Digital Literacy and Academic Performance in Visual Arts Teacher Education in Ghana

Digital Literacy and Academic Performance in Visual Arts Teacher Education in Ghana | 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 Digital Literacy and Academic Performance in Visual Arts Teacher Education in Ghana Prosper Setsoafia, Harry Baton Essel, Akosua Tachie-Menson, Julliet Appiah-Kubi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9667213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates how digital literacy relates to academic performance among pre-service visual arts teachers in Ghana, addressing the limited use of moderation models by examining whether gender conditions this relationship. A quantitative correlational design was applied to survey data from 258 pre-service teachers. Digital literacy and academic performance were measured using validated Likert-scale instruments. Data were analysed using partial least squares structural equation modelling (PLS-SEM). Digital literacy exerts a strong positive effect on academic performance (β = 0.685, p < 0.001), explaining substantial variance (R² = 0.526). Contrary to expectations, gender does not moderate this relationship (β = 0.130, p = 0.264), indicating comparable academic returns from digital literacy across male and female students. Findings suggest that digital literacy development should be prioritised in teacher education without overemphasising gender-based differentiation, while addressing infrastructural and pedagogical constraints in resource-constrained environments. By integrating moderation analysis and robustness testing within a discipline-specific context, this study challenges assumptions of gendered digital advantage and reframes digital literacy as a consistent predictor of academic performance across gender. Digital competency academic achievement Visual art education Higher education pre-service teachers gender connectivism network learning digital natives PLS-SEM Figures Figure 1 Figure 2 INTRODUCTION In the 21st century, digital literacy has become a critical component of learners' knowledge bases. This is particularly highlighted in the education sector, where digital literacy is a prerequisite for learning (Gutiérrez-Ángel et al., 2022 ; Spante et al., 2018 ), and its absence engenders a knowledge gap inconsistent with the era. Students are expected not only to access information digitally but to evaluate, synthesise, create, and communicate knowledge across networked platforms (Alsuwaiket, 2026 ; Essel et al., 2021 ; Xiao et al., 2026 ). The essence of the digitalisation drive is highlighted in the neo-anthropological space engendered by an information overflow from various digital sources, blurring boundaries among various disciplines (Raschke, 2003 ; Setsoafia et al., 2025 ). Digital literacy, as a survival skill in the information-laced ecosystem, helps learners find gold in gravel, detect fake information, and train themselves to differentiate facts from fiction (Reddy et al., 2020 ; van Laar et al., 2020 ). Consequently, students navigating the vast digital expanse without guidance require educators who serve as lighthouses, teachers proficient in digital literacy, to steer them safely through potential hazards. Digital literacy refers to the student’s intellectual ability to effectively and responsibly access, evaluate, create and communicate information digitally (Arslantas et al., 2024 ; Avinç & Doğan, 2024 ; Ng, 2012 ). Digital literacy encompasses other literacies such as ICT skills, Internet literacy, information literacy, and media literacy (Ahmed & Roche, 2021 ; Tinmaz et al., 2022 ). Digital literacy and digital competences are used interchangeably, creating what Ferrari ( 2013 ) called a “jargon jungle. The concept of digital literacy is most commonly used in research, whereas digital competence is mostly used in policy documents (Spante et al., 2018 ; Vodă et al., 2022 ). Notwithstanding the nuances of the jargons, these conceptualisations share comparable fundamental elements. Ng ( 2012 ) refers to digital literacy as the multiplicity of literacy associated with the use of digital technologies. Digital technology within the educational domain encompasses the technical, cognitive, and socio-emotional dimensions of learning (online or offline) with digital technologies. The ability of a student to adapt seamlessly to emerging or disruptive technologies signals a digitally literate person. Pre-service teachers can either serve as technology enablers in their future classrooms by being technology savvy or a constraint in the use of technology if they are not digitally literate (Alsuwaiket, 2026 ; Ng, 2012 ). Empirical studies operationalised of the tripartite digital literacy framework of Ng ( 2012 ) reports moderate to high digital literacy competencies (Chen, 2025 ; Salimi et al., 2025 ). This illuminates the concept of “Digital Nativeness”, thus the student is to harness their ubiquitous knowledge of digital technology in their academic endeavours. The average digital literacy competencies help manage risks associated with the digital technologies: technostress (Essel et al., 2021 ; Khlaif et al., 2022 ) nomophobia (Essel et al., 2021 ), cognitive overload/ offloading (Gerlich, 2025 ; Sweller et al., 2011 ), Google effects (Gong & Yang, 2024 ; Sparrow et al., 2011 ), Digital Obesity (Demir et al., 2023 ; Oniz et al., 2023 ) and improves student learning outcomes (Ardhiani et al., 2023 ; Li et al., 2025 ; Munir et al., 2024 ). The proliferation and adoption of digital tools have aroused students’ interest in using digital technologies in their academic endeavours, hence improving their learning outcomes (Mehrvarz et al., 2021 ; Wu & Yuan, 2023 ). This widespread use of digital tools in the educational setting highlighted the need for digital literacy for effective and efficient use of digital tools in this generation and sharing of knowledge (Ng, 2012 ; Salimi et al., 2025 ). Digital literacy has been found to positively impact students’ academic performance (Ding et al., 2024 ; Jeon & Kim, 2022 ). Academic performance in this context refers to students learning outputs that reflect their learning process vis-à-vis the institutional objective (Mehrvarz et al., 2021 ; Zakir et al., 2025 ). In the context of education, Academic performance and achievement are used interchangeably (Salimi et al., 2025 ) to refer to student learning outcomes. Empirical studies outside Ghana (a resource-constrained setting) find that digital literacy correlates positively with academic performance. Thus, a student with high digital literacy is able to collect verified information, communicate properly and use that information to achieve better learning outcomes in the e-permeated world (Holm, 2025 ; Ng, 2012 ; Zakir et al., 2025 ). Zakir et al. ( 2025 ) employed SEM to demonstrate that increase in students’ digital literacy is associated not only with greater digital engagement and self-efficacy but also with significant higher academic performance. This positive correlation between digital literacy and academic performance is reported in meta-analysis (Ardhiani et al., 2023 ; Lei et al., 2021 ; Li et al., 2025 ) and other studies (Ding et al., 2024 ; Holm, 2025 ), suggesting that stronger literacy in a digital environment generally aligns with higher academic outcomes. Contrary to the positive correlation between digital literacy and academic performance, other empirical studies report a non-significant correlation (Abbas et al., 2019 ; Munir et al., 2024 ; Rodafinos et al., 2024 ). This highlights the complex notion of digital literacy and how it is shaped by context, resources, access and individual characteristics rather than being uniformly beneficial (Ardhiani et al., 2023 ; Spante et al., 2018 ; Zakir et al., 2025 ). The complexity deepens when gender is introduced. Gender issues have become a prominent focus of education research, largely due to mounting evidence demonstrating the substantial influence of gender stereotypes on students' attitudes and behaviours, thereby affecting their learning experiences (De la Hoz Serrano et al., 2024 ; Lasfeto et al., 2024 ). Regarding students’ gender, research is abundant globally but rather contradictory. Some studies found no difference between male and female students (Itasanmi & Ajani, 2023 ; Omar et al., 2022 ; Zeng et al., 2022 ). Other studies found differences favouring males (Aslan, 2021 ; Rizal et al., 2021 ), while others found them favouring females (Huatay et al., 2023 ; Siddiq & Scherer, 2019 ). Gender inconsistency makes it challenging to generalise the influence of gender on digital literacy, necessitating further studies to establish a consensus. In Ghana, the empirical terrain is even more unsettled. Studies of university students and pre-service teachers indicate uneven digital literacy skills shaped by access to infrastructure, institutional supports and socio-economic background (Dzidzornu & Xu, 2025 ; Nkansah & Oldac, 2024 ; Setsoafia & Ng, 2025 ). Importantly, gender differences appear inconsistent. In the study by Ofosu-Koranteng et al.(2025), a gender disparity was observed, with female Economics students exhibiting greater digital competence than their male counterparts. Salifu et al. ( 2025 ) reported a counter-gender difference, with male students performing better than female students and in other studies gender disparity is negligible (Dzidzornu & Xu, 2025 ; Setsoafia et al., 2025 ). This contradiction raises substantial theoretical questions. Is gender merely associated with digital literacy levels, or does it fundamentally alter how digital literacy translates into academic performance? Noticeably, available studies in Ghana examine digital literacy descriptively, mapping access, frequency or perceived skills (Adarkwah & Huang, 2023 ; Nkansah & Oldac, 2024 ; Yarkwah et al., 2024 ) but stop short of modelling its structural relationship with measurable academic performance. Other related studies measure the relationship between digital literacy and other variables: technostress (Essel et al., 2021 ), information literacy (Akakpo et al., 2025 ), online migration services (Bokpin & Akakpo, 2024 ), and digital citizenship (Arkorful et al., 2024 ; Salifu et al., 2025 ). As a result, the field knows little about whether digital literacy operates uniformly across male and female pre-service teachers, particularly in discipline-specific contexts such as visual art education. This oversight matters because digital literacy functions differently in creative disciplines (Ceran, 2025 ; Kolyvas & Kostagiolas, 2024 ). Pre-service visual art teachers operate at the intersection of studio practice, visual culture, digital image, production and pedagogical design. Thus, digital literacy extends beyond information retrieval into visual communication, multimodal production and digital critique (Essuman et al., 2025 ; Setsoafia et al., 2025 ). If digital literacy enhances networked learning, as connectivism proposes, its academic payoff may depend on how effectively students leverage digital networks for creative and pedagogical purposes (Siemens, 2018 ). However, connectivism does not account for how socially structured expectations shape participation in these networks. Social role theory offers a complementary lens. It posits that gender differences vary regarding appropriate roles and competencies (Eagly & Wood, 2012 ). If technology use and access are socialised to perceive digital technologies as aligned with masculine roles, they may report higher confidence in digital literacy. Yet confidence does not automatically convert to academic achievement. Conversely, female students may exhibit lower reported digital confidence but stronger academic regulations, producing different performance patterns. This could explain why empirical findings on gender, digital literacy and academic performance frequently diverge, signalling a theoretical gap. Thus, if gender shapes how digital networks are accessed, interpreted, and valued, the academic relationship with digital literacy may vary systematically. Upon reviewing the related literature, it becomes clear that there is a focus on the link between digital literacy and academic performance, often overlooking the role of moderation. In Particular, the use of gender as a moderating factor in research models to evaluate the interrelationship between Digital literacy and academic performance is missing. Additionally, there is a scarcity of research assessing the effect of digital literacy on academic performance in developing countries, such as Ghana. Empirically, studies investigating this causal relationship among pre-service visual art teachers in a resource-constrained setting, such as Ghana, are also lacking. Drawing on connectivism and social role theory, we conceptualise digital literacy as a network-navigation capacity, whose academic consequences may be socially structural rather than universal. Using partial least squares structural equation modelling (PLS-SEM), we estimate both the direct effect of digital literacy on academic performance and the interaction effects of gender. The study seeks to address these research gaps by examining the connection between digital literacy and academic performance, with gender acting as a moderator (Fig. 1 ). The study contributes to the growing body of research on digital literacy and academic performance, most especially the moderating role of gender. This underscores the need for ongoing efforts to create an inclusive and equitable digital education environment. RESEARCH QUESTIONS AND HYPOTHESES What is the level of pre-service visual arts teachers’ digital literacy? To what degree will digital literacy predict the academic performance of pre-service visual art teachers? This study tested a set of hypotheses to determine whether Pre-service visual art teachers perceived level of digital literacy influences their academic performance and whether gender differences impact the relationship. The hypotheses and the results are discussed in detail in the results section. H 1 Digital literacy significantly influences the academic performance of pre-service visual art teachers H 2 Gender significantly moderates the relationship between digital literacy and pre-service visual art teachers’ academic performance. METHODS RESEARCH DESIGN This study employed a quantitative correlational research approach to examine the relationship between pre-service visual art teachers perceived digital literacy and their academic performance, and how this relationship was moderated by gender. A correlational research design was chosen to examine whether and to what extent perceived digital literacy influences academic performance, with gender as an interaction effect among pre-service teachers in Ghana (Creswell & Creswell, 2018 ). Despite the limitations of correlational studies, they are ideal for exploratory studies in which no variables are manipulated, and the focus is on how variables correlate with one another (Spector, 2019 ). An Online survey and convenience sampling have been employed in this study. This approach included recruiting accessible and willing participants and was appropriate given the fairly homogeneous population of pre-service Visual Arts teachers (Etikan, 2016 ). Convenience sampling was chosen for its practicality, cost-effectiveness and efficiency, especially in addressing the geographical scattering of colleges of education and minimising data entry errors in manual processing (Memon et al., 2025 ). The strategy is commonly employed in educational and teacher education research and is regarded as appropriate for studies that examine theoretical relationships rather than for generating population estimates (Nurzhanova et al., 2023 ). While convenience sampling limitations lie in the failure to achieve representativeness, prior research indicates that when appropriately managed, non-probability samples can produce reliable and meaningful results, especially in research focusing on theory testing in specified academic contexts (Berndt, 2020 ). Power analysis was conducted to determine the sample size for the [main] study using the G*Power software (Faul et al., 2009 ). At the significance level of 0.05 and a power of 0.95, the required minimum sample size was 129. We added 10% safety factor (Das & Datta, 2024 ), resulting in a minimum sample size of 142 valid responses. From the initial set of 364 responses (194 online and 170 offline), attention-check questions (Wang et al., 2023 ) screening removed 106 invalid responses, resulting in a final sample of 258 responses. Of the respondents, 179 (69%) were male, and 79 (31%) were female. The data analysis was conducted using SmartPLS 4 software (Ringle et al., 2024 ) for both the measurement and structural models. PLS-SEM was used to analyse the relationships among digital literacy, academic performance and the interaction effect to investigate the gender effects. PLS-SEM is a non-parametric technique and therefore does not require the assumption of multivariate normality. The dataset was examined for multivariate normality by using the WebPower analysis tool (Zhang & Yuan, 2018 ). Mardia’s multivariate skewness and kurtosis were significant, indicating non-normality; therefore, covariance-based SEM (CB-SEM) is not appropriate for the analysis. PLS-SEM can be applied in this study because it provides a greater predictive capacity than CB-SEM (J. F. J. Hair et al., 2019 ). The reflective measurement model was tested using PLS-SEM, to assess whether the hypothesised direct and moderating effects were acceptable, providing an insight into what sufficient conditions can trigger the results (J. F. Hair et al., 2020 ; Sarstedt et al., 2023 ) DATA COLLECTION TOOLS The study employed validated scales from previous studies. We adopted the Digital Literacy Scale by Ng ( 2012 ), which consists of 10 items to measure the digital literacy of pre-service visual arts teachers. The Cronbach’s Alpha of the scale is 0.86, indicating 40 per cent of the variance is explained. The items were measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Mehrvarz et al.'s ( 2021 ) four-item academic performance scale, with a reliability of 0.86, was used to measure students’ perceived academic performance. Students rated each item on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating greater perceived academic performance. RESULTS DESCRIPTIVES Table 1 shows the descriptive statistics and correlations among the study variables. The results indicate that digital literacy is positively and significantly associated with academic performance (r = 0.597, p .05) but has a weak positive relationship with academic performance (r = 0.149, p < .05). Table 1 descriptive statistics Variable Mean SD 1 2 3 1. Age 23.5 4.30 2 DL 5.26 1.10 0.005 3 AP 5.71 1.06 0.149* 0.597*** Note. * p < .05, ** p < .01, *** p < .001 “1.00-3.40 low, 3.40–5.20 moderate, 5.20-7.00 high” COMMON METHOD BIAS Prior to model estimation, the common method bias (CMB) was evaluated because the models had latent variables and shared data-collection procedures. CMB is also possible due to unclear questionnaires, which can lead to social desirability bias (Kock, 2015 ). The presence of CMB was excluded when inner VIFs were less than 3.3 (Kock & Lynn, 2012 ). MEASUREMENT MODEL ASSESSMENT The outer model, also known as the measurement model, is tested for reliability and validity through confirmatory factor analysis (CFA). This is done by evaluating indicator strength (outer loading ≥ 0.708, t-statistic = + 1.96), consistency (Cronbach’s alpha ≥ 0.70, CR ≥ 0.80) and convergent reliability (AVE ≥ 0.50) (J. F. J. Hair et al., 2019 ). Since Cronbach's alpha can be too liberal and composite reliability too liberal, ρA (rho_A > 0.70), proposed by Dijkstra and Henseler ( 2015 ) as an approximately precise metric, offers a practical compromise for construct reliability (Henseler et al., 2015 ). Each indicator variable had a loading above the proposed 0.708 threshold, and items that did not meet this threshold were deleted sequentially in descending order of loading. We retained DL_4, DL_5, DL_8, and DL_9, because their CRs are greater than 0.70 and their respective AVEs are greater than 0.50 (J. F. J. Hair et al., 2019 ). Convergent validity was established by demonstrating that each construct's average variance extracted (AVE) exceeded 0.50 (J.F.J. Hair et al., 2022 ). As shown in Table 2 , this value indicates that more than 50% of the variation in the measured items was explained by their respective constructs (J.F.J. Hair et al., 2019 ). Table 2 reliability and validity Construct Factor Loading VIF Cronbach’s Alpha Rho_a Rho_c AVE Academic performance 0.814 0.835 0.877 0.642 AP_1 0.847 1.993 AP_2 0.789 1.659 AP_3 0.846 1.833 AP_4 0.718 1.443 Digital literacy 0.841 0.853 0.880 0.513 DL_3 0.773 1.769 DL_4 0.695 1.696 DL_5 0.589 1.417 DL_6 0.797 2.091 DL_7 0.772 1.829 DL_8 0.673 1.674 DL_9 0.694 1.388 Discriminant validity was assessed to determine the distinctiveness of constructs within the structural model (J.F.J.Hair et al., 2019 ). We employed the heterotrait-monotrait (HTMT) method for this evaluation, as Henseler et al. ( 2015 ) deemed it more effective than the Fornell and Larcker (1981) method. Adequate discriminant validity is established when the HTMT ratio is significantly less than 1.0, typically below 0.90 (Henseler et al., 2016 ; Lim, 2024 ). HTMT values below the liberal benchmark of 0.90 for conceptually distinct constructs establish discriminant validity. Bootstrapping (10,000 samples) was performed to assess the significance of the HTMT values differing from 1.00. Table 3 demonstrates the discriminant validity assessment using the HTMT criterion and (2.5%,97.5%) bias-corrected confidence interval. The results of the measurement model indicate that the reliability and validity of all constructs met the proposed thresholds. Table 3 htmt criterion for discriminant validity AP DL 0.825 (0.675–0.938 Note: 2.5% and 97.5% bias-corrected intervals in parentheses. HTMT ˂0.90 STRUCTURAL MODEL The initial structural evaluation in PLS-SEM assesses collinearity (VIF ± 1.96 are ideal (J. F. J. Hair et al., 2019 ; Manley et al., 2021 ). Predictive relevance (Q² > 0) and the coefficient of determination (R² values explaining dependent variables) are also analysed (J. F. J. Hair et al., 2022 ). Preferred R² values are ≥ 0.1 for each construct pathway, with 0.75, 0.50, and 0.25 indicating substantial, moderate, and weak relationships, respectively. The f 2 effect size measures the significance of each of the independent variable (large 0.35, medium 0.15, small 0.02) (Nitzl et al., 2016 ). The structural model is intended to achieve good suitability and strong hypothesis testing, and acceptable model fit is indicated by an SRMR value less than 0.1 (Kock, 2020 ). It was found that the inner VIF was less than 5, ruling out multicollinearity as a source of model skewness (J. F. J. Hair et al., 2022 ; Mir & Dwivedi, 2023 ). The effect sizes (f 2 ) for the individual predictor constructs were also calculated. Table 4 presents the model's explanatory power and fit for the estimated model. Our findings indicate that most R 2 values were moderate (> .25), suggesting that the structural model accounted for a substantial proportion of the observed variance (J.F.J. Hair et al., 2019 ; Lim, 2024 ). The review of the f 2 values showed that digital literacy (DL) has a noteworthy effect on Academic performance (f 2 = .630). The f 2 value for the interaction effect of gender on DL→AP is negligible. Table 4 explanatory power and model fit Explanatory power: R 2 R 2 R 2 Adjusted AP 0.526 0.520 Effect size: f 2 AP DL 0.630 Gender*DL →AP 0.008 Predictive Power: Q 2 Q 2 AP 0.496 Model fit SRMR 0.088 The next procedure was to evaluate the size and significance of the structural path coefficients. We conducted a two-tailed, bias-corrected, accelerated bootstrap using 10,000 subsamples. Table 5 presents the structural model for the direct, indirect, total, and interaction effects. Figure 2 shows the model estimation results. According to the results, hypothesised direct relationships between exogenous and endogenous variables are statistically significant (p < .05). The findings demonstrate a significant effect of digital literacy on academic performance. However, the hypothesised moderating role of gender on the relationship between Digital literacy and academic performance (H 2 ) was not statistically significant, with a path coefficient of 0.130 and a p-value of 0.264. Thus, gender does not have a significant moderating effect on the relationship between digital literacy and academic performance. Table 5 structural model results BCCI Path Path coefficient T-statistic Lower Upper p-value Decision H 1 DL→AP 0.685 9.023 0.509 0.809 0.000 Supported H 2 Gender*DL→AP 0.130 1.117 -0.091 0.374 0.264 Unsupported Note: BCCI = bias-corrected confidence interval STRUCTURAL MODEL ROBUSTNESS TESTS To test for likely nonlinearities (see Table 6 ), we analysed the quadratic effect. The results of bootstrapping with 10000 samples indicate a significant relationship between DL and GenAI literacy (Sarstedt et al., 2020 ; Vaithilingam et al., 2024 ). Thus, the link between DL and AP is quadratic (β = -0.081, t = 1.987, p < 0.05), contrary to the hypothesised linear relationship. The assessment of potential endogeneity is based on the Gaussian copula approach (Hult et al., 2018 ; Vaithilingam et al., 2024 ). The combination of the Gaussian copula was insignificant (see Table 6 ). Hence, endogeneity is absent, supporting the robustness of the structural model (Sarstedt et al., 2020 ). Table 6 assessment of the nonlinear effect and the endogeneity test Effect Path coefficient T-value BCCI p-values lower Upper Quadratic effect QE(DL)→AP -0.081 1.987 -0.145 0.014 0.047 Gaussian Copula test GC(DL→AP) →AP -0.277 1.739 -0.613 0.025 0.082 Note: BCCI = bias-corrected confidence interval DISCUSSION The finding shows that the perceived digital literacy level of the pre-service teachers was high (x ̅ =5.26; SD = 1.10). These results forecast a promising sign with regard to the application of digital literacy skills and the fostering of a networked environment for the students by the prospective visual art teachers. In this case the prospective visual art teacher can serve as technology enablers in their future classrooms (Alsuwaiket, 2025; Ng 2012 ; Ustungag et al., 2017). This finding in line with empirical studies reports moderate to high digital literacy competencies (Atar & Bagci 2023; Aslan et al., 2025; Salimi et al., 2025 ). This illuminates the concept of “Digital Nativeness”, thus the student is able to harness their ubiquitous knowledge of digital technology in their academic endeavours (Ng, 2012 ). The results to aid the confirmation of H 1 presented a positive AP impact of DL. With the blistering growth of technology, the highly digitally literate individuals are capable of utilizing the information, collaborating, communicating and digital tools in order to improve their learning results (Lei et al., 2021 ; Mehrvarz et al., 2022; Ng, 2012 ). These outcomes were not different to earlier research, which has reported the positive effect of DL on AP (Ardhiani et al., 2023 ; Holm, 2024; Salimi et al., 2025 ). The outcome is the opposite of what was found in the literature (Abbas et al., 2019 ; Rodafinos et al., 2024 ) on this topic, which stated that the effects of DL are insignificant on the academic performance of students. This finding shows investing in learners’ digital literacy has a positive impact on their educational or learning outcomes. In other words, students with high digital literacy are confident in curating appropriate information resources [digital networks] to constructs new knowledge with impact their learning outcomes. While research on digital literacy and academic performance is extensive, studies that expressly include gender as a moderator are uncommon (Itasami & Ajani, 2023; Omar et al., 2022 ; Zeng et al., 2022 ). The study aims to explore the variations in digital literacy scores between male and females within a Ghana sample. The findings of this study appear to contradict previous research that has reported gender difference in digital literacy (Aslan, 2021 ; Huatay et al., 2023 ; Rizal et al., 2021 ; Siddiq & Scherer, 2019 ). The results of the moderation analysis showed that gender did not influence the relationship between digital literacy and academic performance. This result is supported by (Itasanmi & Ajani, 2023 ; Osaai, 2022; Omar et al., 2023; Zeng et al., 2022 ) who reported no gender digital divide. Thus, both male and females performed similarly in the digital literacy competencies. These findings contradict the studies that reported gender differences either favouring males (Aslan, 2021 ; Rizal et al., 2021 ), or females (Huatay et al., 2023 ; Issifu et al., 2025; Siddiq & Scherer, 2019 ). These varying results might be due to social, cultural dynamics and disparity in educational opportunities traditionally available to each gender. Notwithstanding, in the present, education is equally accessible to both male and females at the same level. The study contributes to the growing body of research on gender and digital competency and underscores the need for ongoing efforts to create inclusive and equitable digital education environment. IMPLICATIONS This study highlights the significant role of digital literacy in improving students’ academic performance. Digital literacy is the new currency of the digital age which enables students to leverage digital networks in their academic activities, thereby enhancing their academic performance. However, this could be impractical if the challenges of face by pre-service visual art teachers, such as unreliable internet access, inadequate access to ICT tools, insufficient ICT training, high cost of digital devices and data and low proficiency of advance digital tools. Higher education institutions should prioritise digital literacy and professional development programs to enhance the human capital of the pre-service visual art teachers. This investment is to enable student curate and leverage available digital networks and build new information more effectively, thereby translating into excellent learning outcomes. Notwithstanding the high digital literacy level reported, which illuminate their digital nativeness, it doesn’t automatically translate into the ethical use of the available technologies for education purpose. Hence the need for curated digital literacy training. In addition, though gender does not play a moderating role in the relationship between digital literacy and academic performance in this study, initial teacher education should prioritise the creation of an inclusive and supportive academic environment that enhance exploration and use of digital tools effectively and efficiently regardless of gender. This would minimise the tendency of socio-cultural traits associated with the use and adoption digital tool from creeping into the academic environment. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS Notwithstanding, the firm theoretical framework and methodology, this study recognises some shortcomings and provides roadmap for future studies. First and foremost, the use of quantitative research design and self-reporting used for this cross-sectional study highlights a probable operational limitation. The self-reported data is prone to social-desirability bias. Even though appropriate steps were made to control such biases, and the occurrence of the latter has not been statistically observed, the subjectivity of questionnaire data may still pose a threat to the results of the research. In order to enhance the quality of reliability and validity of the study, future scholars ought to focus on objective data (e.g., transcript of students) and use a mixed-methods research design in order to clarify the intricacies in the correlation between the two variables The research dataset was restricted to pre-service Visual Arts teachers in Ghana, and convenience sampling techniques were used. It is therefore recommended that further studies examine the study model with a different unit of analysis and a more varied sampling method to improve the generalisability of the outcomes. The design used is the cross-sectional survey design, which proves correlations between the variables but does not establish the time-based causation. Future studies may take a longitudinal design to capture the changes of variables with time to indicate the dynamic relationship as well as the cause-and-effect effects of the variables. Moreover, the relationship between digital literacy and academic performance in this study is not linear, this signals the need future studies on possible antecedents and consequence of digital literacy. CONCLUSION This study examined the relationship between digital literacy and academic performance among pre-service visual art teachers in Ghana. While research on digital literacy and academic performance is extensive, studies that expressly include gender as a moderator are uncommon. The explore the variations in digital literacy scores between male and females among the pre-service visual art teachers. Based on the survey data, this study employed structural equation modelling to analyse the hypothesis. Digital literacy has a significant positive impact on academic performance of pre-service visual art teachers. In addition, gender does not moderate the relationship between digital literacy and academic performance of the preservice visual art teachers. From a practical point the study highlights the need for investment into digital literacy trainings to improve the human capital of the pre-service visual art teachers. Declarations Informed consent: The authors confirms that Informed consent for publication was obtained from all participants/ respondents involved in the study. Participants/respondents were Informed about the study's purpose, voluntary nature, and their right to withdraw without penalty. All responses were confidential and used solely for research purposes. Ethical consideration and consent to participate The researchers assured the participants of the confidentiality of their responses and their willingness to withdraw from the study. Ethical approval was sought from and granted (Approval reference: HuSSREC/AP/235/VOL.4; Approval date: 3rd July, 2025) by the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. The study was implemented in accordance with the Helsinki Declaration. Competing interest The authors declare no competing interests. Funding statement No funds, grants, or other support were received Author Contribution PS: Conceptualization, Methodology, Formal analysis, Data curation, Investigation, Visualization, Writing – original draft; Writing – review & editing; Project administration; HBE: Conceptualization, Methodology, Formal analysis, Supervision; Validation, Writing – review & editing; ATM: Methodology, Validation, Supervision, Writing – review & editing; JAK: Investigation, Data curation, Writing – review & editing; CAK: Validation, Visualization, Writing – review & editing; MN: Investigation; Validation, Data curation; EBA: Methodology; Writing – review & editing; GAA: Resources, Writing – review & editing. All authors have read and agreed to the published version of this manuscript. Data Availability The dataset used and/or analysed during the current study are available from the corresponding author on reasonable request. References Abbas, Q., Hussain, S., & Rasool, S. (2019). 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"Atta","lastName":"Koduah","suffix":""},{"id":638009600,"identity":"4c1a4ae5-7cdb-4733-b374-d8b94985d28d","order_by":5,"name":"Macharious Nabang","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Macharious","middleName":"","lastName":"Nabang","suffix":""},{"id":638009601,"identity":"df1a0d41-acf3-43be-8762-708f33ed131c","order_by":6,"name":"Edmund Boamah Acheampong","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Edmund","middleName":"Boamah","lastName":"Acheampong","suffix":""},{"id":638009607,"identity":"90e565e0-6194-464a-8de7-d8f2c1041c3d","order_by":7,"name":"George Attah Aboagye","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"Attah","lastName":"Aboagye","suffix":""}],"badges":[],"createdAt":"2026-05-10 03:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9667213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9667213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109115011,"identity":"065ef388-e0f8-4b9d-b933-73be263c2c71","added_by":"auto","created_at":"2026-05-12 16:13:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37156,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eresearch model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9667213/v1/aa6c791558c1992c5c3ad8f3.jpeg"},{"id":109115013,"identity":"4b5fa76b-a7ea-4706-8730-11ec88779051","added_by":"auto","created_at":"2026-05-12 16:13:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emodel estimation results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9667213/v1/cb884d92fdbb2c6f4196aeda.png"},{"id":109115032,"identity":"4fd32095-3b1b-4658-8293-83378ee7558d","added_by":"auto","created_at":"2026-05-12 16:13:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9667213/v1/5344dab1-1798-4338-b50d-2cc17bc06f1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Literacy and Academic Performance in Visual Arts Teacher Education in Ghana","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn the 21st century, digital literacy has become a critical component of learners' knowledge bases. This is particularly highlighted in the education sector, where digital literacy is a prerequisite for learning (Gutiérrez-Ángel et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spante et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), and its absence engenders a knowledge gap inconsistent with the era. Students are expected not only to access information digitally but to evaluate, synthesise, create, and communicate knowledge across networked platforms (Alsuwaiket, \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e; Essel et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiao et al., \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e). The essence of the digitalisation drive is highlighted in the neo-anthropological space engendered by an information overflow from various digital sources, blurring boundaries among various disciplines (Raschke, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Setsoafia et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Digital literacy, as a survival skill in the information-laced ecosystem, helps learners find gold in gravel, detect fake information, and train themselves to differentiate facts from fiction (Reddy et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; van Laar et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, students navigating the vast digital expanse without guidance require educators who serve as lighthouses, teachers proficient in digital literacy, to steer them safely through potential hazards.\u003c/p\u003e \u003cp\u003eDigital literacy refers to the student’s intellectual ability to effectively and responsibly access, evaluate, create and communicate information digitally (Arslantas et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Avinç \u0026amp; Doğan, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ng, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Digital literacy encompasses other literacies such as ICT skills, Internet literacy, information literacy, and media literacy (Ahmed \u0026amp; Roche, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tinmaz et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital literacy and digital competences are used interchangeably, creating what Ferrari (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) called a “jargon jungle. The concept of digital literacy is most commonly used in research, whereas digital competence is mostly used in policy documents (Spante et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vodă et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notwithstanding the nuances of the jargons, these conceptualisations share comparable fundamental elements. Ng (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) refers to digital literacy as the multiplicity of literacy associated with the use of digital technologies. Digital technology within the educational domain encompasses the technical, cognitive, and socio-emotional dimensions of learning (online or offline) with digital technologies. The ability of a student to adapt seamlessly to emerging or disruptive technologies signals a digitally literate person. Pre-service teachers can either serve as technology enablers in their future classrooms by being technology savvy or a constraint in the use of technology if they are not digitally literate (Alsuwaiket, \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e; Ng, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Empirical studies operationalised of the tripartite digital literacy framework of Ng (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) reports moderate to high digital literacy competencies (Chen, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salimi et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This illuminates the concept of “Digital Nativeness”, thus the student is to harness their ubiquitous knowledge of digital technology in their academic endeavours. The average digital literacy competencies help manage risks associated with the digital technologies: technostress (Essel et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khlaif et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) nomophobia (Essel et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), cognitive overload/ offloading (Gerlich, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sweller et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), Google effects (Gong \u0026amp; Yang, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sparrow et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), Digital Obesity (Demir et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oniz et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) and improves student learning outcomes (Ardhiani et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Munir et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The proliferation and adoption of digital tools have aroused students’ interest in using digital technologies in their academic endeavours, hence improving their learning outcomes (Mehrvarz et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu \u0026amp; Yuan, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This widespread use of digital tools in the educational setting highlighted the need for digital literacy for effective and efficient use of digital tools in this generation and sharing of knowledge (Ng, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Salimi et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Digital literacy has been found to positively impact students’ academic performance (Ding et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jeon \u0026amp; Kim, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Academic performance in this context refers to students learning outputs that reflect their learning process vis-à-vis the institutional objective (Mehrvarz et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zakir et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the context of education, Academic performance and achievement are used interchangeably (Salimi et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) to refer to student learning outcomes.\u003c/p\u003e \u003cp\u003eEmpirical studies outside Ghana (a resource-constrained setting) find that digital literacy correlates positively with academic performance. Thus, a student with high digital literacy is able to collect verified information, communicate properly and use that information to achieve better learning outcomes in the e-permeated world (Holm, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ng, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zakir et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Zakir et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) employed SEM to demonstrate that increase in students’ digital literacy is associated not only with greater digital engagement and self-efficacy but also with significant higher academic performance. This positive correlation between digital literacy and academic performance is reported in meta-analysis (Ardhiani et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lei et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) and other studies (Ding et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Holm, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), suggesting that stronger literacy in a digital environment generally aligns with higher academic outcomes. Contrary to the positive correlation between digital literacy and academic performance, other empirical studies report a non-significant correlation (Abbas et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Munir et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rodafinos et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highlights the complex notion of digital literacy and how it is shaped by context, resources, access and individual characteristics rather than being uniformly beneficial (Ardhiani et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Spante et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zakir et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The complexity deepens when gender is introduced. Gender issues have become a prominent focus of education research, largely due to mounting evidence demonstrating the substantial influence of gender stereotypes on students' attitudes and behaviours, thereby affecting their learning experiences (De la Hoz Serrano et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lasfeto et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Regarding students’ gender, research is abundant globally but rather contradictory. Some studies found no difference between male and female students (Itasanmi \u0026amp; Ajani, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Omar et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zeng et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other studies found differences favouring males (Aslan, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rizal et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), while others found them favouring females (Huatay et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Siddiq \u0026amp; Scherer, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Gender inconsistency makes it challenging to generalise the influence of gender on digital literacy, necessitating further studies to establish a consensus.\u003c/p\u003e \u003cp\u003eIn Ghana, the empirical terrain is even more unsettled. Studies of university students and pre-service teachers indicate uneven digital literacy skills shaped by access to infrastructure, institutional supports and socio-economic background (Dzidzornu \u0026amp; Xu, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nkansah \u0026amp; Oldac, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Setsoafia \u0026amp; Ng, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Importantly, gender differences appear inconsistent. In the study by Ofosu-Koranteng et al.(2025), a gender disparity was observed, with female Economics students exhibiting greater digital competence than their male counterparts. Salifu et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported a counter-gender difference, with male students performing better than female students and in other studies gender disparity is negligible (Dzidzornu \u0026amp; Xu, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setsoafia et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This contradiction raises substantial theoretical questions. Is gender merely associated with digital literacy levels, or does it fundamentally alter how digital literacy translates into academic performance?\u003c/p\u003e \u003cp\u003eNoticeably, available studies in Ghana examine digital literacy descriptively, mapping access, frequency or perceived skills (Adarkwah \u0026amp; Huang, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nkansah \u0026amp; Oldac, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yarkwah et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) but stop short of modelling its structural relationship with measurable academic performance. Other related studies measure the relationship between digital literacy and other variables: technostress (Essel et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), information literacy (Akakpo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), online migration services (Bokpin \u0026amp; Akakpo, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), and digital citizenship (Arkorful et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salifu et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, the field knows little about whether digital literacy operates uniformly across male and female pre-service teachers, particularly in discipline-specific contexts such as visual art education. This oversight matters because digital literacy functions differently in creative disciplines (Ceran, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kolyvas \u0026amp; Kostagiolas, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Pre-service visual art teachers operate at the intersection of studio practice, visual culture, digital image, production and pedagogical design. Thus, digital literacy extends beyond information retrieval into visual communication, multimodal production and digital critique (Essuman et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setsoafia et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIf digital literacy enhances networked learning, as connectivism proposes, its academic payoff may depend on how effectively students leverage digital networks for creative and pedagogical purposes (Siemens, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, connectivism does not account for how socially structured expectations shape participation in these networks. Social role theory offers a complementary lens. It posits that gender differences vary regarding appropriate roles and competencies (Eagly \u0026amp; Wood, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). If technology use and access are socialised to perceive digital technologies as aligned with masculine roles, they may report higher confidence in digital literacy. Yet confidence does not automatically convert to academic achievement. Conversely, female students may exhibit lower reported digital confidence but stronger academic regulations, producing different performance patterns. This could explain why empirical findings on gender, digital literacy and academic performance frequently diverge, signalling a theoretical gap. Thus, if gender shapes how digital networks are accessed, interpreted, and valued, the academic relationship with digital literacy may vary systematically.\u003c/p\u003e \u003cp\u003eUpon reviewing the related literature, it becomes clear that there is a focus on the link between digital literacy and academic performance, often overlooking the role of moderation. In Particular, the use of gender as a moderating factor in research models to evaluate the interrelationship between Digital literacy and academic performance is missing. Additionally, there is a scarcity of research assessing the effect of digital literacy on academic performance in developing countries, such as Ghana. Empirically, studies investigating this causal relationship among pre-service visual art teachers in a resource-constrained setting, such as Ghana, are also lacking. Drawing on connectivism and social role theory, we conceptualise digital literacy as a network-navigation capacity, whose academic consequences may be socially structural rather than universal. Using partial least squares structural equation modelling (PLS-SEM), we estimate both the direct effect of digital literacy on academic performance and the interaction effects of gender. The study seeks to address these research gaps by examining the connection between digital literacy and academic performance, with gender acting as a moderator (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The study contributes to the growing body of research on digital literacy and academic performance, most especially the moderating role of gender. This underscores the need for ongoing efforts to create an inclusive and equitable digital education environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRESEARCH QUESTIONS AND HYPOTHESES\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the level of pre-service visual arts teachers’ digital literacy?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what degree will digital literacy predict the academic performance of pre-service visual art teachers?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThis study tested a set of hypotheses to determine whether Pre-service visual art teachers perceived level of digital literacy influences their academic performance and whether gender differences impact the relationship. The hypotheses and the results are discussed in detail in the results section.\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e Digital literacy significantly influences the academic performance of pre-service visual art teachers\u003c/p\u003e\n\u003cp\u003e \u003cem\u003eH\u003c/em\u003e \u003csub\u003e \u003cem\u003e2\u003c/em\u003e \u003c/sub\u003e \u003cem\u003eGender significantly moderates the relationship between digital literacy and pre-service visual art teachers’ academic performance.\u003c/em\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch2\u003eRESEARCH DESIGN\u003c/h2\u003e\u003cp\u003eThis study employed a quantitative correlational research approach to examine the relationship between pre-service visual art teachers perceived digital literacy and their academic performance, and how this relationship was moderated by gender. A correlational research design was chosen to examine whether and to what extent perceived digital literacy influences academic performance, with gender as an interaction effect among pre-service teachers in Ghana (Creswell \u0026amp; Creswell, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the limitations of correlational studies, they are ideal for exploratory studies in which no variables are manipulated, and the focus is on how variables correlate with one another (Spector, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). An Online survey and convenience sampling have been employed in this study.\u003c/p\u003e\u003cp\u003eThis approach included recruiting accessible and willing participants and was appropriate given the fairly homogeneous population of pre-service Visual Arts teachers (Etikan, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Convenience sampling was chosen for its practicality, cost-effectiveness and efficiency, especially in addressing the geographical scattering of colleges of education and minimising data entry errors in manual processing (Memon et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The strategy is commonly employed in educational and teacher education research and is regarded as appropriate for studies that examine theoretical relationships rather than for generating population estimates (Nurzhanova et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). While convenience sampling limitations lie in the failure to achieve representativeness, prior research indicates that when appropriately managed, non-probability samples can produce reliable and meaningful results, especially in research focusing on theory testing in specified academic contexts (Berndt, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Power analysis was conducted to determine the sample size for the [main] study using the G*Power software (Faul et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). At the significance level of 0.05 and a power of 0.95, the required minimum sample size was 129. We added 10% safety factor (Das \u0026amp; Datta, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), resulting in a minimum sample size of 142 valid responses. From the initial set of 364 responses (194 online and 170 offline), attention-check questions (Wang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) screening removed 106 invalid responses, resulting in a final sample of 258 responses. Of the respondents, 179 (69%) were male, and 79 (31%) were female.\u003c/p\u003e\u003cp\u003eThe data analysis was conducted using SmartPLS 4 software (Ringle et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) for both the measurement and structural models. PLS-SEM was used to analyse the relationships among digital literacy, academic performance and the interaction effect to investigate the gender effects. PLS-SEM is a non-parametric technique and therefore does not require the assumption of multivariate normality. The dataset was examined for multivariate normality by using the WebPower analysis tool (Zhang \u0026amp; Yuan, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mardia’s multivariate skewness and kurtosis were significant, indicating non-normality; therefore, covariance-based SEM (CB-SEM) is not appropriate for the analysis. PLS-SEM can be applied in this study because it provides a greater predictive capacity than CB-SEM (J. F. J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The reflective measurement model was tested using PLS-SEM, to assess whether the hypothesised direct and moderating effects were acceptable, providing an insight into what sufficient conditions can trigger the results (J. F. Hair et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sarstedt et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003ch3\u003eDATA COLLECTION TOOLS\u003c/h3\u003e\u003cp\u003eThe study employed validated scales from previous studies. We adopted the Digital Literacy Scale by Ng (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), which consists of 10 items to measure the digital literacy of pre-service visual arts teachers. The Cronbach’s Alpha of the scale is 0.86, indicating 40 per cent of the variance is explained. The items were measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Mehrvarz et al.'s (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) four-item academic performance scale, with a reliability of 0.86, was used to measure students’ perceived academic performance. Students rated each item on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores indicating greater perceived academic performance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDESCRIPTIVES\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics and correlations among the study variables. The results indicate that digital literacy is positively and significantly associated with academic performance (r\u0026thinsp;=\u0026thinsp;0.597, p \u0026lt; .001), suggesting that higher levels of digital literacy are correlated with better academic outcomes. Age shows no significant relationship with digital literacy (r\u0026thinsp;=\u0026thinsp;0.005, p \u0026gt; .05) but has a weak positive relationship with academic performance (r\u0026thinsp;=\u0026thinsp;0.149, p \u0026lt; .05).\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\u003edescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 DL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. * p \u0026lt; .05, ** p \u0026lt; .01, *** p \u0026lt; .001 \u0026ldquo;1.00-3.40 low, 3.40\u0026ndash;5.20 moderate, 5.20-7.00 high\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCOMMON METHOD BIAS\u003c/h2\u003e \u003cp\u003ePrior to model estimation, the common method bias (CMB) was evaluated because the models had latent variables and shared data-collection procedures. CMB is also possible due to unclear questionnaires, which can lead to social desirability bias (Kock, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The presence of CMB was excluded when inner VIFs were less than 3.3 (Kock \u0026amp; Lynn, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMEASUREMENT MODEL ASSESSMENT\u003c/h3\u003e\n\u003cp\u003eThe outer model, also known as the measurement model, is tested for reliability and validity through confirmatory factor analysis (CFA). This is done by evaluating indicator strength (outer loading\u0026thinsp;\u0026ge;\u0026thinsp;0.708, t-statistic\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.96), consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;\u0026ge;\u0026thinsp;0.70, CR\u0026thinsp;\u0026ge;\u0026thinsp;0.80) and convergent reliability (AVE\u0026thinsp;\u0026ge;\u0026thinsp;0.50) (J. F. J. Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since Cronbach's alpha can be too liberal and composite reliability too liberal, ρA (rho_A\u0026thinsp;\u0026gt;\u0026thinsp;0.70), proposed by Dijkstra and Henseler (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as an approximately precise metric, offers a practical compromise for construct reliability (Henseler et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Each indicator variable had a loading above the proposed 0.708 threshold, and items that did not meet this threshold were deleted sequentially in descending order of loading. We retained DL_4, DL_5, DL_8, and DL_9, because their CRs are greater than 0.70 and their respective AVEs are greater than 0.50 (J. F. J. Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Convergent validity was established by demonstrating that each construct's average variance extracted (AVE) exceeded 0.50 (J.F.J. Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, this value indicates that more than 50% of the variation in the measured items was explained by their respective constructs (J.F.J. Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\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\u003ereliability and validity\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRho_a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRho_c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL_9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was assessed to determine the distinctiveness of constructs within the structural model (J.F.J.Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We employed the heterotrait-monotrait (HTMT) method for this evaluation, as Henseler et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) deemed it more effective than the Fornell and Larcker (1981) method. Adequate discriminant validity is established when the HTMT ratio is significantly less than 1.0, typically below 0.90 (Henseler et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lim, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). HTMT values below the liberal benchmark of 0.90 for conceptually distinct constructs establish discriminant validity. Bootstrapping (10,000 samples) was performed to assess the significance of the HTMT values differing from 1.00. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the discriminant validity assessment using the HTMT criterion and (2.5%,97.5%) bias-corrected confidence interval. The results of the measurement model indicate that the reliability and validity of all constructs met the proposed thresholds.\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\u003ehtmt criterion for discriminant validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.825\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(0.675\u0026ndash;0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: 2.5% and 97.5% bias-corrected intervals in parentheses. HTMT ˂0.90\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSTRUCTURAL MODEL\u003c/h3\u003e\n\u003cp\u003eThe initial structural evaluation in PLS-SEM assesses collinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5), significance, and correlations in hypothesis testing (p-values, beta weights). Hypotheses with a t-statistic\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 are ideal (J. F. J. Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Manley et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Predictive relevance (Q\u0026sup2; \u0026gt; 0) and the coefficient of determination (R\u0026sup2; values explaining dependent variables) are also analysed (J. F. J. Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Preferred R\u0026sup2; values are \u0026ge;\u0026thinsp;0.1 for each construct pathway, with 0.75, 0.50, and 0.25 indicating substantial, moderate, and weak relationships, respectively. The f \u003csup\u003e2\u003c/sup\u003e effect size measures the significance of each of the independent variable (large 0.35, medium 0.15, small 0.02) (Nitzl et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The structural model is intended to achieve good suitability and strong hypothesis testing, and acceptable model fit is indicated by an SRMR value less than 0.1 (Kock, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was found that the inner VIF was less than 5, ruling out multicollinearity as a source of model skewness (J. F. J. Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mir \u0026amp; Dwivedi, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The effect sizes (f\u003csup\u003e2\u003c/sup\u003e) for the individual predictor constructs were also calculated.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the model's explanatory power and fit for the estimated model. Our findings indicate that most R\u003csup\u003e2\u003c/sup\u003e values were moderate (\u0026gt;\u0026thinsp;.25), suggesting that the structural model accounted for a substantial proportion of the observed variance (J.F.J. Hair et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lim, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The review of the f\u003csup\u003e2\u003c/sup\u003e values showed that digital literacy (DL) has a noteworthy effect on Academic performance (f\u003csup\u003e2\u003c/sup\u003e= .630). The f\u003csup\u003e2\u003c/sup\u003e value for the interaction effect of gender on DL\u0026rarr;AP is negligible.\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\u003eexplanatory power and model fit\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eExplanatory power: R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEffect size: f\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender*DL \u0026rarr;AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictive Power: Q\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\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\u003eThe next procedure was to evaluate the size and significance of the structural path coefficients. We conducted a two-tailed, bias-corrected, accelerated bootstrap using 10,000 subsamples. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the structural model for the direct, indirect, total, and interaction effects. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the model estimation results. According to the results, hypothesised direct relationships between exogenous and endogenous variables are statistically significant (p \u0026lt; .05). The findings demonstrate a significant effect of digital literacy on academic performance. However, the hypothesised moderating role of gender on the relationship between Digital literacy and academic performance (H\u003csub\u003e2\u003c/sub\u003e) was not statistically significant, with a path coefficient of 0.130 and a p-value of 0.264. Thus, gender does not have a significant moderating effect on the relationship between digital literacy and academic performance.\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\u003estructural model results\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=\"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 \u003cdiv align=\"left\" 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\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBCCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e DL\u0026rarr;AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e Gender*DL\u0026rarr;AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnsupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: BCCI\u0026thinsp;=\u0026thinsp;bias-corrected confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSTRUCTURAL MODEL ROBUSTNESS TESTS\u003c/h2\u003e \u003cp\u003eTo test for likely nonlinearities (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), we analysed the quadratic effect. The results of bootstrapping with 10000 samples indicate a significant relationship between DL and GenAI literacy (Sarstedt et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vaithilingam et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the link between DL and AP is quadratic (β = -0.081, t\u0026thinsp;=\u0026thinsp;1.987, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), contrary to the hypothesised linear relationship. The assessment of potential endogeneity is based on the Gaussian copula approach (Hult et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vaithilingam et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The combination of the Gaussian copula was insignificant (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Hence, endogeneity is absent, supporting the robustness of the structural model (Sarstedt et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eassessment of the nonlinear effect and the endogeneity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePath coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBCCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eQuadratic effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQE(DL)\u0026rarr;AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGaussian Copula test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC(DL\u0026rarr;AP) \u0026rarr;AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: BCCI\u0026thinsp;=\u0026thinsp;bias-corrected confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe finding shows that the perceived digital literacy level of the pre-service teachers was high (x ̅ =5.26; SD\u0026thinsp;=\u0026thinsp;1.10). These results forecast a promising sign with regard to the application of digital literacy skills and the fostering of a networked environment for the students by the prospective visual art teachers. In this case the prospective visual art teacher can serve as technology enablers in their future classrooms (Alsuwaiket, 2025; Ng \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ustungag et al., 2017). This finding in line with empirical studies reports moderate to high digital literacy competencies (Atar \u0026amp; Bagci 2023; Aslan et al., 2025; Salimi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This illuminates the concept of \u0026ldquo;Digital Nativeness\u0026rdquo;, thus the student is able to harness their ubiquitous knowledge of digital technology in their academic endeavours (Ng, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results to aid the confirmation of H\u003csub\u003e1\u003c/sub\u003e presented a positive AP impact of DL. With the blistering growth of technology, the highly digitally literate individuals are capable of utilizing the information, collaborating, communicating and digital tools in order to improve their learning results (Lei et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mehrvarz et al., 2022; Ng, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These outcomes were not different to earlier research, which has reported the positive effect of DL on AP (Ardhiani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holm, 2024; Salimi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The outcome is the opposite of what was found in the literature (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rodafinos et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on this topic, which stated that the effects of DL are insignificant on the academic performance of students. This finding shows investing in learners\u0026rsquo; digital literacy has a positive impact on their educational or learning outcomes. In other words, students with high digital literacy are confident in curating appropriate information resources [digital networks] to constructs new knowledge with impact their learning outcomes.\u003c/p\u003e \u003cp\u003eWhile research on digital literacy and academic performance is extensive, studies that expressly include gender as a moderator are uncommon (Itasami \u0026amp; Ajani, 2023; Omar et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study aims to explore the variations in digital literacy scores between male and females within a Ghana sample. The findings of this study appear to contradict previous research that has reported gender difference in digital literacy (Aslan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huatay et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rizal et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Siddiq \u0026amp; Scherer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The results of the moderation analysis showed that gender did not influence the relationship between digital literacy and academic performance. This result is supported by (Itasanmi \u0026amp; Ajani, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Osaai, 2022; Omar et al., 2023; Zeng et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) who reported no gender digital divide. Thus, both male and females performed similarly in the digital literacy competencies. These findings contradict the studies that reported gender differences either favouring males (Aslan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rizal et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or females (Huatay et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Issifu et al., 2025; Siddiq \u0026amp; Scherer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These varying results might be due to social, cultural dynamics and disparity in educational opportunities traditionally available to each gender. Notwithstanding, in the present, education is equally accessible to both male and females at the same level. The study contributes to the growing body of research on gender and digital competency and underscores the need for ongoing efforts to create inclusive and equitable digital education environment.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIMPLICATIONS\u003c/h2\u003e \u003cp\u003eThis study highlights the significant role of digital literacy in improving students\u0026rsquo; academic performance. Digital literacy is the new currency of the digital age which enables students to leverage digital networks in their academic activities, thereby enhancing their academic performance. However, this could be impractical if the challenges of face by pre-service visual art teachers, such as unreliable internet access, inadequate access to ICT tools, insufficient ICT training, high cost of digital devices and data and low proficiency of advance digital tools. Higher education institutions should prioritise digital literacy and professional development programs to enhance the human capital of the pre-service visual art teachers. This investment is to enable student curate and leverage available digital networks and build new information more effectively, thereby translating into excellent learning outcomes. Notwithstanding the high digital literacy level reported, which illuminate their digital nativeness, it doesn\u0026rsquo;t automatically translate into the ethical use of the available technologies for education purpose. Hence the need for curated digital literacy training.\u003c/p\u003e \u003cp\u003eIn addition, though gender does not play a moderating role in the relationship between digital literacy and academic performance in this study, initial teacher education should prioritise the creation of an inclusive and supportive academic environment that enhance exploration and use of digital tools effectively and efficiently regardless of gender. This would minimise the tendency of socio-cultural traits associated with the use and adoption digital tool from creeping into the academic environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS AND FUTURE RESEARCH DIRECTIONS\u003c/h2\u003e \u003cp\u003eNotwithstanding, the firm theoretical framework and methodology, this study recognises some shortcomings and provides roadmap for future studies. First and foremost, the use of quantitative research design and self-reporting used for this cross-sectional study highlights a probable operational limitation. The self-reported data is prone to social-desirability bias. Even though appropriate steps were made to control such biases, and the occurrence of the latter has not been statistically observed, the subjectivity of questionnaire data may still pose a threat to the results of the research. In order to enhance the quality of reliability and validity of the study, future scholars ought to focus on objective data (e.g., transcript of students) and use a mixed-methods research design in order to clarify the intricacies in the correlation between the two variables The research dataset was restricted to pre-service Visual Arts teachers in Ghana, and convenience sampling techniques were used. It is therefore recommended that further studies examine the study model with a different unit of analysis and a more varied sampling method to improve the generalisability of the outcomes. The design used is the cross-sectional survey design, which proves correlations between the variables but does not establish the time-based causation. Future studies may take a longitudinal design to capture the changes of variables with time to indicate the dynamic relationship as well as the cause-and-effect effects of the variables. Moreover, the relationship between digital literacy and academic performance in this study is not linear, this signals the need future studies on possible antecedents and consequence of digital literacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study examined the relationship between digital literacy and academic performance among pre-service visual art teachers in Ghana. While research on digital literacy and academic performance is extensive, studies that expressly include gender as a moderator are uncommon. The explore the variations in digital literacy scores between male and females among the pre-service visual art teachers. Based on the survey data, this study employed structural equation modelling to analyse the hypothesis. Digital literacy has a significant positive impact on academic performance of pre-service visual art teachers. In addition, gender does not moderate the relationship between digital literacy and academic performance of the preservice visual art teachers. From a practical point the study highlights the need for investment into digital literacy trainings to improve the human capital of the pre-service visual art teachers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eInformed consent: The authors confirms that Informed consent for publication was obtained from all participants/ respondents involved in the study. Participants/respondents were Informed about the study's purpose, voluntary nature, and their right to withdraw without penalty. All responses were confidential and used solely for research purposes.\u003c/p\u003e \u003ch2\u003eEthical consideration and consent to participate\u003c/h2\u003e \u003cp\u003eThe researchers assured the participants of the confidentiality of their responses and their willingness to withdraw from the study. Ethical approval was sought from and granted (Approval reference: HuSSREC/AP/235/VOL.4; Approval date: 3rd July, 2025) by the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. The study was implemented in accordance with the Helsinki Declaration.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eNo funds, grants, or other support were received\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePS: Conceptualization, Methodology, Formal analysis, Data curation, Investigation, Visualization, Writing \u0026ndash; original draft; Writing \u0026ndash; review \u0026amp; editing; Project administration; HBE: Conceptualization, Methodology, Formal analysis, Supervision; Validation, Writing \u0026ndash; review \u0026amp; editing; ATM: Methodology, Validation, Supervision, Writing \u0026ndash; review \u0026amp; editing; JAK: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing; CAK: Validation, Visualization, Writing \u0026ndash; review \u0026amp; editing; MN: Investigation; Validation, Data curation; EBA: Methodology; Writing \u0026ndash; review \u0026amp; editing; GAA: Resources, Writing \u0026ndash; review \u0026amp; editing. All authors have read and agreed to the published version of this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, Q., Hussain, S., \u0026amp; Rasool, S. (2019). Digital Literacy Effect on the Academic Performance of Students at Higher Education Level in Pakistan. \u003cem\u003eGlobal Social Sciences Review\u003c/em\u003e, \u003cem\u003eIV\u003c/em\u003e(I), 108\u0026ndash;116. https://doi.org/10.31703/gssr.2019(IV-I).14\u003c/li\u003e\n\u003cli\u003eAdarkwah, M. A., \u0026amp; Huang, R. (2023). 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ISDSA Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital competency, academic achievement, Visual art education, Higher education, pre-service teachers, gender, connectivism, network learning, digital natives, PLS-SEM","lastPublishedDoi":"10.21203/rs.3.rs-9667213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9667213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates how digital literacy relates to academic performance among pre-service visual arts teachers in Ghana, addressing the limited use of moderation models by examining whether gender conditions this relationship. A quantitative correlational design was applied to survey data from 258 pre-service teachers. Digital literacy and academic performance were measured using validated Likert-scale instruments. Data were analysed using partial least squares structural equation modelling (PLS-SEM). Digital literacy exerts a strong positive effect on academic performance (β\u0026thinsp;=\u0026thinsp;0.685, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining substantial variance (R\u0026sup2; = 0.526). Contrary to expectations, gender does not moderate this relationship (β\u0026thinsp;=\u0026thinsp;0.130, p\u0026thinsp;=\u0026thinsp;0.264), indicating comparable academic returns from digital literacy across male and female students. Findings suggest that digital literacy development should be prioritised in teacher education without overemphasising gender-based differentiation, while addressing infrastructural and pedagogical constraints in resource-constrained environments. By integrating moderation analysis and robustness testing within a discipline-specific context, this study challenges assumptions of gendered digital advantage and reframes digital literacy as a consistent predictor of academic performance across gender.\u003c/p\u003e","manuscriptTitle":"Digital Literacy and Academic Performance in Visual Arts Teacher Education in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:12:40","doi":"10.21203/rs.3.rs-9667213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21627d8d-b451-468f-a615-b63b56f75aae","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-11T07:23:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-11T07:23:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-05-10T03:05:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T16:12:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:12:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9667213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9667213","identity":"rs-9667213","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}