Using Knowledge Space theory to Personalize Teaching for Groups of Students

Abstract

Educational analytics have a great potential to personalize learning processes in the developing world. Most schools in the developing world follow fixed curricula in their schools with little room for personalization. Infrastructure and cost constraints make the use of 1-1 computer-based tutors to personalize student learning unrealistic in these contexts. However, pervasive presence of mobile and tablet devices has made it possible to collect student assessment data, and to personalize decision-making for teachers and educational administrators. This paper presents an educational analytics method based on Knowledge Space Theory (KST) and KMeans to group students based on their zone of proximal development (ZPD). The paper also presents a case study applying this technique to support educational administrators and teachers in semi-rural schools in a developing country.