Big Data and Learning Analytics
Using learning analytics to transform digital learning experiences for children, advance academic research and drive evidence-based innovation in educational technology.
The Learning Analytics strand underpins all other strands of the LiFT project, aiming to understand in depth how children learn through digital play. We achieve this by collecting, analysing and visualising data generated as children and their families engage with the Applaydu app.
This app-based data is relatively unique as it contains rich information about how children and their families play the game. For example, the data can provide information about how much time players spend on different features of a game, how players navigate through the content, and what content they return to. Such information can provide valuable insights into the processes of learning and play over time.
We use the anonymised data collected from these activities to develop and enhance existing theories of learning and child development, and to inform the creation of effective digital learning experiences. To date, we have focused on research questions related to vocabulary acquisition (drawing insights from Word Explorer and Let’s Story), creative expression (drawing on the Inkmagination game), and family interaction (through the use of parent prompts in Draw and Chat).
App data alone is rarely sufficient to advance theoretical and practical understandings. Thus, in the LifT project, we combine this data with other research methods. In any study, we tend to use a variety of different approaches to inform our research. These can include survey data, experimental data and observational data from school and home settings. This comprehensive approach allows us to test explanatory and causal models of learning and development rigorously.
Beyond our contributions to learning, child development and applied linguistics, we are committed to making methodological advancements in learning analytics, educational data mining and artificial intelligence in education (AIED). We also critically examine the ethical considerations surrounding the use of such data, prioritising the safety and privacy of children and their families through strict adherence to data protection laws and ethical guidance. More information about how we use the app data in the research is available within the app and here.
The Learning Analytics strand also offers significant value to the commercial tech industry. We help industry partners create more effective, engaging and commercially successful educational products. Furthermore, we foster a shared understanding of the crucial role of evidence-based innovation in the digital learning space.
AI and the Early Years
Our team have been engaged in exploring the ethical use of AI in the digital learning experiences of young children and their families. In collaboration with Microsoft and GameLoft we have worked with Ferrero International to generate 765,000 stories for young children in 18 different languages. Through the development of these stories we have tackled challenges such as AI text generation, AI image generation, AI translation and AI speech that needed to be both ethical and age-appropriate in its design. In doing so, we have engaged in an ethnographic study that documents the key decisions made, the challenges overcome and how they have shaped an A to Z guide for sharing with others when it comes to the ethical use of AI in digital learning for young children. The stories, for children aged 3-9 years will be freely available around the world from September 2024 on a platform that has already been downloaded by more than 60 million families. We aim to research how young children interact and learn from AI generated stories.
Publications
Ratner, S., Ong, C. & Murphy, V. (2024). Reimagining Digital Learning Game Design and Development: A Case Study. In C. Bonk & G. Marks (Eds.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 636-641). Singapore, Singapore: Association for the Advancement of Computing in Education (AACE). Retrieved January 9, 2025 from https://www.learntechlib.org/primary/p/225114/.
List of site pages
