The rapid digitalization of society over the past decades has fundamentally changed how children and adolescents socialize, study, and play. Subsequently, children and adolescents’ use of digital technologies has increased rapidly, facilitated by the ever-evolving mobile accessibility and computing power of new digital technologies. Further, the current cohorts of children also experienced key developmental and socialization stages during the COVID-19 pandemic which led children and adolescents, by circumstance and necessity, to engage in higher levels of digital engagement. Such marked and rapid increases in both access and use of digital technologies, as well as the COVID-19 pandemic, has caused growing concerns in parents, researchers, educators, and clinicians alike as to what effects such technologies may have on children’s development and educational outcomes.

            Overall, the current literature on the effects of digital use and child and adolescent educational outcomes is mixed. Some studies indicate that adolescent digital use, particularly texting, hampers children’s literacy outcomes (Kemp and Bushnell, 2011) and that early mobile phone ownership negatively impacts academic development (Dempsey et al., 2019). However, other studies found positive effects (Plester et al., 2008) or no associations (Verheijen, 2013). Some literature has examined the potential negative impacts of adolescent digital use on academic performance via cognitive functioning, including cognitive overload or multitasking (May and Elder, 2018), distraction and diminished attentional abilities (Ward et al., 2017), and memory and learning patterns (Loh and Kanai, 2016). Additionally, Lissak (2018) identified that the effects of digital use on academic performance may be indirectly channeled through reductions in sleep duration and quality, leading to problems of time displacement or sleep disruption.

As a whole, results on associations between digital use and academic and educational outcomes remains unclear, and further research on these associations with current cohorts of children remain essential to understand how today’s digital environments are affecting how children learn and develop. Further, while there have been a multitude of studies that have investigated the effects of the COVID-19 pandemic on children’s educational outcomes (Coles et al, 2023), few studies have examined how effects of digital use on child outcomes differ from other cohorts of children who did not experience the pandemic during the critical stage of childhood and early adolescence.

This study crucially aims to address some of the above gaps in knowledge. This study utilizes high-quality multi-cohort data to examine 1) how children at different stages of development are using digital technologies and 2) how these children’s digital use is associated with perceived academic ability (from both children and teachers). To do so, we utilize the most recently collected wave from the Children’s School Lives (CSL) study collected in April 2023, when the participating cohorts of children were age 8 and age 12/13. Preliminary analyses include descriptive statistics and OLS regression modelling, however, further analyses will incorporate more diverse regression modelling, longitudinal models as well as standardized testing data (not yet available) to compare perceptions vs realities of chidlren's academic ability.

This study utilizes data from the Children’s School Lives (CSL) study, a multi-cohort, longitudinal study from Ireland which aims to provide a rich and detailed understanding of children’s learning, wellbeing, and engagement. CSL follows two age cohorts: Cohort B, who were born in approx. 2010 who started 2nd class in 2018; and Cohort A, born in approx. 2015 who transitioned from pre-school into Junior Infants in 2019. Data collection began in April 2019, with both cohorts sampled every year through Spring 2023. For the current analyses we utilize the most recent wave of data collection (Wave 5), in which the study children are approximately age 8 (Cohort A, N = 1,598) and age 12/13 (Cohort B, N = 1,911). Multiple imputation was applied on variables with high levels of missingness. This study first descriptively examines differences in digital technologies and digital screen-time between the two cohorts We then perform a number of OLS linear regression models to investigate associations of digital screen-time on a) children’s perceived academic ability and b) teacher’s perceived academic performance. Three OLS models were examined for each cohort and outcome variable: a univariate model, a model that includes sociodemographic controls (child gender, single parenthood, parental education, and household income) and a final model that include sociodemographic variables and previous perceived academic ability to preliminarily address issues of bidirectionality. To measure digital screen-time, children were asked how much time they spend on screen-based activities on an average weekday and weekend day (Responses: None, 30 minutes, 1 hour, 2 hours, and 3+ hours). Further, children were asked what digital technologies they either own themselves or share regularly (smartphone, tablet, smartwatch, computer, games console). To examine children’s perceived academic ability, children were asked “Compared to other children in your class, how well do you think you do in [reading/maths]?”, with responses of ‘Struggling a lot’, ‘Struggling a little bit’, ‘Same as everyone else’, ‘A little bit better’, and ‘A lot better’. Teachers were also asked to assess the study children’s academic ability “typical ability compared to their peers?”, with responses of ‘Lower’, ‘Average’, or ‘Higher’. Although these outcome variables can be considered categorical or ordinal, for these preliminary analyses we utilize them as continuous variables where lower scores indicate lower perceived ability and higher scores indicate higher perceived ability. This was done to examine preliminary associations and trends, and further analyses will utilize and compare multinomial and ordinal logistic regression modelling.

Both cohorts exhibit high engagement (approx. 70%) with tablets and consoles, while the older Cohort B uses/owns smartphones and computers at higher levels than Cohort A. In terms of screen-time, we observe that the older cohorts has overall higher rates of screen-time than the younger cohort, with nearly half of Cohort B spending over 3 hours on average per weekday on digital devices, compared to 28% of children from Cohort A. However, this is a drastic increase compared to data from previous research which found only 1-2% of Irish 9-year-olds (born in 1998 and 2008) to use digital technologies for 3+ hours per day (Bohnert & Gracia, 2021), this indicates that current generations of children and adolescents, particularly those who have experienced the COVID-19 pandemic, might be participating in much higher levels of screen-time than even very recent previous cohorts. From the OLS models we first observe that 3+ hours weekday screen-time is significantly associated with lowered child perceptions of academic ability in Cohort B, in both reading (B = -0.244, p < 0.001) and math (B = -0.178, p < 0.01). We further observe a significant association of 3+ hours screen-time with reduced teacher perception of reading ability (B = -0.109, p < 0.05). However, we observe no significant associations of digital screen-time with perceptions of academic ability in Cohort A. The findings from Cohort B is in line with some previous research in Ireland which found negative associations between digital engagement and children’s academic development (Dempsey et al., 2019). Further, the differing associations between cohorts might indicate that effects of digital use on outcomes are somewhat delayed i.e. that significant negative effect might emerge later in childhood and adolescence (Kardefelt-Winther, 2017). Overall, our preliminary results reveal key similarities and differences in the digital effects among current cohorts of Irish children.

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