Using learning analytics to provide personalized recommendations for finding peers (1910.07381v1)

Abstract: This work aims to propose a method to support students in finding appropriate peers in collaborative and blended learning settings. The main goal of this research is to bridge the gap between pedagogical theory and data driven practice to provide personalized and adaptive guidance to students who engage in computer supported learning activities. The research hypothesis is that we can use Learning Analytics to model students' cognitive state and to assess whether the student is in the Zone of Proximal Development. Based on this assessment, we can plan how to provide scaffolding based on the principles of Contingent Tutoring and how to form study groups based on the principles of the Zone of Proximal Development.