How Might New Data Help Uncover Pathways to Student Achievement in Ontario?
If you observe a kindergarten classroom, its games, songs, and stories might seem to be just for fun. But play-based learning can help children develop skills and knowledge before elementary school and provide an essential foundation for high school and beyond.
What if we could discover key features of early child development that correlate with strong learning pathways through high school? Educators could use that information to ensure all students, especially those who are struggling in school, are getting the supports they need, when they need them. Research tells us that the earlier we provide individualized supports to students, the more positive their effects.
But to help achieve that goal, we need data -- specifically data that follow children’s development and learning from their early childhood through their adolescence.
Ontario is Canada’s most populous province, yet for decades it has lacked such data. But a new paper from researchers from Memorial, McMaster and Toronto shows that situation is changing.
The paper, led by Dr. Jeanne Sinclair from the Faculty of Education at Memorial University, reports on the first steps of a much larger SSHRC-funded longitudinal study that is being co-led by Dr. Scott Davies from the University of Toronto, and Dr. Magdalena Janus from McMaster University. That research team’s prime goal was to create a valid resource that could chart student learning over time and also contain other meaningful measures of their early skills and attributes of their schools and neighbourhoods. The team possessed two separate datasets that, if combined, would provide an array of crucial measures of children’s demographics, their perceptions of themselves as learners, their routines in and out of school, the languages they use at home, and attributes of their neighbourhoods.
This particular paper describes a core challenge: to successfully link information from two datasets – one containing only kindergarten students and another containing only elementary and high school students – when both are anonymized and lack common identifiers. But by adapting extensive protocols developed by data scientists to link such datasets, the authors used information on students’ birthdate, sex, school board, school, language program and language status to successfully link 42-50% of all students, and then validate that linkage. The resulting dataset contains extensive information on over 150,000 children who started kindergarten from 2004 to 2012.
Dr. Sinclair said, “To provide children the best learning opportunities…we need data that can follow children’s development and learning from early childhood through adolescence.”
Early results suggest that play-based kindergarten learning is likely important long after kindergarten. We will next use these linked data to examine which childhood indicators are associated with later success or struggles. Such research can support school administrators and policymakers with solid evidence to allocate resources.
Dr. Jeanne Sinclair, Assistant Professor, Faculty of Education, Memorial University of Newfoundland and Labrador
Sinclair, J., Davies, S. and Janus, M. (2023) “Student Achievement Trajectories in Ontario: Creating and validating a province-wide, multi-cohort and longitudinal database”, International Journal of Population Data Science, 8(1). doi: 10.23889/ijpds.v8i1.1843.