Computer and information literacy, or CIL, measures the ability to use computers to investigate, create and communicate effectively in different settings. Computational thinking, or CT, assesses the ability to recognize real-world problems that can be expressed computationally and to develop algorithmic solutions. In the study’s within-school comparisons, strict limits were negatively associated with CIL scores at B = −14.94 and with CT scores at B = −17.31, with both p-values below .001. Flexible limits that made exceptions for homework showed the opposite pattern: their associations with CIL and CT were positive at B = 2.17 and B = 8.34, respectively, also with p-values below .001.

Similar contrasts appeared when researchers compared schools. Schools with more students reporting strict parental limits had lower average performance in CIL and CT, with coefficients of −48.12 and −47.22, both statistically significant at p < .001. Schools in which flexible rules were more common instead had higher average CIL, at B = 9.74 with p < .05, and substantially higher CT, at B = 33.67 with p < .001. The models distinguished a student’s household rule from the prevalence of similar rules among classmates, allowing the analysis to separate within-school individual differences from broader school-level patterns.

The study used three-level linear models reflecting the structure of ICILS, which places students within schools and schools within countries. In null models, 34% of the variance in CIL and 48% in CT was attributable to differences between schools, while 50% and 22%, respectively, was associated with between-country variation. The researchers adjusted for socioeconomic status, gender, immigration status, computer experience, information and communications technology self-efficacy and internet access at home. Continuous variables were standardized, school-average predictors were included alongside group-mean-centered individual variables, and models were estimated with restricted maximum likelihood.

Because ICILS assessment outcomes were reported as five plausible values per domain, the researchers fitted separate models for each set and combined the estimates using Rubin’s rules. They also incorporated rescaled student sampling weights to reflect the study’s complex survey design, with weights adjusted within each country. The CIL analysis included students in 4,609 schools across 29 countries, while the CT analysis covered 2,722 schools in 19 countries. This difference in coverage reflects the fact that CIL was a core domain in more participating systems, whereas CT was administered as an optional domain.

Household circumstances remained important even after these controls. Socioeconomic status was positively associated with both outcomes at the individual and school levels, as were general ICT self-efficacy and home internet access. Computer experience was also positively associated with performance within schools, at B = 8.51 for CIL and B = 9.74 for CT, both with p < .001. The socioeconomic measure combined parental education, parental occupation and the number of books available at home. These relationships mean that the screen-time estimates should not be interpreted as isolated effects of a single parental choice.

The clearest evidence of socioeconomic moderation concerned flexible rules and computational thinking. The interaction between socioeconomic status and flexible limits was positive at B = 3.82, with p < .01, indicating that the association between homework exceptions and CT was stronger among students from more advantaged socioeconomic backgrounds. No statistically significant moderation by socioeconomic status was detected for CIL. The interaction between socioeconomic status and strict limits was also not significant for CT, meaning the evidence does not show that socioeconomic background altered the negative association observed for rigid restrictions.

The results add large-scale evidence to a body of research in which screen restrictions have produced inconsistent findings. Previous work has linked restrictions to fewer opportunities to develop digital skills, while other studies have found benefits from moderate technology use or structured parental guidance. The new analysis suggests that the relevant distinction may be purpose as well as duration: a rule that ends a student’s access before educational work is completed differs from one that places recreational use in a structured boundary while allowing school tasks. The analysis is consistent with, but cannot prove, the possibility that flexible rules preserve opportunities for learning by doing while strict limits remove access at moments when digital tools may be needed.

Several limitations constrain what can be concluded. The design is cross-sectional and relies on students reporting whether parental limits apply during school days, so it cannot show whether rules preceded changes in digital competence or whether more digitally capable families selected particular strategies. The analysis cannot determine whether the associations operate through academic engagement, self-regulation, parental digital knowledge or other mechanisms. CT was assessed in fewer countries than CIL, and only student-level weights were used, as the authors noted limitations in available multilevel weighting methods. The relatively broad category of screen regulation also leaves the frequency, consistency and quality of parental enforcement unresolved.

Future research could test the proposed mechanisms with longitudinal and mixed-method studies, while also measuring negotiation, parental digital competence and co-learning more precisely. Comparisons across cultures could examine whether expectations about obedience, independence and technology use change the relationship between rules and digital performance. For now, the study’s clearest result is that “less screen time” and “more structured screen use” are not interchangeable: in large international Grade 8 samples, no-exception restrictions were associated with weaker digital performance, while flexible homework exceptions were associated with stronger performance, especially in computational thinking and among students with higher socioeconomic status.

Subject of Research: Parental screen-time regulation and adolescents’ computer literacy and computational thinking

Article Title: Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data

Article References: Tlessov, A., Courtney, M. G. R., Vassilova, M., & Kambatyrova, A. (2026). Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data. Large-scale Assessments in Education, 14(1), Article 56. https://doi.org/10.1186/s40536-026-00328-4

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00328-4

Keywords: screen time, adolescents, digital literacy, computational thinking, parental mediation, ICILS 2023, socioeconomic status, computer skills, Parental, screentime, limits, child