Enabling Personalized Learning Pathways - A Framework

Envisioning A System For Personalized Learning

LIM Yeow Heng, POH Boon Hor, YEEM Kai Ean Silas

NUS High School

Students learn at different paces, and may not always be metacognitively aware of their learning gaps. Hence learning, and formative assessments in particular, work better when customized to the individual student. 

Due to time and manpower constraints, often the design of the next formative assessment (or the next learning step) does not take into account individual readiness and competencies that have been revealed in prior assessments. The result is a one-size-fit-all solution to meet the perceived needs of the average student but is optimized for none.

We envision an adaptive learning system that provides personalized learning pathways through formative assessments. We also brainstorm a wish-list for such a learning system from students' and teachers' perspectives. For example, AI can be leveraged to analyze incoming assessment data to discover/modify personalized learning pathways, generate assessment items, and also to act as 24/7 learning assistant. Neuroscience schemes (such as interleaving and spaced repetition) can also be embedded. The system should also be teacher-customizable and enables student agency.