In the field of educational technology, a critical question remains: how can digital technologies contribute to both learning and future digital inclusion? Digital technologies need to address learning outcomes, but also needs to incorporate use and experiences that provide students with understanding and skills to fully participate in the digital society. However, while research has shown that students benefit from technology integration, it is still unclear why or how this happens (e.g. Falloon, 2014).  This has made it difficult for school and teachers to know how to best design learning. Recent European Commission council recommendations on ‘promoting common values, inclusive education, and the European dimension of teaching’ has emphasized the importance of promoting digital and media skills in education to support full participation in society (European Commission, 2018). To ensure all students benefit from technology integration, develop the necessary skills and knowledge to thrive and fully participate in the digital society, it is necessary to understand the role of digital technologies in the classroom.

To understand and explore how digital technologies contribute to learning and digital inclusion, it is necessary to consider their wider context of use. Young people do not use digital technologies in isolation. They are often part of a complex ‘ecology’, which includes a combination of different technologies, teaching practices and contextual elements that happen over time (Arnott, 2016). These combined practices then need to be considered in relation to learning and how they contribute to digital and media literacy (Livingstone, 2012). In this paper, we explore a portion of young children’s digital ecology through an analysis of aggregated ‘app’ use over time. Internationally, there is a large and ever-growing number of apps available to young children, but there has been limited critical analysis of actual use over time and what this means for expected learning and future inclusion. Consequently, it remains unclear how apps, individually or collectively, may contribute to learning, digital and media literacy.

Drawing on a large aggregated Australian dataset of app usage between Years 1 and 2 in school, collected from 15,000 Android devices in 178 schools over six months, this paper explores typical and unique patterns of app use within the learning context. One of the issues in educational technology research has been difficulty capturing real usage of digital technologies. Most research in the field relies on observation or self-reporting to record usage, but this data has a natural bias. In the current research, anonymous app usage data is collected automatically from the devices, which provides an unbiased record of usage behaviours. Data is then analyzed using a combined data mining and theoretical framework. Data mining techniques were used to identify patterns of use. Components of key apps arising out of analysis analyzed in relation to the teaching and learning context, and components of digital and media literacy. Results suggest that individual apps may relate to specific learning outcomes, but combinations of app use over time are related to achievement. Findings go beyond analysis of apps, to provide important insights into the complexity of app use in learning contexts. It is important to consider how a range of apps, types of use and modes of interaction come together to create a learning environment within a tablet device and how this can affect learning, digital and media literacy.

Students’ anonymous tablet usage data was collected automatically from the Android devices using a specially designed computer agent between July and December 2016. Publicly available app Category metadata from Google Play was also gathered, e.g. Education, Game, Creative, etc., assigned to the app when posted online. App usage data was aggregated the school level. Using the K-means algorithm, schools were first clustered on apps and then on Categories of apps used, and numeracy and literacy scores on Australian national standardized tests. A full description of this process can be found in our technical paper (see Yang, Ma, & Howard, 2017). App use within each of clusters was then analyzed using a data mining approach, Association Rules Analysis, to identify patterns of use. Association rules analysis produces ‘rules’ with the logic of ‘if’ and ‘then’. The rule is expressed as A  B. This can be understood as: if A, then B. If Education apps were used at a school, then it is likely Games apps were also used. A full description of this approach can be found in our paper looking at students’ confidence using digital technologies (see Howard, Ma, & Yang, 2016). The rules show combinations of app categories used most often in each of the clusters. The most popular apps in each of these rules, for each cluster, were then analysed using the Legitimation Code Theory concept of ‘autonomy codes’ (Maton, 2014), which explores the implications for learning of where content is drawn from and how it is related to the intended purpose of the app. Different autonomy codes have different affordances for learning different kinds of knowledge (Maton & Howard, forthcoming). This approach provides a way to conceptualize apps’ relations to learning, digital and media literacy and possible roles in educational contexts.

The initial clustering on apps did not show a relation to performance on standardized testing. When aggregated on app categories, a five-cluster solution was accepted. In order of increasing performance on standardized testing and relation to learning, digital and media literacy, the first cluster of 24 schools was classified as ‘limited non-educational use’ of apps. This group was the lowest performing, mainly using apps from the Action and Arcade categories. Six schools in the second cluster showed ‘limited educational use’, with frequent use of Action and Education apps. Forty-eight schools in the third cluster showed ‘highly varied use’, including Education, Lifestyle, Puzzle and Tools. They were also likely to use non-Google apps. The fourth cluster included 43 schools which were typified by ‘paid educational use’ of Education and Games apps. The final 27 school cluster showed ‘education focused use’ including Lifestyle, Education and non-Google apps. This was the highest performing group. Autonomy analysis showed third, fourth and fifth cluster use relating to digital and media literacy, particularly problem solving and hypothesizing with digital technologies. Clusters showing a combined use of Education apps and those relating to digital/media literacy showed higher performance, even if they were also using other Games and Arcade apps. In the paper presentation, analysis of each cluster and its app use in terms of autonomy codes will be illustrated. Further research broadening this analysis to observe how other elements of the educational context, such as other technologies, role-playing, etc., relate to use of the tablet device and apps will be conducted. Given that apps are used across a range of international education contexts, findings from this analysis have the potential to inform technology integration, potential learning, development of digital and media literacy, internationally.

Arnott, L. (2016). An ecological exploration of young children’s digital play: framing children’s social experiences with technologies in early childhood. Early Years, 36(3), 271–288. https://doi.org/10.1080/09575146.2016.1181049 European Commission. (2018). Council recommendation on promoting common values, inclusive education, and the European dimension of teaching. Brussles. Falloon, G. (2014). What’s going on behind the screens? Researching young students’ learning pathways using iPads. Journal of Computer Assisted Learning, 30(4), 318–336. https://doi.org/10.1111/jcal.12044 Howard, S. K., Ma, J., & Yang, J. (2016). Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning. Computers & Education, 101, 29–42. https://doi.org/10.1016/j.compedu.2016.05.008 Livingstone, S. (2012). Critical reflections on the benefits of ICT in education. Oxford Review of Education, 38(1), 9–24. https://doi.org/10.1080/03054985.2011.577938 Maton, K. (2014). Knowledge and Knowers: Towards a realist sociology of education. London: Routledge. Yang, J., Ma, J., & Howard, S. K. (2017). Investigating live streaming data for student behaviour modelling. In IEEE International Conference on Fuzzy Systems. https://doi.org/10.1109/FUZZ-IEEE.2017.8015451