Learning environment

She wants to build a more inclusive learning environment for children with learning difficulties

A child’s school years should be a time of exploration, curiosity and discovery, leading to a lifetime of learning. However, the reality is that some children, especially those with neurodevelopmental disabilities or from disadvantaged backgrounds, may be unable to keep up and end up falling through the cracks.

Ms Jane Sum, Research Assistant at NUS Yong Loo Lin School of Medicine (NUS Medicine) Center For Holistic Initiatives For Learning & Development (CHILD), recalls meeting a boy diagnosed with Autism Spectrum Disorder (ASD) while volunteering at Pathlight School in 2016.

The boy started telling her excitedly about MRT stations in Singapore and asked her if she knew which station EW16/NE3 was. Mrs. Sum did not know the answer, but the boy quickly informed her that it was Outram Park and went on to list all the different MRT stations on the East-West line, station after station.

“It was such an incredible meeting with a child with such a great memory! The experience struck a chord with me, and after that I became more curious about brain-associated neurodevelopmental disorders and the neuroscience behind them.

After graduating from high school, Ms. Sum first wanted to study psychology at a foreign university in Australia or the United Kingdom (UK), but a friend told her about the psychology program at the Singapore campus of the University. James Cook University (JCU).

“I deepened my research on the school and I was attracted by the pedagogy it offered. I also liked that it was also accredited by the Australian Psychology Accreditation Council,” she shares. She enrolled in the Bachelor of Psychological Science (Honours) in 2017 and graduated in 2020.

Now 26, she is using what she has learned to create a more inclusive learning environment for children with learning difficulties at CHILD.

Her main project is to develop a school readiness assessment to screen preschool children between the ages of four and six. It features an algorithm that helps identify children at risk for developmental, learning and behavioral delays in early childhood, especially when brain plasticity is most favorable.

The assessment consists of a set of questions and tasks to measure children’s general knowledge, executive functioning (a set of mental skills that include working memory, flexibility of thinking, and self-control), socio-emotional and learning skills. Ms. Sum’s team is looking to deploy user-friendly technology and machine learning to help capture child learning profiles more efficiently and accurately.

“Better identifying children at risk for learning and behavioral problems will help reduce social disparities to inform better early childhood education strategies. It will also help manage resources efficiently and facilitate policy planning in education and health,” she explains.