Staying in the zone: Sequencing content in classrooms based on the zone of proximal development

Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment emphasize the importance of personalized learning. In this work, we introduce a novel adaptive learning engine named E-gostky that builds on these concepts. E-gotsky creates a machine-learning-based skipping policy that presents students with questions which will challenge, but not overwhelm them, keeping students in their Zone of Proximal Development. We evaluated our engine in a real classroom environment, including hundreds of students from several elementary schools. Our results show that using E-gostky can significantly reduce the time required to reach comparable performance. Specifically, in our experiment, it took students who were using the adaptive learning engine 17% less time to reach a similar level of mastery as of those who didn't. Moreover, students made greater efforts to find the correct answer rather than guessing, and class teachers reported that students showed higher engagement.