Towards an Adaptive Regulation Scaffolding through Role-based Strategies

Abstract

Agents (virtual/physical) in a learning environment can be introduced in different roles, such as a tutor, mentor, motivator, expert, peer student etc. Each agent type brings an expertise, creating a unique social relationship with students. Depending on their role, agents have specific goals and beliefs, as well as attitudes towards the learners, thereby influencing different aspects of learning such as cognitive, affective and meta-cognitive processes in a learner. The proposed research will primarily investigate the meta-cognitive aspect of self-regulation in collaborative learning interactions and its variations with various scaffolding strategies based on agent roles. The learning interaction will be based on the socially shared regulation model of self regulation, which accommodates the social context of self regulated learning created by agents in multiples roles and behaviours. The objectives of this research will be to understand how various roles and behaviours of the agents would influence the self regulation skills of the learner and to design a role-based strategy selection model for regulation scaffolding, based on the behavioural, motivational and cognitive measures of the learning interaction.