Designing optimal peer support to alleviate learner cognitive load in Learning Networks

Abstract

In Learning Networks, learners have to engage in social interactions for sharing knowledge to achieve their personalized learning goals. When working on complex tasks, self-organized knowledge sharing imposes too much cognitive load and this is detrimental to learning. According to pedagogical guidelines of cognitive load theory, learning environments should not only avoid activities that distract learner attention but also focus learner attention on relevant activities that contribute to learning. This paper applied these guidelines in two studies, both meant to explore how to design an optimal peer support system. Study 1 aimed to alleviate learner cognitive load by using an automated peer tutor selection system. However, the results could not support our assumption that finding available peers for those who need knowledge sharing alleviates learner cognitive load. Study 2 explored how to support the interaction process of knowledge sharing by enhancing different competencies, namely content knowledge and tutoring skills. The results showed that supporting learners with different competencies alleviates cognitive load on different dimensions. Interestingly, students supported with content knowledge felt significantly more frustrated than those with tutoring skills. Our future research aims to design an optimal peer support system by 1) alleviating learner cognitive load through refining selection criteria to find suitable peers for knowledge sharing and 2) optimizing interaction process by designing support structures based on content knowledge and tutoring skills during knowledge sharing.