Parents' digital skills and their development in the context of the Corona pandemic

The study addresses how the COVID-19 pandemic, which caused widespread school closures and a rapid shift to online teaching and assessment, influenced parents’ digital skills and their role in supporting children’s learning at home. Digital technologies have transformed communication, information access, and problem-solving, encompassing digital literacy across social media use, content creation, and online safety. Prior work suggests training in digital skills enhances individuals’ ability to identify and use appropriate resources, and that digital participation supports educational and social outcomes. In this context, the study aims to understand the digital skills parents used to teach children during the pandemic and whether these skills differed by employment status, age, and teaching responsibility. The importance lies in informing how families and education systems can leverage parents’ digital competencies to sustain learning during disruptions and beyond.

The theoretical framework defines digital skills as combinations of knowledge, skills, and attitudes enabling effective, safe participation in work and social life, including information retrieval, evaluation, storage, production, presentation, communication, and collaboration online. Building on Van Deursen et al. (2014) and Van Dijk (2006), three general skill types are highlighted: (A) automation/operational skills (hardware/software use, networks, information handling, content creation); (B) structural/cognitive skills (locating, processing, evaluating information and building cognitive schemas to solve problems); (C) strategic skills (using resources proactively to achieve objectives). The review outlines factors driving the need for digital skills during COVID-19 (limited internet/device access, infrastructure challenges, need for engaging online content) and synthesizes seven types of skills relevant to parents: technological skills (following learning paths, compensating for teachers’ physical absence), cybersecurity skills (personal and information security, technophobia concerns), critical skills (monitoring behaviors, informed judgments of information quality), virtual environments for competitive learning (cognitive load, deeper learning), information skills (navigating inequalities, using online groups and AI resources), communication skills (encoding/decoding messages across modalities), and knowledge navigation (linking 21st-century skills such as collaboration, creativity, critical thinking, problem-solving, and self-direction). The literature also notes variation in parents’ mastery by social strata and the role of lifelong learning in developing advanced digital skills.

Design: Descriptive-analytical study using a multi-part questionnaire. Part 1 collected demographics (gender, education level, age, work status, years of experience, teaching responsibilities, dependence on PC at work). Subsequent sections measured: technological skills; personal security skills; critical skills; hardware locking skills; information skills; communication skills; knowledge navigation; and electronic social skills. Participants: N=250 Saudi parents (Google Forms). Demographics: Fathers 73.2% (n=183), Mothers 26.8% (n=67). Education: Diploma 16.4%, Bachelor 52.8%, Post-graduate study 1.2%, Master’s 15.6%, PhD 14%. Age: 20–30 (5.6%), 30–40 (33.2%), 40–50 (45.6%), >50 (15.6%). Employment: Has a job 84.8%, No job 15.2%. Experience: <5y 9.6%, 5–10y 11.6%, 10–15y 19.6%, >15y 59.2%. Teaching responsibility: fully responsible 30%, partially responsible 29.2%, shared 34.4%, no direct involvement 6.4%. Work dependency on PC: Yes 81.2%, No 18.8%. Measures: Digital skills scale developed from prior literature (e.g., Rodríguez-de-Dios et al., Van Deursen et al., Van Laar et al.). 51 items across eight theoretical subscales; five-point Likert (1–5). Content analysis conceptualized eight skills; empirically, a three-factor model was supported: operational skills, cognitive constructivism skills, and elementary/automated skills (also termed instrumental/automated skills in results). Procedures and analyses: IBM SPSS v23 used. EFA with PCA extraction, Varimax rotation; items with loadings <0.50 removed; parsimonious criteria (single-factor saturation). Reliability via Cronbach’s alpha. CFA conducted (LISREL) to test EFA model fit. Normality tested via Shapiro–Wilk. Associations between demographic variables and teaching responsibility assessed with chi-square tests. Differences across demographic variables evaluated using MANOVA for each skills dimension. Ethical approval obtained (IRB Log Number: RG-21-064).

Scale structure and validity/reliability: - KMO = 0.94, indicating sampling adequacy; communalities 0.40–0.75. - Pre-rotation, eight factors explained 68.80% variance (eigenvalues: 23.35, 2.86, 2.20, 1.78, ...). First factor inflated, suggesting a general factor. - After Varimax rotation, three factors emerged with eigenvalues 10.38, 10.19, 7.85; total variance explained 55.71%. - CFA fit: RMSEA 0.11; GFI 0.92; NNFI 0.95; PNFI 0.90; SRMR 0.069; χ² significant and sensitive to non-normality and sample size. - Item loadings (CFA) ranged 0.56–0.84 (all significant). Overall Cronbach’s alpha = 0.954; subscales: cognitive constructivism α≈0.949; operational/elementary α≈0.911–0.949. Descriptive statistics (subscales): - Elementary/automated skills: Mean 66.89; SD 14.02; Variance 196.64; Skewness −0.23. - Cognitive constructivism skills: Mean 36.98; SD 6.37; Variance 40.52; Skewness −0.36. - Operational skills: Mean 65.04; SD 14.21; Variance 201.83; Skewness −0.19. - Shapiro–Wilk indicated non-normality for all three (p<0.001). Associations with teaching responsibility: - Teaching responsibility vs years of experience: χ²=26.16, df=9, p<0.002 (significant association). - Teaching responsibility vs dependence on PC at work: χ²=2.53, df=3, p=0.470 (not significant). - Teaching responsibility vs educational level: χ²=26, df=12, p=0.011 (significant). - Teaching responsibility vs age: χ²=10.33, df=9, p=0.325 (not significant). MANOVA results: - Digital instrumental/elementary skills: Significant effects for age level (F=4.090, p=0.007), instructional responsibilities (F=9.238, p<0.001), depend_on_PC (F=8.740, p=0.003), and kind_of_job (F=3.122, p=0.046). Non-significant for education level (p=0.167), job status (p=0.140), status (p=0.823), experience (p=0.051 borderline). - Cognitive constructivism skills: Significant for education level (F=8.495, p<0.001), age level (F=3.463, p=0.017), instructional responsibilities (F=3.843, p=0.010), depend_on_PC (F=14.055, p<0.001). Non-significant for status (p=0.069), job status (p=0.077), experience (p=0.308), kind_of_job (p=0.543). - Operational skills: Significant for education level (F=4.554, p=0.001), job status (F=4.786, p=0.030), instructional responsibilities (F=4.629, p=0.004). Non-significant for status (p=0.241), age level (p=0.084), experience (p=0.356), kind_of_job (p=0.377), depend_on_PC (p=0.444). Substantive interpretations: - Parents demonstrated high performance particularly in operational and instrumental skills, followed by cognitive constructivism skills. - Dependence on computers at work is associated with higher instrumental and cognitive constructivism skills, but not with operational skills. - Younger parents tended to show greater proficiency in instrumental skills; parental educational level and instructional responsibilities related to higher skills, especially cognitive constructivism and operational skills.

The findings address the research questions by identifying the types and levels of digital skills parents used during the pandemic and by demonstrating how these skills vary with demographic and role-related factors. High operational and instrumental skill levels suggest parents effectively managed digital tools and security aspects while supporting children’s learning at home. Significant effects of age, parental education, instructional responsibility, and work-related PC dependence on instrumental and cognitive constructivism skills highlight mechanisms through which parents acquire and apply digital competencies—such as leveraging alternative digital resources for information seeking, knowledge formulation, and critique. These capacities likely facilitated children’s attainment of learning goals by reducing cognitive load, enabling flexible access to content, and strengthening higher-order thinking through modeled practices of knowledge navigation and evaluation. Operational skills’ association with parental education, job status, and instructional responsibilities suggests that formal education and engagement with the child’s learning tasks enhance parents’ ability to present, manage, and summarize learning materials, contributing to children’s performance and enjoyment. Overall, the results underscore the importance of supporting parents as co-educators in hybrid and online contexts, as their digital skills can scaffold learners’ progression and resilience during disruptions.

This study developed and validated a digital skills scale for parents, empirically supporting a three-factor structure (operational, instrumental/elementary, and cognitive constructivism skills) with strong reliability. Parents reported high operational and instrumental skills and moderate-to-high cognitive constructivism skills. Differences in skills were associated with age, education level, instructional responsibilities, and work-related computer use, indicating that both personal and contextual factors shape parents’ digital competence during crises. Practically, institutions can offer targeted online courses and supports—particularly for mothers and less-experienced or less-educated parents—to build information seeking, knowledge navigation, and critical evaluation skills. Future research could expand to more balanced samples across gender and age groups, examine longitudinal development of parental digital skills post-pandemic, and explore causal pathways linking specific parental digital practices to children’s academic and socio-emotional outcomes.

Sample composition was skewed toward fathers (73.2%) and toward older age groups (approximately 65% aged 40+), potentially biasing factor structure and generalizability. Social desirability may have influenced responses, contributing to high reported skill levels and variance in instrumental and operational skills. The three-factor model explained 55.7% of variance, leaving substantial unexplained variance that may relate to sample characteristics. Findings are most generalizable to parents aged 30–50 and families with students at university, secondary, and preparatory stages. Non-normality of subscale distributions and reliance on self-report survey data are additional constraints.