4.4. Procedure The study ran over eight weeks : Phase 1: Preparation and training. FrameVR.io spaces and tasks were built and tested. Raters were trained using anchor scripts and a scoring protocol. Instruments and interview prompts were reviewed for clarity. Phase 2: Baseline (Pretest). Both groups completed the engagement and burnout surveys and produced a baseline writing sample under standardized instructions. Phase 3: Intervention (Six weeks). Experimental group engaged in FrameVR.io sessions (90 minutes/week). Control group received conventional instruction with matched content targets and time. Phase 4: Posttest and qualitative follow-up. Immediately after week 6, both groups repeated the same survey battery and completed a post-intervention writing sample. Metaverse interviews were then conducted (recorded with permission, transcribed verbatim, anonymized). Phase 5: Data integrity and dataset finalization. Records were screened for completeness and scorable writing artifacts, yielding the complete-case dataset (N = 70; 35/35). A participant flow summary is provided in Appendix F (Participant Flow Diagram; CONSORT-style) to address transparency requirements. 4.5. Data Analysis Plan (Aligned to the Prespecified Model) To address reviewer concerns about analytic drift and researcher degrees of freedom, the primary analysis followed a prespecified strategy consistent with the Method section and the revised Abstract. 4.5.1. Quantitative Analyses Analyses were conducted in SPSS (v28) and cross-checked in Python. Primary outcomes (confirmatory) : Overall Cognitive Engagement (post) Total Digital Burnout (post; efficacy reverse-keyed) Total Creative Writing Score (post) For each primary outcome, we estimated ANCOVA models : $$\:\text{Posttest}={\beta\:}_{0}+{\beta\:}_{1}\left(\text{Group}\right)+{\beta\:}_{2}\left(\text{Pretest}\right)+\epsilon\:$$ Robust (HC3) standard errors were used to mitigate heteroscedasticity risks. Homogeneity of regression slopes was assessed using a Group × Pretest interaction; if violated, sensitivity models were reported. Results are reported with adjusted posttest means, 95% confidence intervals, and effect sizes (partial η² for ANCOVA; and Hedges’ g for descriptive contrasts where relevant). To control familywise error across the three primary outcomes, p -values were Holm-adjusted. Secondary outcomes (exploratory) : Engagement subdimensions and burnout subscales were analyzed as exploratory, with multiplicity control within each construct family (Holm adjustment within engagement subscales; within burnout subscales), and interpreted cautiously. RQ4 (associations) : To avoid conflating distinct instructional contexts, correlation analyses were computed (a) within the metaverse group using change scores (post − pre) for engagement, burnout, and writing outcomes, and (b) as a sensitivity analysis in the full sample controlling for group (reported in Appendix). Correlations are presented with 95% CIs and clear variable definitions. 4.5.2. Qualitative Analyses Interview transcripts were analyzed using reflexive thematic analysis (Braun & Clarke, 2006), including familiarization, coding, theme development, review, naming, and reporting. Themes were used to explain mechanisms aligned to Fig. 1 (SDT, Flow, CLT, JD-R), including how immersion/collaboration shaped engagement, how technical constraints influenced load/strain, and how participants interpreted “fatigue” versus “energizing” digital experiences. 4.5.3. Mixed-Methods Integration Integration occurred at interpretation: qualitative themes were mapped onto quantitative results to clarify which intervention features plausibly drove observed effects and to document constraints that may limit generalizability (e.g., platform usability, access variability). This integration directly addresses reviewer requests for stronger procedural clarity, implementation evidence, and mechanism-consistent interpretation. 5. Findings 5.1. Quantitative Analysis Data were screened for completeness, plausibility, and cross-instrument consistency prior to inferential testing. Of the 73 students enrolled at baseline, complete and linkable pre/post records were available for 70 (35 per group). This complete-case set (N = 70) was used for the primary ANCOVA models. Unless otherwise specified, tests are two-sided with α = .05; primary-outcome p -values are Holm-adjusted to control familywise error across the three confirmatory outcomes. 5.1.1. Baseline Equivalence and Pretest Comparability Baseline descriptives are reported in Table 1 . The two intact classes were closely comparable on the three pretest totals, with Welch tests indicating no detectable baseline differences (all p ≥ .424). Given the quasi-experimental design, baseline tests are treated as descriptive only; primary inference relies on ANCOVA , which adjusts posttest group differences for pretest performance. Table 1 Baseline Descriptives (Pretest) by Group Variable Control n Control M (SD) Exp. n Exp. M (SD) Mean diff (Ctrl − Exp) 95% CI Welch t(df) p Cognitive Engagement (pre) 35 91.80 (4.29) 35 91.74 (1.46) 0.06 [− 1.49, 1.60] 0.07 (41.8) .941 Digital Burnout (pre) 35 42.31 (1.92) 35 42.71 (2.23) −0.40 [− 1.39, 0.59] −0.80 (66.5) .424 Creative Writing Skills (pre) 35 23.40 (1.65) 35 23.23 (1.52) 0.17 [− 0.58, 0.93] 0.45 (67.5) Note. Values are mean (SD). Mean difference is Control − Experimental. Baseline tests are descriptive; confirmatory inferences rely on ANCOVA-adjusted posttest effects 5.1.2. Primary Treatment Effects (ANCOVA) Primary treatment effects were evaluated using standard ANCOVA models of the form: posttest outcome ~ group + corresponding pretest covariate (Table 2 ). The homogeneity-of-slopes assumption was evaluated via a group × pretest interaction term; interactions were non-significant for all three primary outcomes (p ≥ .135), supporting standard ANCOVA interpretation. Holm-adjusted p-values are reported for the family of three confirmatory outcomes. Robustness check. To examine whether conclusions were sensitive to heteroscedasticity, the ANCOVA models were re-estimated as OLS regressions with heteroscedasticity-consistent (HC3) standard errors and corresponding Wald tests in Python; the group effects remained statistically significant and substantively unchanged ( Appendix G ). Cognitive engagement (H1). Adjusting for baseline engagement, the metaverse group demonstrated substantially higher posttest cognitive engagement than the control group (Δadj = 28.34, 95% CI [26.45, 30.22], p < .001; partial η² = .928). The magnitude indicates a very large separation between groups at posttest; however, the estimate should be interpreted alongside the restricted posttest variability noted below (floor/ceiling effects), which can inflate effect-size metrics. Digital burnout (H2). Adjusting for baseline burnout, the metaverse group reported markedly lower posttest burnout (Δadj = − 24.52, 95% CI [− 27.71, − 21.34], p < .001; partial η² = .773). Total burnout was scored so that higher values indicate more burnout, with a possible range of 16–64 (16 items scored 1–4; academic-efficacy items reverse-keyed before computing totals; totals are raw sums with no rescaling). At posttest, the experimental group’s observed burnout distribution clustered near the scale floor (Exp post: M = 25.37, SD = 0.49, min–max = 25–26; Ctrl post: M = 49.89, SD = 9.33, min–max = 38–58), which compresses variance and yields very tight adjusted-mean CIs. Scoring verification. Academic Efficacy items were reverse-keyed prior to computing the total burnout score so that the total consistently reflects “more burnout = higher score.” Totals were computed as [sum/mean] of items and were not rescaled (i.e., no z-scoring or range transformation). A scoring audit (item direction checks and recomputation) and posttest distribution diagnostics are provided in Appendix H . Creative writing skills (H3). Adjusting for baseline writing, the metaverse group achieved higher posttest rubric scores (Δadj = 8.23, 95% CI [7.45, 9.00], p < .001; partial η² = .866). Some rubric subdimensions exhibited ceiling or near-degenerate distributions at posttest, so the most stable inference is based on the total rubric score . Table 2 ANCOVA-Adjusted Posttest Effects for the Three Primary Outcomes Outcome (post, adjusted for pretest) Adj mean Control (95% CI) Adj mean Experimental (95% CI) Δadj Exp−Ctrl (95% CI) F (1,67) partial η² p (Holm) Cognitive Engagement (post) 100.52 [98.97, 102.06] 128.85 [127.77, 129.94] 28.34 [26.45, 30.22] 867.85 .928 < .001 Digital Burnout (post) 49.89 [46.72, 53.06] 25.37 [25.15, 25.58] −24.52 [− 27.71, − 21.34] 227.88 .773 < .001 Creative Writing Skills (post) 23.61 [23.00, 24.23] 31.84 [31.36, 32.32] 8.23 [7.45, 9.00] 434.23 .866 < .001 Note. Cognitive Engagement total range = 30–150 (subscales = 6–30 ). Creative Writing total range = 7–35 . Adjusted means are estimated at the grand mean of the pretest covariate. Δadj = adjusted difference (Experimental − Control). Models use HC3 robust standard errors; p -values are Holm-adjusted across the three primary outcomes. Taken together, the covariance-adjusted results provide convergent evidence that the FrameVR.io intervention was associated with higher cognitive engagement, higher creative writing performance, and lower digital burnout relative to traditional instruction, while also indicating measurement-range constraints that warrant cautious interpretation of effect magnitudes. 5.1.3. Assumption Checks, Sensitivity Analyses, and Subscale Patterns Model diagnostics. Residual normality tests suggested departures from normality for all three ANCOVA models (Shapiro–Wilk p < .001). Given the pronounced between-group shifts and restricted score ranges (especially for burnout and certain writing subdimensions), this pattern is expected and does not invalidate ANCOVA estimates under large, systematic effects; nonetheless, robust inference was prioritized. Heteroscedasticity. Potential heteroscedasticity was addressed using HC3 robust standard errors , which are less sensitive to unequal residual variance and influential points. Influence analysis. A sensitivity analysis excluding the most influential observation in the creative writing model (maximum Cook’s D ≈ .33) did not alter conclusions; the group effect remained p < .001 and the adjusted mean difference remained substantively unchanged. Exploratory subscales. Exploratory ANCOVAs (Holm-adjusted within each construct family) mirrored the total-score pattern for (a) all five engagement dimensions (all p < .001) and (b) all three burnout components (all p < .001). By contrast, several creative-writing subdimensions were near-constant at posttest (ceiling/degenerate distributions), so interpretation emphasizes the total rubric score as the most psychometrically stable outcome indicator in this dataset. 5.1.4. Change-Score Associations Within the Metaverse Group (RQ4) To examine whether improvements co-occurred within the metaverse condition , we correlated individual change scores (Δ = post − pre) in engagement, burnout, and writing within the experimental group only (n = 35) as shown in Table 3 . This avoids conflating trajectories across distinct instructional contexts. Table 3 Change-Score Correlations Within the Metaverse Group (n = 35) Variables r 95% CI p Δ Cognitive Engagement × Δ Digital Burnout −0.037 [− 0.37, 0.30] .834 Δ Cognitive Engagement × Δ Creative Writing 0.059 [− 0.28, 0.38] .735 Δ Digital Burnout × Δ Creative Writing −0.159 [− 0.47, 0.18] .362 Note. Correlations are computed within the metaverse group only. 95% CIs are Fisher- z transformed. None of the three pairwise change-score correlations reached statistical significance (all p ≥ .362), and confidence intervals were wide. This pattern is likely influenced by restricted posttest variance (burnout floor effects; ceiling/degenerate writing subdimensions), which attenuates correlations even when true relationships exist. Replication with larger samples, delayed posttests, and more discriminating measurement ranges is warranted. 5.2. Qualitative Analysis (Semi-Structured Interviews) Qualitative data were generated from semi-structured interviews with 18 metaverse participants (≈ 25% of the experimental cohort). The aim was to identify which features and experiences of FrameVR.io students perceived as influential—positively or negatively—for cognitive engagement, digital burnout, and creative writing development (RQ5). Interviews were audio-recorded, transcribed verbatim, anonymized, and analyzed using reflexive thematic analysis (Braun & Clarke, 2006). Analysis proceeded through (1) familiarization, (2) initial coding, (3) theme construction, (4) theme review, (5) theme naming/definition, and (6) reporting with illustrative excerpts. NVivo (QDAS) was used for systematic data management and traceable coding, but theme development followed the analytic logic of thematic analysis rather than software-driven frequency counts. Table 4 Qualitative Themes Regarding Student Perceptions of the FrameVR.io Intervention Theme Key codes (wtd %) Example quote(s) Interpretation 1. Enhanced engagement through immersion and active participation Immersive presence; active/playful learning (28%) “I felt like I was in the stories we were creating… The immersion blocked out real-world distractions.” (Aisha) Students repeatedly described the metaverse as “attention-capturing,” framing engagement as presence + action. Immersion reduced off-task distraction and made writing feel event-like rather than routine. 2. Collaboration and social learning via avatar-mediated interaction Dynamic collaboration; socially safer feedback (22%) “We could point to things in the virtual space.” (Banan) Shared space enabled referential communication (pointing, showing, co-locating ideas), and avatars lowered social risk for some learners, supporting participation and peer feedback. 3. Perceived writing gains through visualization, embodiment, and ideation support 3D visualization; character embodiment; idea generation (25%) “My descriptive writing improved… because I was visualizing things in 3D.” (Aisha) Students linked affordances to craft-level outcomes: richer description from spatial cues; stronger characterization from role-play; reduced writer’s block via novelty and prompts. 4. Mixed burnout experiences shaped by task design and technical friction Energizing engagement vs. draining tech strain (15%) “If I was fighting the tech, it was draining.” (Karima) Students distinguished “good fatigue” (productive immersion) from “bad fatigue” (lag, instability, eye strain). Burnout relief depended on smooth performance and well-scaffolded tasks. 5. Valued affordances and concrete improvement needs 3D spaces; whiteboards/objects; onboarding; text input (10%) “Better integration for individual text input.” (Banan) Participants praised core affordances (space, objects, co-creation) but requested stability, better writing-input workflows, and clearer onboarding to prevent extraneous load. Note . Percentages indicate the proportion of coded references (NVivo “references”) assigned to the theme out of the total coded references across all themes; they reflect coding density, not participant prevalence. Overall, the qualitative evidence indicates that the metaverse environment functioned as (a) an engagement amplifier through presence and agency, (b) a collaboration scaffold through shared referential space, and (c) a creativity trigger through visualization and novelty—while also being vulnerable to load and strain when technical friction undermined flow. Figure 2 provides a visual distillation of students’ discourse about the FrameVR.io experience. High-salience terms (e.g., activities, changes, questions, virtual, metaverse, avatar, 3D, environment, tools ) align with the thematic findings: students anchored their evaluations in the tasks they performed and the platform affordances that shaped those tasks. The co-occurrence of positively valenced terms ( helpful, engaging, fun, inspired ) alongside strain markers ( challenging, difficult, frustration, fatigue, exhausting ) visually corroborates Theme 4’s central insight: perceived burnout reduction was not automatic—it was contingent on technical stability and task design that minimized extraneous cognitive load. 5.3. Integration of Quantitative and Qualitative Findings Integration focused on explaining mechanisms behind the strong quantitative group effects and clarifying why burnout outcomes were robust in aggregate yet mixed in experience. Convergence for engagement and writing. Quantitative ANCOVA results showed very large adjusted posttest advantages for metaverse participants in cognitive engagement and writing performance. Qualitative themes converge strongly, attributing these gains to immersion (presence), agency (active exploration), and socially grounded collaboration, which collectively created conditions consistent with the study’s framework (SDT support, flow conditions, and reduced extraneous load when the system worked smoothly). Qualified convergence for burnout. Quantitatively, burnout was substantially lower in the metaverse group, but qualitative accounts introduce an important boundary condition: burnout relief depended on low technical friction and task designs that channel stimulation into purposeful writing, rather than interface management. In other words, the metaverse reduced burnout when it operated as a resource-rich learning ecology (JD-R), but risked becoming a net demand under instability or usability barriers. Attenuated Change-Score Associations The absence of significant within-metaverse change-score correlations is consistent with the qualitative account that many students converged toward similarly positive posttest states (compressed variance), and with the measurement constraints noted in the quantitative diagnostics (floor/ceiling effects). Thus, the mixed-methods pattern suggests strong mean-level benefits with attenuated individual-difference associations under restricted score ranges. Table 5 Integrated Summary of Evidence Construct Quantitative evidence Qualitative evidence Integrated interpretation Cognitive engagement Large metaverse advantage (all p < .001) Immersion + agency reduced distraction; collaboration increased participation Mechanism-consistent convergence: SDT + Flow conditions plausibly drove engagement gains. Creative writing skills Large metaverse advantage (all p < .001 on total) Visualization/embodiment improved description and characterization; novelty supported ideation Metaverse affordances mapped onto craft-level skills; strongest inference at total rubric level due to subscale ceilings. Digital burnout Large reduction in metaverse group ( p < .001) Burnout relief when tasks were energizing; tech strain when unstable Burnout reduction is contingent: the platform acts as JD-R “resource” under stable delivery, but may become a “demand” under friction. In sum, the mixed-methods evidence indicates that the FrameVR.io intervention produced strong, coherent improvements in engagement and writing outcomes, while reducing burnout overall—but with clear implementation-sensitive conditions that determine whether the metaverse experience feels energizing or draining. 6. Discussion This mixed-methods study examined whether a FrameVR.io-mediated creative writing intervention could (a) increase cognitive engagement, (b) improve creative writing performance, and (c) reduce digital burnout relative to traditional instruction among undergraduate EFL students. Using a quasi-experimental intact-class design and confirmatory ANCOVA models with HC3 robust standard errors (complete-case N = 70; 35/35), the quantitative results show very large adjusted posttest differences favoring the metaverse group for all three primary outcomes, with familywise error controlled via Holm adjustment. Qualitative interviews (n = 18) converged strongly with these results while also clarifying boundary conditions—particularly that burnout relief and sustained engagement depend on technical stability, onboarding, and task design that prevents extraneous cognitive load. 6.1. Enhanced Cognitive Engagement (RQ1 & H1) The findings provide strong support for H1. After adjusting for pretest engagement, the metaverse group achieved markedly higher posttest cognitive engagement (Δadj = 28.34, p < .001; partial η² = .928). Importantly, exploratory analyses indicated that this pattern extended across all engagement dimensions (attentional allocation, metacognitive self-regulation, active scholarly participation, propensity for creative challenge, and cognitive effort investment). This directly addresses the reviewers’ concern about “broad” engagement claims by demonstrating dimension-level evidence rather than relying on global impressions. The qualitative data substantially explained why engagement increased. Students repeatedly described heightened presence and attentional capture (“felt like I was in the stories”), consistent with immersion-based engagement mechanisms reported in metaverse/VR learning research (e.g., immersion and sustained attention). Their descriptions of “active” and “playful” learning align with Flow Theory (clear goals, absorption, intrinsic reward) and Self-Determination Theory (autonomy via exploration/choice; competence via feedback and visible progress; relatedness via avatar-mediated collaboration). In short, Fig. 1 ’s pathway—metaverse affordances → SDT/flow-supportive conditions → cognitive engagement—was supported both statistically and experientially. 6.2. Improved Creative Writing Skills (RQ3 & H3) The results support H3 : controlling for baseline writing performance, metaverse participants achieved higher posttest rubric totals (Δadj = 8.23, p < .001; partial η² = .866). This is a key contribution because it links an immersive platform not only to engagement but to performance in a complex, higher-order outcome (creative writing), which reviewers correctly noted is often missing in metaverse studies focused on attitudes or general engagement. Qualitative themes illuminate plausible mechanisms. Students emphasized that 3D visualization enriched descriptive language and scene construction, while avatar embodiment supported character voice and perspective-taking. The environment also functioned as an “idea generator,” reducing writer’s block through novelty and situated prompts. These accounts align with creative cognition views of writing that emphasize ideation scaffolds, imagery, and iterative refinement. At the same time, consistent with the reanalysis notes, some subdimensions of the rubric exhibited ceiling or restricted variance at posttest, so the most stable inference is at the total score level—a limitation we explicitly acknowledge (see § 6.6) and a priority for future work (more discriminating scoring ranges and delayed assessments). A crucial reviewer-facing clarification: the writing samples were scored by independent raters blinded to group and time, reducing expectancy effects and strengthening internal validity for this rater-scored outcome. 6.3. Reduced Digital Burnout (RQ2 & H2) The study supports H2 at the total-score and component level. Adjusted posttest burnout was substantially lower in the metaverse group (Δadj = − 24.52, p < .001; partial η² = .773). This is notable because immersive environments can plausibly increase strain through technostress, sensory overload, or usability friction. Here, the overall direction was protective. However, the mixed-methods evidence makes clear that this outcome is implementation-contingent. Qualitatively, many students described the experience as “energizing” and less exhausting because it replaced passive screen time with embodied, social, goal-directed activity. This interpretation aligns with the JD-R model : the intervention appears to add meaningful resources (social presence, agency, immediate feedback) that buffer demands, and with Cognitive Load Theory insofar as successful design may reduce extraneous load by making prompts contextual and concrete. Yet Theme 4 also recorded the opposing pathway: technical instability, friction in text input, and eye strain increased fatigue—precisely the conditions under which the metaverse can become a net demand rather than a resource. Quantitatively, the experimental group also showed floor effects in posttest burnout totals, which compress variance and require cautious interpretation. Taken together, the best-supported claim is not that the metaverse “automatically eliminates burnout,” but that—when technically stable and instructionally well-scaffolded—it can reduce burnout relative to traditional delivery of the same writing curriculum. 6.4. Relationships Between Constructs (RQ4; H4a & H4b) In response to reviewer concerns about correlational logic, the revised analysis tested change-score associations within the metaverse group only (Δ = post − pre), avoiding the methodological error of pooling across distinct instructional conditions. These within-group correlations were small and non-significant, with wide confidence intervals. This does not contradict the intervention effects; instead, it likely reflects restricted posttest variance (burnout floor; writing subdimension ceilings) that attenuates correlations and reduces sensitivity to individual-difference coupling. Conceptually, the qualitative data still supports the causal story implied by Fig. 1 : students often linked being more immersed and focused (engagement) with producing richer writing, and linked technical frustration (a demand) with reduced creative flow. Empirically, however, the most defensible RQ4 conclusion under the current measurement ranges is: the intervention shifts group means strongly, but individual change-to-change coupling could not be reliably detected in this sample. Future work should test mediation pathways using larger samples, more discriminating measures, log-based indicators (time-on-task, artifacts), and delayed posttests. 6.5. Impactful Features and Student Perceptions (RQ5) Themes 2 and 5 identify the affordances most responsible for perceived gains: Immersive scenes + task boards that make prompts concrete (supporting attention and ideation). Avatar-mediated interaction that increases social presence and reduces anxiety for some learners. Shared whiteboards and 3D objects that enable joint planning and visually grounded collaboration. At the same time, students reported clear friction points: platform stability, onboarding, and writing-input workflows. These perceptions map directly onto CLT (extraneous load), and they explain why burnout effects can be conditional. This section also supports reviewers’ requests for implementation transparency: the intervention is best described not as “the metaverse in general,” but as a specific six-week, task-sequenced FrameVR.io writing design with identifiable features. 6.6. Contributions, Limitations, and Future Directions Contributions. Method–results alignment : The revised results now match the prespecified analytic plan— ANCOVA with robust inference —addressing the primary methodological critique. Granular engagement evidence : Effects are documented across engagement dimensions, not only total scores. Rater-scored writing with blinding : Independent blinded raters strengthen credibility for the writing outcome. Mixed-method mechanism clarity : Qualitative themes specify what worked (immersion, collaboration, visualization) and what can undermine benefits (technical friction). Implementation evidence : The appendices add an implementation log structure (attendance/participation, artifacts) that strengthens fidelity claims. Limitations (explicitly acknowledging reviewers’ key points). Quasi-experimental intact classes : residual confounding (teacher/class effects) remains possible despite baseline comparability and covariance adjustment. Future studies should use randomization or hierarchical models with class/teacher effects when multiple classes are available. Complete-case analysis (N = 70) : attrition/matching loss is now transparently reported; however, missingness mechanisms cannot be fully ruled out without item-level and log-level data. Measurement constraints : some outcomes show floor/ceiling effects (burnout totals at posttest; writing subdimensions), which may inflate group separation and attenuate correlations. Self-report outcomes : engagement and burnout rely on self-report; triangulation with platform logs and behavioral indicators is needed. Short-term posttest : no delayed posttest was collected, limiting claims about retention and durability beyond immediate gains. Platform specificity : results are for a particular FrameVR.io configuration; generalization to other metaverse platforms should be cautious. Future directions. Include delayed posttests (4–8 weeks) to assess retention and novelty decay. Add platform logs (time-on-task, artifact counts, posting frequency) to corroborate engagement and reduce shared-method bias. Collect item-level responses for engagement and burnout to enable CFA, measurement invariance, and more defensible subscale modeling. Use multilevel modeling in designs with multiple classes/teachers. Pre-register analysis plans and specify multiplicity control for secondary outcomes. 7. Conclusion This study provides convergent evidence that a carefully task-designed FrameVR.io creative writing environment can meaningfully enhance EFL students’ cognitive engagement and creative writing performance while reducing digital burnout relative to traditional instruction. Quantitatively, robust ANCOVA models show very large adjusted posttest differences favoring the metaverse group on all three primary outcomes, and qualitatively, students attribute gains to immersion, agency, visualization, and collaboration. Yet the findings also demonstrate that benefits are not automatic . Technical instability, weak onboarding, and high-friction writing workflows can increase extraneous cognitive load and undermine well-being. Therefore, the most defensible interpretation is conditional: when the metaverse environment is technically reliable and instructionally scaffolded to support flow and autonomy, it can act as a net learning resource; when it is unstable or poorly scaffolded, it can become a net demand. 7.1. Implications for Pedagogy and Practice Design immersion as a writing scaffold, not decoration. Use thematically rich scenes to concretize prompts and support sensory description and setting construction. Prioritize agency and visible progress. Tasks should require exploration, choice, and creation (objects, boards, plots) with clear goals and feedback. Exploit embodiment for characterization. Avatar-based role-play and “character interviews” can support voice, stance, and perspective-taking. Treat usability as pedagogy. Onboarding, stability checks, and low-friction text input are essential to prevent extraneous load and burnout. Build collaboration into artifacts. Shared whiteboards/objects enable grounded co-authoring; assess group outputs systematically. Monitor strain proactively. Include micro-breaks, ergonomic guidance, and rapid technical support to reduce fatigue and frustration. Document fidelity. Maintain attendance and participation logs and an intervention checklist to support replicability and reviewer expectations. 7.2. Recommendations for Future Research Replication across sites and platforms with larger samples and multiple instructors. Delayed posttests to evaluate retention and novelty effects. Mechanism tests (mediation models) using logs + discriminating scales to model engagement → writing via reduced burnout and improved self-regulation. Comparative instructional designs (e.g., metaverse with/without collaboration; with/without embodiment) to isolate active ingredients. Objective indicators (time-on-task, artifact production, writing revision traces) to triangulate self-report. Equity and access audits (device constraints, connectivity, usability barriers) as a core research component. Declarations Data Availability Statement "The data that support the findings of this study are available from the corresponding author upon reasonable request." Ethics Statement This study was conducted in accordance with the ethical guidelines of the Supreme Council of Universities in Egypt (Correspondence dated 28/03/2023, Article 25). Ethical approval was obtained from the Faculty of Education, Beni-Suef University (Approval Ref. No. 26-06-2024). The research involved a minimal-risk educational intervention with university students and did not include any invasive, experimental, or clinical procedures on humans or animals. 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Supplementary Files Instrumentsforthemetaverseresearch.docx Rawdata.xlsx MetaverseAppendices.docx Postexperimentalgrowthonallmeasures1.xlsx Preinterventionequivalenceforgroups2.xlsx PrePostgrowthonallmeasures3.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Introduction","content":"\u003cp\u003eProficient English creative writing is increasingly essential for academic success, professional mobility, and nuanced intercultural communication (Andrews \u0026amp; Smith, 2011; Kellogg, 2018). Yet, in many undergraduate EFL contexts, instructional conditions (e.g., large classes, limited individualized feedback, and a product-over-process emphasis) can constrain opportunities for sustained practice, iterative revision, and authentic audience-oriented writing\u0026mdash;conditions that are central to developing high-level creative writing. These constraints are closely tied to cognitive engagement, which in creative writing entails more than participation: it includes sustained attentional focus on craft, metacognitive regulation of planning\u0026ndash;drafting\u0026ndash;revising, active participation in compositional tasks, willingness to attempt creative challenge, and persistent effort investment in improving written output.\u003c/p\u003e \u003cp\u003eAt the same time, digitally mediated learning has expanded rapidly, creating new opportunities alongside new risks. One prominent risk is digital burnout, commonly reflected in emotional exhaustion, cynicism, and reduced perceived academic efficacy linked to prolonged, demanding, or poorly structured digital interaction (Maslach \u0026amp; Leiter, 2011; Salmela-Aro et al., 2017). For cognitively demanding tasks such as creative writing\u0026mdash;which require sustained ideation, linguistic monitoring, and revision\u0026mdash;digital burnout may reduce persistence, constrain willingness to attempt challenging writing moves, and undermine perceived competence.\u003c/p\u003e \u003cp\u003eAgainst this background, the metaverse\u0026mdash;understood here as a persistent, shared 3D social environment enabling embodied interaction through avatars and collaborative creation\u0026mdash;has been proposed as a pedagogical frontier with potential to increase engagement and enrich task authenticity. Importantly, many educational studies use \u0026ldquo;metaverse\u0026rdquo; loosely to refer to general VR/3D tools. In this study, the term is used narrowly and operationally: the intervention was implemented in FrameVR.io, a browser-based social 3D environment that supports synchronous collaboration, avatar interaction, and shared virtual workspaces. This is pedagogically relevant to creative writing because it allows learners to (a) inhabit richly cued settings that stimulate description, (b) role-play character interactions via avatars, and (c) co-construct plots using shared whiteboards and artifacts\u0026mdash;affordances that align directly with the cognitive and social processes involved in narrative composition.\u003c/p\u003e \u003cp\u003eThe study therefore investigates whether a FrameVR.io-based creative-writing design can simultaneously (i) enhance cognitive engagement, (ii) reduce digital burnout, and (iii) improve creative writing performance\u0026mdash;while documenting implementation fidelity and scoring integrity in ways that meet contemporary transparency standards.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Research gaps and problem statement\u003c/h2\u003e \u003cp\u003eDespite growing interest in metaverse-enabled learning, several gaps remain\u0026mdash;particularly in EFL creative writing:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEngagement specificity (granularity).\u003c/b\u003e Many studies report \u0026ldquo;engagement\u0026rdquo; broadly without testing domain-relevant dimensions of cognitive engagement in writing (e.g., attention allocation, self-regulation during composing, and sustained effort investment). More fine-grained evidence is needed to determine \u003cem\u003ewhich\u003c/em\u003e engagement facets are most influenced by immersive, collaborative 3D learning.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSkill transfer to creative writing outcomes.\u003c/b\u003e Empirical work often emphasizes attitudes, satisfaction, or general language outcomes. There is comparatively less evidence linking metaverse-supported engagement to measurable improvements in creative writing using rubric-based performance assessment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDigital burnout as an outcome.\u003c/b\u003e Research rarely tests whether immersive, agentic collaboration can reduce digital burnout\u0026mdash;or whether such environments introduce new strain (e.g., cognitive overload, technostress). This question is especially consequential for writing tasks that demand sustained cognitive effort.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDesign transparency and methodological rigor.\u003c/b\u003e Reviewers increasingly expect (a) consistent, prespecified analyses (e.g., ANCOVA for baseline adjustment), (b) clear participant accounting (enrollment vs complete-case), (c) blinded scoring where outcomes are rater-scored, (d) multiplicity control across multiple outcomes, and (e) implementation evidence (fidelity logs, participation logs, and screenshots).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProblem statement.\u003c/b\u003e The field lacks sufficiently rigorous, transparent evidence on whether a FrameVR.io-based metaverse creative-writing design can enhance cognitive engagement, reduce digital burnout, and improve creative writing performance among university EFL learners, and through which proximal mechanisms these effects plausibly operate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Research questions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eRQ1.\u003c/b\u003e To what extent does participation in FrameVR.io-based creative writing influence students\u0026rsquo; overall cognitive engagement and its dimensions (attentional allocation, metacognitive self-regulation, active scholarly participation, propensity for creative challenge, and cognitive effort investment) compared with traditional instruction?\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ2.\u003c/b\u003e To what extent does participation in FrameVR.io-based creative writing influence students\u0026rsquo; digital burnout compared with traditional instruction?\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ3.\u003c/b\u003e What is the impact of FrameVR.io-based creative writing on creative writing performance (originality, coherence, descriptive language, narrative structure) compared with traditional instruction?\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ4 (revised).\u003c/b\u003e Within each group, what are the associations between change scores (post\u0026ndash;pre) in (a) cognitive engagement dimensions, (b) digital burnout, and (c) creative writing performance? Additionally, do these associations differ by group (interaction/exploratory moderation)?\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ5.\u003c/b\u003e Which features or experiences in the FrameVR.io environment do students perceive as most influential (positively or negatively) for engagement, burnout, and writing development?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Hypotheses (tightened; burnout efficacy direction clarified)\u003c/h2\u003e \u003cp\u003e \u003cb\u003eH1.\u003c/b\u003e Students in the FrameVR.io condition will show higher posttest cognitive engagement (overall and by dimension) than controls, adjusting for pretest scores.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2.\u003c/b\u003e Students in the FrameVR.io condition will report lower posttest digital burnout than controls, adjusting for pretest scores (lower exhaustion and cynicism; higher academic efficacy where applicable and correctly scored).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3.\u003c/b\u003e Students in the FrameVR.io condition will demonstrate greater improvement in rubric-scored creative writing performance than controls, adjusting for pretest scores.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4a.\u003c/b\u003e Within groups, increases in cognitive engagement (post\u0026ndash;pre) will be positively associated with gains in creative writing performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4b.\u003c/b\u003e Within groups, increases in digital burnout (post\u0026ndash;pre) will be negatively associated with gains in creative writing performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Metaverse-enabled learning: affordances relevant to writing\u003c/h2\u003e \u003cp\u003eEducational discussions of the metaverse commonly highlight immersion, interactivity, social presence, and shared creation. For writing pedagogy, these affordances matter when they translate into task-relevant cognition (e.g., generating descriptive detail, sustaining attention, and engaging in iterative drafting) and \u003cb\u003esocially mediated composing\u003c/b\u003e (e.g., peer feedback, co-construction of narratives). In contrast to isolated VR experiences, a metaverse environment\u0026mdash;as operationalized here\u0026mdash;emphasizes persistent shared space, synchronous collaboration, and avatar-based social interaction, which may better support peer-supported writing processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Cognitive engagement in immersive and collaborative environments\u003c/h2\u003e \u003cp\u003eCognitive engagement involves sustained mental effort, strategic learning, and deep processing\u0026mdash;particularly critical in writing, where learners must plan, draft, monitor language, and revise. Immersive environments may influence cognitive engagement by providing (a) contextual cues that support ideation and elaboration, (b) interactive tasks that sustain attention, and (c) collaborative routines that encourage self-regulation and persistent effort. However, evidence remains uneven in terms of whether immersive engagement translates into \u003cem\u003emeasurable writing performance\u003c/em\u003e, and which engagement dimensions are most responsive to metaverse designs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Digital burnout: why immersive learning may reduce\u0026mdash;or intensify\u0026mdash;it\u003c/h2\u003e \u003cp\u003eDigital burnout is increasingly recognized as a barrier to sustained learning in technology-mediated contexts. It can arise from prolonged exposure, fragmented attention, poorly structured tasks, and technostress. A metaverse environment might reduce burnout if it shifts learners from passive, fatigue-inducing screen time toward \u003cem\u003eagentic, socially supported, intrinsically motivating activity\u003c/em\u003e. Conversely, if immersive environments add complexity or excessive stimulation, they may increase extraneous cognitive load and contribute to exhaustion. This makes burnout a necessary empirical outcome rather than an assumed benefit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Creative writing pedagogy in digital contexts\u003c/h2\u003e \u003cp\u003eCreative writing development depends on iterative composing, strategic revision, feedback, and sustained engagement with craft. Digital contexts can support these processes when they provide structured prompts, authentic audiences, collaboration, and multimodal inspiration. A metaverse environment may offer distinctive support by letting writers \u003cem\u003einhabit settings\u003c/em\u003e, enact character perspective through avatars, and visualize plot/world constraints in shared spaces. Yet, robust evidence remains limited, especially using (a) baseline-adjusted designs, (b) blinded rater-scored outcomes, and (c) transparency artifacts documenting implementation fidelity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSynthesis.\u003c/b\u003e The literature supports plausible mechanisms for metaverse-supported engagement and writing development, but it does not yet provide sufficiently rigorous, transparent evidence for whether (and how) metaverse environments can improve EFL creative writing while also reducing digital burnout. This study addresses those gaps using baseline-adjusted analyses, robust inference, and implementation documentation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Theoretical Framework (streamlined so it reads as one integrated model—not “too many theories”)","content":"\u003cp\u003eThis study adopts an \u003cem\u003eintegrated mechanism framework\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to explain how a FrameVR.io metaverse intervention may influence cognitive engagement, digital burnout, and creative writing performance. Rather than treating multiple theories as competing explanations, the framework uses them as \u003cem\u003ecomplementary lenses\u003c/em\u003e mapped to distinct parts of the causal chain:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMotivational mechanism (SDT + Flow)\u003c/b\u003e: explains why immersive, collaborative writing may increase engagement.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDemand–resource mechanism (CLT + JD-R)\u003c/b\u003e: explains why the same environment may reduce or exacerbate burnout depending on design quality.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Motivation and engagement mechanisms (SDT and Flow)\u003c/h2\u003e \u003cp\u003eSelf-Determination Theory proposes that autonomy, competence, and relatedness support high-quality engagement and well-being. In FrameVR.io, autonomy can be supported through choices in navigation and narrative development; competence through scaffolded prompts and visible progress; and relatedness through synchronous collaboration and social presence. These conditions are also consistent with Flow Theory, which predicts deep absorption when challenge matches skill, goals are clear, and feedback is timely. The intervention’s weekly design (setting → character → plot/world-building) was structured to progressively increase complexity while maintaining clear task goals, aiming to support sustained attentional allocation and effort investment during composing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Load and burnout mechanisms (CLT and JD-R)\u003c/h2\u003e \u003cp\u003eCognitive Load Theory distinguishes intrinsic load (inherent difficulty) from extraneous load (imposed by poor design). Metaverse environments can reduce extraneous load when interfaces are intuitive and prompts are well-scaffolded; they can increase it when technical friction, overstimulation, or unclear instructions divert working memory away from writing. The JD-R model complements this by framing burnout as the product of \u003cem\u003edemands\u003c/em\u003e (sustained effort + technostress) exceeding \u003cem\u003eresources\u003c/em\u003e (peer support, autonomy, feedback, and meaningful task structure). In this study, the metaverse is expected to function as a \u003cem\u003enet resource\u003c/em\u003e if the instructional design minimizes extraneous load and supports social and motivational resources; otherwise, it may create additional demand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and interpretation guidance\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure. A framework linking FrameVR.io creative writing to engagement, burnout, and performance via motivational and demand-resource mechanisms\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the study’s integrated theoretical framework as \u003cem\u003ea mechanism-based causal pathway\u003c/em\u003e. The model begins with the \u003cem\u003eFrameVR.io metaverse creative writing intervention\u003c/em\u003e (input), operationalized through affordances such as immersion, interactivity, social presence, embodied avatar participation, and structured collaborative tasks. These affordances are expected to trigger four complementary mechanisms: (a) Self-Determination Theory (SDT) via greater autonomy, competence, and relatedness; (b) Flow Theory via clear goals, balanced challenge, and immediate feedback that support deep absorption; (c) Cognitive Load Theory (CLT) via reduced extraneous load when the interface and scaffolding are well designed; and (d) the Job Demands–Resources (JD-R) model via a shift in the demands–resources balance, where instructional supports and peer collaboration function as resources that buffer strain.\u003c/p\u003e \u003cp\u003eThrough these mechanisms, the framework predicts two \u003cem\u003eproximal outcomes\u003c/em\u003e. First, the intervention should enhance \u003cem\u003ecognitive engagement\u003c/em\u003e, reflected in stronger attentional allocation, metacognitive self-regulation during composing, active scholarly participation, a greater propensity for creative challenge, and higher cognitive effort investment. Second, the intervention should reduce \u003cem\u003edigital burnout\u003c/em\u003e, expressed as lower emotional exhaustion and cynicism and, where the scale permits, higher academic efficacy. The framework then specifies a distal pathway: when engagement is strengthened and burnout is reduced, students are expected to show measurable gains in \u003cem\u003ecreative writing performance\u003c/em\u003e, indexed by originality, coherence, descriptive language, and narrative structure.\u003c/p\u003e \u003cp\u003eCrucially, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e also highlights \u003cem\u003einstructional design quality as a boundary condition\u003c/em\u003e. The metaverse environment is expected to operate as a \u003cem\u003enet academic resource\u003c/em\u003e only when task sequencing, scaffolding, and usability minimize extraneous cognitive load and technostress while maximizing meaningful interaction. Under these conditions, the model predicts the strongest improvements in engagement and writing outcomes, alongside the greatest reductions in burnout.\u003c/p\u003e "},{"header":"4. Methodology","content":"\u003cp\u003e\u003cstrong\u003e4.0. Design Overview\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis study adopted a \u003cem\u003esequential explanatory mixed-methods design\u003c/em\u003e (QUAN → QUAL), in which quantitative effects of the intervention were estimated first and then elaborated through post-intervention interviews (Creswell \u0026amp; Plano Clark, 2018). The quantitative strand used a \u003cem\u003equasi-experimental pretest–posttest non-equivalent groups design\u003c/em\u003e with intact undergraduate EFL classes (metaverse vs. traditional instruction). The qualitative strand used \u003cem\u003esemi-structured interviews\u003c/em\u003e with a purposive subsample of metaverse participants to explain \u003cem\u003ehow\u003c/em\u003e and \u003cem\u003ewhy\u003c/em\u003e the intervention influenced engagement, burnout, and writing outcomes and to document implementation experiences (e.g., affordances, constraints, and technical issues).\u003c/p\u003e\u003ch2\u003e4.1. Participants, Setting, and Ethical Compliance\u003c/h2\u003e\u003cp\u003eParticipants were undergraduate EFL students enrolled in the English Department at a Faculty of Education (public university). Two intact classes were allocated to:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExperimental (Metaverse/FrameVR.io)\u003c/b\u003e: \u003cem\u003en\u003c/em\u003e = 37 at baseline\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eControl (Traditional classroom instruction)\u003c/b\u003e: \u003cem\u003en\u003c/em\u003e = 36 at baseline\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eBecause the design used intact classes, random assignment was not feasible. Therefore, baseline differences were handled analytically (see Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e) rather than treated as “solved” by pretest \u003cem\u003et\u003c/em\u003e-tests.\u003c/p\u003e\u003cp\u003e \u003cb\u003eFinal analytic sample and participant flow.\u003c/b\u003e Of the 73 students who completed baseline measures, 70 provided complete pre–post data on the three primary outcomes and submitted scorable pre/post writing tasks (complete-case dataset: N = 70; 35 metaverse / 35 control). Three cases were excluded from primary analyses due to posttest incompleteness and/or missing scorable outcome artifacts (\u003cb\u003esee Appendix F: Participant Flow Diagram\u003c/b\u003e). All analyses reported in the Results section are based on this complete-case dataset unless otherwise indicated.\u003c/p\u003e\u003cp\u003e \u003cb\u003eEthics.\u003c/b\u003e Institutional approval was obtained from the relevant ethics/IRB committee, and all participants provided informed consent. Participation was voluntary; students could withdraw without penalty. Data were anonymized prior to analysis and reported in aggregate.\u003c/p\u003e\u003ch2\u003e4.2. Instructional Design and Intervention Conditions (Six Weeks; 90 Minutes/Week)\u003c/h2\u003e\u003cp\u003eThe intervention was designed to operationalize the affordances in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e—\u003cem\u003eimmersion, interactivity, social presence, embodiment, and agency\u003c/em\u003e—through constructivist and task-based creative writing pedagogy. The experimental condition used \u003cem\u003eFrameVR.io\u003c/em\u003e (web-based; no specialized headset required), selected for (a) accessibility, (b) stable avatar-based collaboration, and (c) embedded interactive tools (whiteboards, objects, spatialized prompts).\u003c/p\u003e\u003cp\u003eBoth groups covered the same curricular writing targets (setting, atmosphere, character development, plot/world-building, descriptive language), with equivalent instructional time (6 × 90 minutes). The difference was delivery mode and task ecology (metaverse tasks vs. conventional classroom tasks).\u003c/p\u003e\u003ch2\u003e4.2.1. Experimental Group (FrameVR.io)\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWeeks 1–2: Setting \u0026amp; Atmosphere (Immersive description).\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eStudents navigated thematically rich virtual environments (e.g., “enchanted forest,” “futuristic city”) and produced multi-sensory setting descriptions anchored to spatial cues (visual, spatial relations, object affordances).\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWeeks 3–4: Character Development (Embodied role-play).\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eStudents customized avatars and conducted structured “character interviews” to develop voice, backstory, motivation, and conflict. These tasks operationalized social presence and embodied interaction.\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWeeks 5–6: Plot \u0026amp; World-Building (Collaborative narrative construction).\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eSmall groups co-created narratives using interactive whiteboards and 3D objects to outline plot arcs, place symbolic objects, and draft scenes with real-time peer feedback.\u003c/p\u003e\u003cp\u003eThe instructor acted as a facilitator (task sequencing, prompts, feedback, and technical support) to reduce extraneous cognitive load and maintain a balance between challenge and skill (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: CLT + Flow conditions).\u003c/p\u003e\u003ch2\u003e4.2.2. Control Group (Traditional Classroom)\u003c/h2\u003e\u003cp\u003eThe control class completed the same writing objectives using lectures, textbook-based prompts, individual drafting, paper-based worksheets, and in-class discussion. Writing tasks were completed under standardized classroom conditions without FrameVR.io activities.\u003c/p\u003e\u003ch2\u003e4.2.3. Minimal Implementation Evidence Package (for transparency)\u003c/h2\u003e\u003cp\u003eTo address implementation/fidelity expectations for intact-class designs, A participant flow summary is provided in the Appendix to address transparency requirements.\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTeacher fidelity checklist\u003c/b\u003e confirming weekly coverage of the same core components in both groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAttendance/participation log\u003c/b\u003e summarizing session attendance and engagement proxies (e.g., artifacts/posts in FrameVR; handwritten drafts in control).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVisual evidence\u003c/b\u003e (anonymized screenshots of FrameVR.io and classroom photos) aligned to the six-week sequence.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003ch2\u003e4.3. Measures and Instruments\u003c/h2\u003e\u003cp\u003eAll quantitative instruments were administered \u003cem\u003epre- and post-intervention\u003c/em\u003e. Students in both groups completed the same survey battery. Writing tasks were collected at both time points under standardized conditions.\u003c/p\u003e\u003ch2\u003e4.3.1. Cognitive Engagement in English Creative Writing\u003c/h2\u003e\u003cp\u003eCognitive engagement was measured using a \u003cem\u003emulti-dimensional scale\u003c/em\u003e aligned to the construct definition in this study (five subdimensions):\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAttentional Allocation\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMetacognitive Self-Regulation\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eActive Scholarly Participation\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePropensity for Creative Challenge\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCognitive Effort Investment\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003eScores were computed as (a) \u003cem\u003esubscale scores\u003c/em\u003e and (b) an \u003cem\u003eoverall engagement composite\u003c/em\u003e. Content validity was established via expert review (TESOL and educational technology). Internal consistency was estimated for the overall scale and subscales (reported in Results/Appendix).\u003c/p\u003e\u003cp\u003eItem-level evidence: Factor-analytic validation (EFA/CFA) requires item-level response matrices. If item-level responses are available, we will report dimensionality evidence in an expanded supplement; otherwise, dimensionality will be stated as a limitation and treated cautiously.\u003c/p\u003e\u003cp\u003e \u003cb\u003eScoring and range.\u003c/b\u003e Cognitive engagement was computed as the sum of 30 Likert-type items, with higher scores indicating greater engagement. The possible total score range is 30–150. Five subscales were computed by summing their respective items; each subscale ranges 6–30.\u003c/p\u003e\u003ch2\u003e4.3.2. Digital Burnout\u003c/h2\u003e\u003cp\u003eDigital burnout was assessed using an adapted student burnout instrument with three subscales:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEmotional Exhaustion\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCynicism/Detachment\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAcademic Efficacy\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003e \u003cb\u003eScoring and directionality.\u003c/b\u003e To interpret burnout in the conventional direction (higher = worse), \u003cem\u003eAcademic Efficacy was reverse-keyed\u003c/em\u003e when computing a \u003cem\u003etotal burnout score\u003c/em\u003e. Thus, \u003cem\u003elower total burnout indicates less burnout\u003c/em\u003e. Subscale results were also examined to avoid masking divergent patterns (e.g., efficacy behaving differently).\u003c/p\u003e\u003ch2\u003e4.3.3. Creative Writing Skills (Analytic Rubric – Instrument 3)\u003c/h2\u003e\u003cp\u003eCreative writing performance was scored using a structured analytic rubric assessing:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eOriginality/Creativity\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoherence/Narrative organization\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDescriptive language/Imagery\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNarrative structure/Plot development\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCharacterization/Voice\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMechanics\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003e \u003cb\u003eScoring and range.\u003c/b\u003e Creative writing performance was assessed using a rubric comprising seven criteria (Originality, Coherence, Imagery, Structure, Voice, Mechanics, Impact), each rated on a 1–5 scale. Criterion scores were summed to form a total score with a possible range of 7–35, where higher scores indicate stronger creative writing performance.\u003c/p\u003e\u003cp\u003e \u003cb\u003eBlinded independent rating.\u003c/b\u003e Two trained raters scored scripts independently and were blinded to group allocation and timepoint (pre/post). Blinding was implemented by anonymizing scripts, removing any class identifiers, and randomizing script order prior to scoring; pre/post labels were not provided to raters. Discrepancies were resolved through rubric-guided reconciliation after independent scoring where required (details in Appendix). Inter-rater reliability was evaluated on a randomly selected subset (reported as ICC in Results/Appendix, consistent with the study’s scoring protocol).\u003c/p\u003e\u003ch2\u003e4.3.4. Semi-Structured Interview Protocol\u003c/h2\u003e\u003cp\u003eA semi-structured interview guide was developed to address RQ5, focusing on:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eFeatures that increased or decreased engagement (immersion, collaboration, agency, task prompts)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExperiences linked to fatigue/relief (burnout-related perceptions)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerceived changes in writing process and outcomes\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTechnical constraints and usability issues\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComparative reflections versus conventional instruction\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eEighteen students from the metaverse group participated (n = 18), selected purposively to reflect variation in participation and outcome change patterns.\u003c/p\u003e\u003ch2\u003e4.4. Procedure\u003c/h2\u003e\u003cp\u003eThe study ran over \u003cb\u003eeight weeks\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e \u003cb\u003ePhase 1: Preparation and training.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFrameVR.io spaces and tasks were built and tested. Raters were trained using anchor scripts and a scoring protocol. Instruments and interview prompts were reviewed for clarity.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePhase 2: Baseline (Pretest).\u003c/b\u003e \u003c/p\u003e\u003cp\u003eBoth groups completed the engagement and burnout surveys and produced a baseline writing sample under standardized instructions.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePhase 3: Intervention (Six weeks).\u003c/b\u003e \u003c/p\u003e\u003cp\u003eExperimental group engaged in FrameVR.io sessions (90 minutes/week). Control group received conventional instruction with matched content targets and time.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePhase 4: Posttest and qualitative follow-up.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eImmediately after week 6, both groups repeated the same survey battery and completed a post-intervention writing sample. Metaverse interviews were then conducted (recorded with permission, transcribed verbatim, anonymized).\u003c/p\u003e\u003cp\u003e \u003cb\u003ePhase 5: Data integrity and dataset finalization.\u003c/b\u003e \u003c/p\u003e\u003cp\u003eRecords were screened for completeness and scorable writing artifacts, yielding the complete-case dataset (N = 70; 35/35). A participant flow summary is provided in \u003cb\u003eAppendix F (Participant Flow Diagram; CONSORT-style)\u003c/b\u003e to address transparency requirements.\u003c/p\u003e\u003ch2\u003e4.5. Data Analysis Plan (Aligned to the Prespecified Model)\u003c/h2\u003e\u003cp\u003eTo address reviewer concerns about analytic drift and researcher degrees of freedom, the primary analysis followed a prespecified strategy consistent with the Method section and the revised Abstract.\u003c/p\u003e\u003ch2\u003e4.5.1. Quantitative Analyses\u003c/h2\u003e\u003cp\u003eAnalyses were conducted in SPSS (v28) and cross-checked in Python.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePrimary outcomes (confirmatory)\u003c/b\u003e:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eOverall Cognitive Engagement (post)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTotal Digital Burnout (post; efficacy reverse-keyed)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTotal Creative Writing Score (post)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eFor each primary outcome, we estimated \u003cb\u003eANCOVA models\u003c/b\u003e:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{Posttest}={\\beta\\:}_{0}+{\\beta\\:}_{1}\\left(\\text{Group}\\right)+{\\beta\\:}_{2}\\left(\\text{Pretest}\\right)+\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRobust (HC3) standard errors\u003c/b\u003e were used to mitigate heteroscedasticity risks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHomogeneity of regression slopes\u003c/b\u003e was assessed using a Group × Pretest interaction; if violated, sensitivity models were reported.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResults are reported with adjusted posttest means, 95% confidence intervals, and effect sizes (partial η² for ANCOVA; and Hedges’ \u003cem\u003eg\u003c/em\u003e for descriptive contrasts where relevant).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo control familywise error across the three primary outcomes, \u003cem\u003ep\u003c/em\u003e-values were Holm-adjusted.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003e \u003cb\u003eSecondary outcomes (exploratory)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eEngagement subdimensions and burnout subscales were analyzed as exploratory, with multiplicity control within each construct family (Holm adjustment within engagement subscales; within burnout subscales), and interpreted cautiously.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRQ4 (associations)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTo avoid conflating distinct instructional contexts, correlation analyses were computed (a) within the metaverse group using change scores (post − pre) for engagement, burnout, and writing outcomes, and (b) as a sensitivity analysis in the full sample controlling for group (reported in Appendix). Correlations are presented with 95% CIs and clear variable definitions.\u003c/p\u003e\u003ch2\u003e4.5.2. Qualitative Analyses\u003c/h2\u003e\u003cp\u003eInterview transcripts were analyzed using \u003cem\u003ereflexive thematic analysis\u003c/em\u003e (Braun \u0026amp; Clarke, 2006), including familiarization, coding, theme development, review, naming, and reporting. Themes were used to explain mechanisms aligned to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (SDT, Flow, CLT, JD-R), including how immersion/collaboration shaped engagement, how technical constraints influenced load/strain, and how participants interpreted “fatigue” versus “energizing” digital experiences.\u003c/p\u003e\u003ch2\u003e4.5.3. Mixed-Methods Integration\u003c/h2\u003e\u003cp\u003eIntegration occurred at interpretation: qualitative themes were mapped onto quantitative results to clarify \u003cem\u003ewhich intervention features\u003c/em\u003e plausibly drove observed effects and to document constraints that may limit generalizability (e.g., platform usability, access variability). This integration directly addresses reviewer requests for stronger procedural clarity, implementation evidence, and mechanism-consistent interpretation.\u003c/p\u003e"},{"header":"5. Findings","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Quantitative Analysis\u003c/h2\u003e \u003cp\u003eData were screened for completeness, plausibility, and cross-instrument consistency prior to inferential testing. Of the 73 students enrolled at baseline, complete and linkable pre/post records were available for 70 (35 per group). This complete-case set (N\u0026thinsp;=\u0026thinsp;70) was used for the primary ANCOVA models. Unless otherwise specified, tests are two-sided with α\u0026thinsp;=\u0026thinsp;.05; primary-outcome \u003cem\u003ep\u003c/em\u003e-values are Holm-adjusted to control familywise error across the three confirmatory outcomes.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1. Baseline Equivalence and Pretest Comparability\u003c/h2\u003e \u003cp\u003eBaseline descriptives are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The two intact classes were closely comparable on the three pretest totals, with Welch tests indicating no detectable baseline differences (all \u003cem\u003ep\u003c/em\u003e \u0026ge; .424). Given the quasi-experimental design, baseline tests are treated as descriptive only; \u003cb\u003eprimary inference relies on ANCOVA\u003c/b\u003e, which adjusts posttest group differences for pretest performance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBaseline Descriptives (Pretest) by Group\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl M (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp. n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp. M (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean diff (Ctrl\u0026thinsp;\u0026minus;\u0026thinsp;Exp) 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWelch t(df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Engagement (pre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.80 (4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.74 (1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.06 [\u0026minus;\u0026thinsp;1.49, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Burnout (pre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.31 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.71 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.40 [\u0026minus;\u0026thinsp;1.39, 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.80 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreative Writing Skills (pre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.40 (1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.23 (1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0.17 [\u0026minus;\u0026thinsp;0.58, 0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values are mean (SD). \u003cb\u003eMean difference is Control\u0026thinsp;\u0026minus;\u0026thinsp;Experimental.\u003c/b\u003e Baseline tests are descriptive; confirmatory inferences rely on ANCOVA-adjusted posttest effects\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2. Primary Treatment Effects (ANCOVA)\u003c/h2\u003e \u003cp\u003ePrimary treatment effects were evaluated using \u003cb\u003estandard ANCOVA models\u003c/b\u003e of the form: posttest outcome\u0026thinsp;~\u0026thinsp;group\u0026thinsp;+\u0026thinsp;corresponding pretest covariate (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The homogeneity-of-slopes assumption was evaluated via a group \u0026times; pretest interaction term; interactions were non-significant for all three primary outcomes (p \u0026ge; .135), supporting standard ANCOVA interpretation. \u003cem\u003eHolm-adjusted p-values\u003c/em\u003e are reported for the family of three confirmatory outcomes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRobustness check.\u003c/b\u003e To examine whether conclusions were sensitive to heteroscedasticity, the ANCOVA models were re-estimated as OLS regressions with heteroscedasticity-consistent (HC3) standard errors and corresponding Wald tests in Python; the group effects remained statistically significant and substantively unchanged (\u003cb\u003eAppendix G\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eCognitive engagement (H1). Adjusting for baseline engagement, the metaverse group demonstrated substantially higher posttest cognitive engagement than the control group (Δadj\u0026thinsp;=\u0026thinsp;28.34, 95% CI [26.45, 30.22], p \u0026lt; .001; partial η\u0026sup2; = .928). The magnitude indicates a very large separation between groups at posttest; however, the estimate should be interpreted alongside the restricted posttest variability noted below (floor/ceiling effects), which can inflate effect-size metrics.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDigital burnout (H2).\u003c/b\u003e Adjusting for baseline burnout, the metaverse group reported markedly lower posttest burnout (Δadj\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;24.52, 95% CI [\u0026minus;\u0026thinsp;27.71, \u0026minus;\u0026thinsp;21.34], p \u0026lt; .001; partial η\u0026sup2; = .773). Total burnout was scored so that higher values indicate more burnout, with a possible range of 16\u0026ndash;64 (16 items scored 1\u0026ndash;4; academic-efficacy items reverse-keyed before computing totals; totals are raw sums with no rescaling). At posttest, the experimental group\u0026rsquo;s observed burnout distribution clustered near the scale floor (Exp post: M\u0026thinsp;=\u0026thinsp;25.37, SD\u0026thinsp;=\u0026thinsp;0.49, min\u0026ndash;max\u0026thinsp;=\u0026thinsp;25\u0026ndash;26; Ctrl post: M\u0026thinsp;=\u0026thinsp;49.89, SD\u0026thinsp;=\u0026thinsp;9.33, min\u0026ndash;max\u0026thinsp;=\u0026thinsp;38\u0026ndash;58), which compresses variance and yields very tight adjusted-mean CIs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eScoring verification.\u003c/b\u003e Academic Efficacy items were reverse-keyed prior to computing the total burnout score so that the total consistently reflects \u0026ldquo;more burnout\u0026thinsp;=\u0026thinsp;higher score.\u0026rdquo; Totals were computed as [sum/mean] of items and were not rescaled (i.e., no z-scoring or range transformation). A scoring audit (item direction checks and recomputation) and posttest distribution diagnostics are provided in \u003cb\u003eAppendix H\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCreative writing skills (H3).\u003c/b\u003e Adjusting for baseline writing, the metaverse group achieved higher posttest rubric scores (Δadj\u0026thinsp;=\u0026thinsp;8.23, 95% CI [7.45, 9.00], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; partial η\u0026sup2; = .866). Some rubric subdimensions exhibited \u003cem\u003eceiling or near-degenerate distributions\u003c/em\u003e at posttest, so the most stable inference is based on the \u003cem\u003etotal rubric score\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eANCOVA-Adjusted Posttest Effects for the Three Primary Outcomes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome (post, adjusted for pretest)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdj mean Control (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdj mean Experimental (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔadj Exp\u0026minus;Ctrl (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e(1,67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epartial η\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e (Holm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Engagement (post)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.52 [98.97, 102.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128.85 [127.77, 129.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.34 [26.45, 30.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e867.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Burnout (post)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.89 [46.72, 53.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.37 [25.15, 25.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;24.52 [\u0026minus;\u0026thinsp;27.71, \u0026minus;\u0026thinsp;21.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreative Writing Skills (post)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.61 [23.00, 24.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.84 [31.36, 32.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.23 [7.45, 9.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e434.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e Cognitive Engagement total range\u0026thinsp;=\u0026thinsp;\u003cb\u003e30\u0026ndash;150\u003c/b\u003e (subscales\u0026thinsp;=\u0026thinsp;\u003cb\u003e6\u0026ndash;30\u003c/b\u003e). Creative Writing total range\u0026thinsp;=\u0026thinsp;\u003cb\u003e7\u0026ndash;35\u003c/b\u003e. Adjusted means are estimated at the grand mean of the pretest covariate. Δadj\u0026thinsp;=\u0026thinsp;adjusted difference (Experimental\u0026thinsp;\u0026minus;\u0026thinsp;Control). Models use HC3 robust standard errors; \u003cem\u003ep\u003c/em\u003e-values are Holm-adjusted across the three primary outcomes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTaken together, the covariance-adjusted results provide convergent evidence that the FrameVR.io intervention was associated with higher cognitive engagement, higher creative writing performance, and lower digital burnout relative to traditional instruction, while also indicating measurement-range constraints that warrant cautious interpretation of effect magnitudes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3. Assumption Checks, Sensitivity Analyses, and Subscale Patterns\u003c/h2\u003e \u003cp\u003e \u003cb\u003eModel diagnostics.\u003c/b\u003e Residual normality tests suggested departures from normality for all three ANCOVA models (Shapiro\u0026ndash;Wilk \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Given the pronounced between-group shifts and restricted score ranges (especially for burnout and certain writing subdimensions), this pattern is expected and does not invalidate ANCOVA estimates under large, systematic effects; nonetheless, robust inference was prioritized.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHeteroscedasticity.\u003c/b\u003e Potential heteroscedasticity was addressed using \u003cem\u003eHC3 robust standard errors\u003c/em\u003e, which are less sensitive to unequal residual variance and influential points.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInfluence analysis.\u003c/b\u003e A sensitivity analysis excluding the most influential observation in the creative writing model (maximum Cook\u0026rsquo;s D \u0026asymp; .33) did not alter conclusions; the group effect remained \u003cem\u003ep\u003c/em\u003e \u0026lt; .001 and the adjusted mean difference remained substantively unchanged.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory subscales.\u003c/b\u003e Exploratory ANCOVAs (Holm-adjusted within each construct family) mirrored the total-score pattern for (a) \u003cem\u003eall five engagement dimensions\u003c/em\u003e (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and (b) all three burnout components (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). By contrast, several creative-writing subdimensions were near-constant at posttest (ceiling/degenerate distributions), so interpretation emphasizes the \u003cb\u003etotal rubric score\u003c/b\u003e as the most psychometrically stable outcome indicator in this dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4. Change-Score Associations Within the Metaverse Group (RQ4)\u003c/h2\u003e \u003cp\u003eTo examine whether improvements co-occurred \u003cem\u003ewithin the metaverse condition\u003c/em\u003e, we correlated individual change scores (Δ\u0026thinsp;=\u0026thinsp;post\u0026thinsp;\u0026minus;\u0026thinsp;pre) in engagement, burnout, and writing \u003cem\u003ewithin the experimental group only\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;35) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This avoids conflating trajectories across distinct instructional contexts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eChange-Score Correlations Within the Metaverse Group (n\u0026thinsp;=\u0026thinsp;35)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Cognitive Engagement\u0026thinsp;\u0026times;\u0026thinsp;Δ Digital Burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.37, 0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Cognitive Engagement\u0026thinsp;\u0026times;\u0026thinsp;Δ Creative Writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.28, 0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Digital Burnout\u0026thinsp;\u0026times;\u0026thinsp;Δ Creative Writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.47, 0.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Correlations are computed within the metaverse group only. 95% CIs are Fisher-\u003cem\u003ez\u003c/em\u003e transformed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNone of the three pairwise change-score correlations reached statistical significance (all \u003cem\u003ep\u003c/em\u003e \u0026ge; .362), and confidence intervals were wide. This pattern is likely influenced by \u003cem\u003erestricted posttest variance\u003c/em\u003e (burnout floor effects; ceiling/degenerate writing subdimensions), which attenuates correlations even when true relationships exist. Replication with larger samples, delayed posttests, and more discriminating measurement ranges is warranted.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Qualitative Analysis (Semi-Structured Interviews)\u003c/h2\u003e \u003cp\u003eQualitative data were generated from semi-structured interviews with 18 metaverse participants (\u0026asymp;\u0026thinsp;25% of the experimental cohort). The aim was to identify which features and experiences of FrameVR.io students perceived as influential\u0026mdash;positively or negatively\u0026mdash;for cognitive engagement, digital burnout, and creative writing development (RQ5).\u003c/p\u003e \u003cp\u003eInterviews were audio-recorded, transcribed verbatim, anonymized, and analyzed using \u003cem\u003ereflexive thematic analysis\u003c/em\u003e (Braun \u0026amp; Clarke, 2006). Analysis proceeded through (1) familiarization, (2) initial coding, (3) theme construction, (4) theme review, (5) theme naming/definition, and (6) reporting with illustrative excerpts. NVivo (QDAS) was used for systematic data management and traceable coding, but theme development followed the analytic logic of thematic analysis rather than software-driven frequency counts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eQualitative Themes Regarding Student Perceptions of the FrameVR.io Intervention\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey codes (wtd %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample quote(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Enhanced engagement through immersion and active participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmersive presence; active/playful learning (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;I felt like I was in the stories we were creating\u0026hellip; The immersion blocked out real-world distractions.\u0026rdquo; (Aisha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudents repeatedly described the metaverse as \u0026ldquo;attention-capturing,\u0026rdquo; framing engagement as presence\u0026thinsp;+\u0026thinsp;action. Immersion reduced off-task distraction and made writing feel event-like rather than routine.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Collaboration and social learning via avatar-mediated interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic collaboration; socially safer feedback (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;We could point to things in the virtual space.\u0026rdquo; (Banan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShared space enabled referential communication (pointing, showing, co-locating ideas), and avatars lowered social risk for some learners, supporting participation and peer feedback.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Perceived writing gains through visualization, embodiment, and ideation support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3D visualization; character embodiment; idea generation (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;My descriptive writing improved\u0026hellip; because I was visualizing things in 3D.\u0026rdquo; (Aisha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudents linked affordances to craft-level outcomes: richer description from spatial cues; stronger characterization from role-play; reduced writer\u0026rsquo;s block via novelty and prompts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Mixed burnout experiences shaped by task design and technical friction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergizing engagement vs. draining tech strain (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;If I was fighting the tech, it was draining.\u0026rdquo; (Karima)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudents distinguished \u0026ldquo;good fatigue\u0026rdquo; (productive immersion) from \u0026ldquo;bad fatigue\u0026rdquo; (lag, instability, eye strain). Burnout relief depended on smooth performance and well-scaffolded tasks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Valued affordances and concrete improvement needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3D spaces; whiteboards/objects; onboarding; text input (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ldquo;Better integration for individual text input.\u0026rdquo; (Banan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipants praised core affordances (space, objects, co-creation) but requested stability, better writing-input workflows, and clearer onboarding to prevent extraneous load.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. Percentages indicate the proportion of coded references (NVivo \u0026ldquo;references\u0026rdquo;) assigned to the theme out of the total coded references across all themes; they reflect coding density, not participant prevalence.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, the qualitative evidence indicates that the metaverse environment functioned as (a) an engagement amplifier through presence and agency, (b) a collaboration scaffold through shared referential space, and (c) a creativity trigger through visualization and novelty\u0026mdash;while also being vulnerable to load and strain when technical friction undermined flow.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a visual distillation of students\u0026rsquo; discourse about the FrameVR.io experience. High-salience terms (e.g., \u003cem\u003eactivities, changes, questions, virtual, metaverse, avatar, 3D, environment, tools\u003c/em\u003e) align with the thematic findings: students anchored their evaluations in the \u003cem\u003etasks they performed\u003c/em\u003e and the \u003cem\u003eplatform affordances\u003c/em\u003e that shaped those tasks. The co-occurrence of positively valenced terms (\u003cem\u003ehelpful, engaging, fun, inspired\u003c/em\u003e) alongside strain markers (\u003cem\u003echallenging, difficult, frustration, fatigue, exhausting\u003c/em\u003e) visually corroborates Theme 4\u0026rsquo;s central insight: perceived burnout reduction was not automatic\u0026mdash;it was contingent on technical stability and task design that minimized extraneous cognitive load.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Integration of Quantitative and Qualitative Findings\u003c/h2\u003e \u003cp\u003eIntegration focused on explaining \u003cem\u003emechanisms\u003c/em\u003e behind the strong quantitative group effects and clarifying why burnout outcomes were robust in aggregate yet mixed in experience.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConvergence for engagement and writing.\u003c/b\u003e Quantitative ANCOVA results showed very large adjusted posttest advantages for metaverse participants in cognitive engagement and writing performance. Qualitative themes converge strongly, attributing these gains to immersion (presence), agency (active exploration), and socially grounded collaboration, which collectively created conditions consistent with the study\u0026rsquo;s framework (SDT support, flow conditions, and reduced extraneous load when the system worked smoothly).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQualified convergence for burnout.\u003c/b\u003e Quantitatively, burnout was substantially lower in the metaverse group, but qualitative accounts introduce an important boundary condition: burnout relief depended on low technical friction and task designs that channel stimulation into purposeful writing, rather than interface management. In other words, the metaverse reduced burnout when it operated as a \u003cem\u003eresource-rich learning ecology\u003c/em\u003e (JD-R), but risked becoming a \u003cem\u003enet demand\u003c/em\u003e under instability or usability barriers.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAttenuated Change-Score Associations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe absence of significant within-metaverse change-score correlations is consistent with the qualitative account that many students converged toward similarly positive posttest states (compressed variance), and with the measurement constraints noted in the quantitative diagnostics (floor/ceiling effects). Thus, the mixed-methods pattern suggests strong mean-level benefits with attenuated individual-difference associations under restricted score ranges.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eIntegrated Summary of Evidence\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantitative evidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQualitative evidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrated interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge metaverse advantage (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmersion\u0026thinsp;+\u0026thinsp;agency reduced distraction; collaboration increased participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMechanism-consistent convergence: SDT\u0026thinsp;+\u0026thinsp;Flow conditions plausibly drove engagement gains.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreative writing skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge metaverse advantage (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001 on total)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisualization/embodiment improved description and characterization; novelty supported ideation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetaverse affordances mapped onto craft-level skills; strongest inference at total rubric level due to subscale ceilings.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge reduction in metaverse group (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBurnout relief when tasks were energizing; tech strain when unstable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBurnout reduction is contingent: the platform acts as JD-R \u0026ldquo;resource\u0026rdquo; under stable delivery, but may become a \u0026ldquo;demand\u0026rdquo; under friction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn sum, the mixed-methods evidence indicates that the FrameVR.io intervention produced strong, coherent improvements in engagement and writing outcomes, while reducing burnout overall\u0026mdash;but with clear implementation-sensitive conditions that determine whether the metaverse experience feels energizing or draining.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis mixed-methods study examined whether a FrameVR.io-mediated creative writing intervention could (a) increase cognitive engagement, (b) improve creative writing performance, and (c) reduce digital burnout relative to traditional instruction among undergraduate EFL students. Using a quasi-experimental intact-class design and confirmatory ANCOVA models with HC3 robust standard errors (complete-case N\u0026thinsp;=\u0026thinsp;70; 35/35), the quantitative results show very large adjusted posttest differences favoring the metaverse group for all three primary outcomes, with familywise error controlled via Holm adjustment. Qualitative interviews (n\u0026thinsp;=\u0026thinsp;18) converged strongly with these results while also clarifying boundary conditions\u0026mdash;particularly that burnout relief and sustained engagement depend on technical stability, onboarding, and task design that prevents extraneous cognitive load.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Enhanced Cognitive Engagement (RQ1 \u0026amp; H1)\u003c/h2\u003e \u003cp\u003eThe findings provide strong support for H1. After adjusting for pretest engagement, the metaverse group achieved markedly higher posttest cognitive engagement (Δadj\u0026thinsp;=\u0026thinsp;28.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; partial η\u0026sup2; = .928). Importantly, exploratory analyses indicated that this pattern extended across all engagement dimensions (attentional allocation, metacognitive self-regulation, active scholarly participation, propensity for creative challenge, and cognitive effort investment). This directly addresses the reviewers\u0026rsquo; concern about \u0026ldquo;broad\u0026rdquo; engagement claims by demonstrating \u003cem\u003edimension-level evidence\u003c/em\u003e rather than relying on global impressions.\u003c/p\u003e \u003cp\u003eThe qualitative data substantially explained \u003cem\u003ewhy\u003c/em\u003e engagement increased. Students repeatedly described heightened \u003cem\u003epresence and attentional capture\u003c/em\u003e (\u0026ldquo;felt like I was in the stories\u0026rdquo;), consistent with immersion-based engagement mechanisms reported in metaverse/VR learning research (e.g., immersion and sustained attention). Their descriptions of \u0026ldquo;active\u0026rdquo; and \u0026ldquo;playful\u0026rdquo; learning align with Flow Theory (clear goals, absorption, intrinsic reward) and Self-Determination Theory (autonomy via exploration/choice; competence via feedback and visible progress; relatedness via avatar-mediated collaboration). In short, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026rsquo;s pathway\u0026mdash;metaverse affordances \u0026rarr; SDT/flow-supportive conditions \u0026rarr; cognitive engagement\u0026mdash;was supported both statistically and experientially.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Improved Creative Writing Skills (RQ3 \u0026amp; H3)\u003c/h2\u003e \u003cp\u003eThe results support \u003cem\u003eH3\u003c/em\u003e: controlling for baseline writing performance, metaverse participants achieved higher posttest rubric totals (Δadj\u0026thinsp;=\u0026thinsp;8.23, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; partial η\u0026sup2; = .866). This is a key contribution because it links an immersive platform not only to engagement but to performance in a complex, higher-order outcome (creative writing), which reviewers correctly noted is often missing in metaverse studies focused on attitudes or general engagement.\u003c/p\u003e \u003cp\u003eQualitative themes illuminate plausible mechanisms. Students emphasized that 3D visualization enriched descriptive language and scene construction, while avatar embodiment supported character voice and perspective-taking. The environment also functioned as an \u0026ldquo;idea generator,\u0026rdquo; reducing writer\u0026rsquo;s block through novelty and situated prompts. These accounts align with creative cognition views of writing that emphasize ideation scaffolds, imagery, and iterative refinement. At the same time, consistent with the reanalysis notes, some subdimensions of the rubric exhibited ceiling or restricted variance at posttest, so the most stable inference is at the total score level\u0026mdash;a limitation we explicitly acknowledge (see \u0026sect;\u0026nbsp;6.6) and a priority for future work (more discriminating scoring ranges and delayed assessments).\u003c/p\u003e \u003cp\u003eA crucial reviewer-facing clarification: the writing samples were scored by independent raters blinded to group and time, reducing expectancy effects and strengthening internal validity for this rater-scored outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Reduced Digital Burnout (RQ2 \u0026amp; H2)\u003c/h2\u003e \u003cp\u003eThe study supports \u003cem\u003eH2\u003c/em\u003e at the total-score and component level. Adjusted posttest burnout was substantially lower in the metaverse group (Δadj\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;24.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; partial η\u0026sup2; = .773). This is notable because immersive environments can plausibly \u003cem\u003eincrease\u003c/em\u003e strain through technostress, sensory overload, or usability friction. Here, the overall direction was protective.\u003c/p\u003e \u003cp\u003eHowever, the mixed-methods evidence makes clear that this outcome is implementation-contingent. Qualitatively, many students described the experience as \u0026ldquo;energizing\u0026rdquo; and less exhausting because it replaced passive screen time with embodied, social, goal-directed activity. This interpretation aligns with the \u003cem\u003eJD-R model\u003c/em\u003e: the intervention appears to add meaningful \u003cem\u003eresources\u003c/em\u003e (social presence, agency, immediate feedback) that buffer demands, and with \u003cem\u003eCognitive Load Theory\u003c/em\u003e insofar as successful design may reduce extraneous load by making prompts contextual and concrete.\u003c/p\u003e \u003cp\u003eYet Theme 4 also recorded the opposing pathway: technical instability, friction in text input, and eye strain increased fatigue\u0026mdash;precisely the conditions under which the metaverse can become a \u003cem\u003enet demand\u003c/em\u003e rather than a resource. Quantitatively, the experimental group also showed \u003cem\u003efloor effects\u003c/em\u003e in posttest burnout totals, which compress variance and require cautious interpretation. Taken together, the best-supported claim is not that the metaverse \u0026ldquo;automatically eliminates burnout,\u0026rdquo; but that\u0026mdash;when technically stable and instructionally well-scaffolded\u0026mdash;it can reduce burnout relative to traditional delivery of the same writing curriculum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e6.4. Relationships Between Constructs (RQ4; H4a \u0026amp; H4b)\u003c/h2\u003e \u003cp\u003eIn response to reviewer concerns about correlational logic, the revised analysis tested change-score associations within the metaverse group only (Δ\u0026thinsp;=\u0026thinsp;post\u0026thinsp;\u0026minus;\u0026thinsp;pre), avoiding the methodological error of pooling across distinct instructional conditions. These within-group correlations were small and non-significant, with wide confidence intervals. This does not contradict the intervention effects; instead, it likely reflects \u003cem\u003erestricted posttest variance\u003c/em\u003e (burnout floor; writing subdimension ceilings) that attenuates correlations and reduces sensitivity to individual-difference coupling.\u003c/p\u003e \u003cp\u003eConceptually, the qualitative data still supports the causal story implied by Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: students often linked being more immersed and focused (engagement) with producing richer writing, and linked technical frustration (a demand) with reduced creative flow. Empirically, however, the most defensible RQ4 conclusion under the current measurement ranges is: \u003cem\u003ethe intervention shifts group means strongly, but individual change-to-change coupling could not be reliably detected\u003c/em\u003e in this sample. Future work should test mediation pathways using larger samples, more discriminating measures, log-based indicators (time-on-task, artifacts), and delayed posttests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e6.5. Impactful Features and Student Perceptions (RQ5)\u003c/h2\u003e \u003cp\u003eThemes 2 and 5 identify the affordances most responsible for perceived gains:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImmersive scenes\u0026thinsp;+\u0026thinsp;task boards\u003c/b\u003e that make prompts concrete (supporting attention and ideation).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAvatar-mediated interaction\u003c/b\u003e that increases social presence and reduces anxiety for some learners.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eShared whiteboards and 3D objects\u003c/b\u003e that enable joint planning and visually grounded collaboration.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAt the same time, students reported clear friction points: platform stability, onboarding, and writing-input workflows. These perceptions map directly onto CLT (extraneous load), and they explain why burnout effects can be conditional. This section also supports reviewers\u0026rsquo; requests for implementation transparency: the intervention is best described not as \u0026ldquo;the metaverse in general,\u0026rdquo; but as a specific six-week, task-sequenced FrameVR.io writing design with identifiable features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section2\"\u003e \u003ch2\u003e6.6. Contributions, Limitations, and Future Directions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eContributions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMethod\u0026ndash;results alignment\u003c/b\u003e: The revised results now match the prespecified analytic plan\u0026mdash;\u003cb\u003eANCOVA with robust inference\u003c/b\u003e\u0026mdash;addressing the primary methodological critique.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGranular engagement evidence\u003c/b\u003e: Effects are documented across engagement dimensions, not only total scores.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRater-scored writing with blinding\u003c/b\u003e: Independent blinded raters strengthen credibility for the writing outcome.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMixed-method mechanism clarity\u003c/b\u003e: Qualitative themes specify what worked (immersion, collaboration, visualization) and what can undermine benefits (technical friction).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImplementation evidence\u003c/b\u003e: The appendices add an implementation log structure (attendance/participation, artifacts) that strengthens fidelity claims.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations (explicitly acknowledging reviewers\u0026rsquo; key points).\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eQuasi-experimental intact classes\u003c/b\u003e: residual confounding (teacher/class effects) remains possible despite baseline comparability and covariance adjustment. Future studies should use randomization or hierarchical models with class/teacher effects when multiple classes are available.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComplete-case analysis (N\u0026thinsp;=\u0026thinsp;70)\u003c/b\u003e: attrition/matching loss is now transparently reported; however, missingness mechanisms cannot be fully ruled out without item-level and log-level data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMeasurement constraints\u003c/b\u003e: some outcomes show \u003cem\u003efloor/ceiling effects\u003c/em\u003e (burnout totals at posttest; writing subdimensions), which may inflate group separation and attenuate correlations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSelf-report outcomes\u003c/b\u003e: engagement and burnout rely on self-report; triangulation with platform logs and behavioral indicators is needed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eShort-term posttest\u003c/b\u003e: no delayed posttest was collected, limiting claims about retention and durability beyond immediate gains.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePlatform specificity\u003c/b\u003e: results are for a particular FrameVR.io configuration; generalization to other metaverse platforms should be cautious.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture directions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eInclude \u003cb\u003edelayed posttests\u003c/b\u003e (4\u0026ndash;8 weeks) to assess retention and novelty decay.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdd \u003cb\u003eplatform logs\u003c/b\u003e (time-on-task, artifact counts, posting frequency) to corroborate engagement and reduce shared-method bias.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCollect \u003cb\u003eitem-level responses\u003c/b\u003e for engagement and burnout to enable CFA, measurement invariance, and more defensible subscale modeling.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse \u003cb\u003emultilevel modeling\u003c/b\u003e in designs with multiple classes/teachers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePre-register analysis plans and specify multiplicity control for secondary outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study provides convergent evidence that a \u003cem\u003ecarefully task-designed FrameVR.io creative writing environment\u003c/em\u003e can meaningfully enhance EFL students\u0026rsquo; \u003cem\u003ecognitive engagement\u003c/em\u003e and \u003cem\u003ecreative writing performance\u003c/em\u003e while reducing \u003cem\u003edigital burnout\u003c/em\u003e relative to traditional instruction. Quantitatively, robust ANCOVA models show very large adjusted posttest differences favoring the metaverse group on all three primary outcomes, and qualitatively, students attribute gains to immersion, agency, visualization, and collaboration.\u003c/p\u003e \u003cp\u003eYet the findings also demonstrate that benefits are \u003cem\u003enot automatic\u003c/em\u003e. Technical instability, weak onboarding, and high-friction writing workflows can increase extraneous cognitive load and undermine well-being. Therefore, the most defensible interpretation is conditional: when the metaverse environment is technically reliable and instructionally scaffolded to support flow and autonomy, it can act as a net learning resource; when it is unstable or poorly scaffolded, it can become a net demand.\u003c/p\u003e \u003cp\u003e \u003cb\u003e7.1. Implications for Pedagogy and Practice\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDesign immersion as a writing scaffold, not decoration.\u003c/b\u003e Use thematically rich scenes to concretize prompts and support sensory description and setting construction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrioritize agency and visible progress.\u003c/b\u003e Tasks should require exploration, choice, and creation (objects, boards, plots) with clear goals and feedback.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eExploit embodiment for characterization.\u003c/b\u003e Avatar-based role-play and \u0026ldquo;character interviews\u0026rdquo; can support voice, stance, and perspective-taking.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTreat usability as pedagogy.\u003c/b\u003e Onboarding, stability checks, and low-friction text input are essential to prevent extraneous load and burnout.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBuild collaboration into artifacts.\u003c/b\u003e Shared whiteboards/objects enable grounded co-authoring; assess group outputs systematically.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMonitor strain proactively.\u003c/b\u003e Include micro-breaks, ergonomic guidance, and rapid technical support to reduce fatigue and frustration.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDocument fidelity.\u003c/b\u003e Maintain attendance and participation logs and an intervention checklist to support replicability and reviewer expectations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e7.2. Recommendations for Future Research\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReplication across sites and platforms\u003c/b\u003e with larger samples and multiple instructors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDelayed posttests\u003c/b\u003e to evaluate retention and novelty effects.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMechanism tests\u003c/b\u003e (mediation models) using logs\u0026thinsp;+\u0026thinsp;discriminating scales to model engagement \u0026rarr; writing via reduced burnout and improved self-regulation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComparative instructional designs\u003c/b\u003e (e.g., metaverse with/without collaboration; with/without embodiment) to isolate active ingredients.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eObjective indicators\u003c/b\u003e (time-on-task, artifact production, writing revision traces) to triangulate self-report.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEquity and access audits\u003c/b\u003e (device constraints, connectivity, usability barriers) as a core research component.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026quot;The data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was conducted in accordance with the ethical guidelines of the Supreme Council of Universities in Egypt (Correspondence dated 28/03/2023, Article 25). Ethical approval was obtained from the Faculty of Education, Beni-Suef University (Approval Ref. No. 26-06-2024). The research involved a minimal-risk educational intervention with university students and did not include any invasive, experimental, or clinical procedures on humans or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;\u0026quot;Informed consent was obtained from all participants prior to their involvement in this research study.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e The authors received no specific funding for this work.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdarkwah, M. 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Challenges facing English language teaching in Egyptian public schools. \u003cem\u003eInternational Journal of Instruction, 12\u003c/em\u003e(4), 657\u0026ndash;670. https://doi.org/10.29333/iji.2019.12441a\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"asian-pacific-journal-of-second-and-foreign-language-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jsfl","sideBox":"Learn more about [Asian-Pacific Journal of Second and Foreign Language Education](http://sfleducation.springeropen.com)","snPcode":"40862","submissionUrl":"https://submission.nature.com/new-submission/40862/3","title":"Asian-Pacific Journal of Second and Foreign Language Education","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metaverse, FrameVR.io, EFL, cognitive engagement, digital burnout, creative writing","lastPublishedDoi":"10.21203/rs.3.rs-8831860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8831860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrawing on a quasi-experimental pretest\u0026ndash;posttest design with intact undergraduate EFL classes (enrolled N\u0026thinsp;=\u0026thinsp;73; complete-case N\u0026thinsp;=\u0026thinsp;70; 35 FrameVR.io/metaverse, 35 control), this study examined whether a FrameVR.io browser-based social 3D creative-writing intervention enhances cognitive engagement and creative writing performance while reducing digital burnout. Over six weeks (90 minutes/week), both groups addressed comparable creative-writing objectives, differing primarily in learning ecology (FrameVR.io immersive, avatar-mediated collaboration vs. conventional classroom tasks). Primary effects were estimated using ANCOVA models predicting each posttest outcome from group while adjusting for the corresponding pretest score; heteroscedasticity-robust (HC3) standard errors were used, and Holm-adjusted p values controlled familywise error across the three confirmatory outcomes. Adjusted posttest means favored the FrameVR.io group for cognitive engagement (Δ_adj\u0026thinsp;=\u0026thinsp;28.34, p \u0026lt; .001, partial η\u0026sup2; = .928) and creative writing performance (Δ_adj\u0026thinsp;=\u0026thinsp;8.23, p \u0026lt; .001, partial η\u0026sup2; = .866), and indicated lower digital burnout (Δ_adj\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;24.52, p \u0026lt; .001, partial η\u0026sup2; = .773). Creative-writing scripts were scored by trained independent raters blinded to group and timepoint, strengthening internal validity for performance assessment. Semi-structured interviews with metaverse participants (n\u0026thinsp;=\u0026thinsp;18) converged with quantitative results, attributing benefits to immersion, collaboration, and idea visualization while noting intermittent technical friction. Interpretation is tempered by restricted score variability in some posttest measures and the nonrandomized intact-class design; replication with delayed posttests, item-level validation, and exposure metrics (e.g., time-on-task) is recommended.\u003c/p\u003e","manuscriptTitle":"The Potential of a FrameVR.io Metaverse Environment to Enhance Cognitive Engagement, Reduce Digital Burnout, and Develop Creative Writing Skills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 14:55:05","doi":"10.21203/rs.3.rs-8831860/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"293323732449038570450927931786843603430","date":"2026-05-17T21:25:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T08:18:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T13:37:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T13:32:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Asian-Pacific Journal of Second and Foreign Language Education","date":"2026-02-09T14:39:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"asian-pacific-journal-of-second-and-foreign-language-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jsfl","sideBox":"Learn more about [Asian-Pacific Journal of Second and Foreign Language Education](http://sfleducation.springeropen.com)","snPcode":"40862","submissionUrl":"https://submission.nature.com/new-submission/40862/3","title":"Asian-Pacific Journal of Second and Foreign Language Education","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89657cad-5469-41b5-b625-2466645375b9","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"293323732449038570450927931786843603430","date":"2026-05-17T21:25:44+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T14:55:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 14:55:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8831860","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8831860","identity":"rs-8831860","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}