E-Gotsky: Sequencing Content using the Zone of Proximal Development
This addresses the need for personalized and efficient learning in e-learning systems, particularly for elementary students, including those with learning disabilities, though it is incremental as it applies existing concepts with machine learning techniques.
The paper tackled the problem of inefficient learning by introducing E-gotsky, an adaptive learning engine that personalizes content sequencing based on the Zone of Proximal Development, resulting in a 17% reduction in time to reach similar mastery levels in fractions for elementary students.
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.