E-Gotsky: Sequencing Content using the Zone of Proximal Development

Abstract

Vygotsky's notions of Zone of Proximal Development and Dynamic Assessment emphasize the importance of personalized learning that adapts to the needs and abilities of the learners and enables more efficient learning. In this work we introduce a novel adaptive learning engine called E-gostky that builds on these concepts to personalize the learning path within an e-learning system. E-gostky uses machine learning techniques to select the next content item that will challenge the student but will not be overwhelming, keeping students in their Zone of Proximal Development. To evaluate the system, we conducted an experiment where hundreds of students from several different elementary schools used our engine to learn fractions for five months. Our results show that using E-gostky can significantly reduce the time required to reach similar mastery. 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 even students with learning disabilities showed higher engagement.

Prerequisite Structure Discovery in Intelligent Tutoring Systems

“…The ZPD refers to the set of activities a learner is unable to do by herself but can do with some assistance. Because learning is most effective in the ZPD, several works have proposed implementations of this concept in tutoring models: [5] propose a tutoring model based on the automatic estimation of the ZPD by exploiting domain knowledge given by experts, while [6] and [7] use student models to estimate respectively student forgetting and student probability of success on given exercises.…”