Using Adaptive Scaffolding Delivered by Pedagogical Agents to Improve Self-Regulation During Complex Learning

• In Event: Scaffolding Self-Assessment and Task Selection Skills to Improve Self-Regulated Learning

Abstract

Improving self-regulation through adaptive scaffolding delivered by pedagogical agents during complex learning with advanced learning technologies is critical (Azevedo et al., 2015). Pedagogical agents (PAs) have the potential to scaffold students’ challenges in regulating their own learning about complex science topics while using advanced learning technologies. We converged product data (pretest and posttest scores) with several on-line measures of cognitive, metacognitive, and affective processes (e.g., log-files, metacognitive judgments, use of SRL palette, and facial expressions of emotions) to examine the effectiveness of self- and externally-regulated learning with (i.e., the adaptive version of MetaTutor) or without the agents (i.e., the non-adaptive version of MetaTutor) (Azevedo et al., 2013). In this study, we focus on (1) briefly describing the theoretical basis for designing our PAs and (2) presenting empirical evidence, based on process and product data, regarding the effectiveness of MetaTutor’s four PAs’ metacognitive scaffolding of students learning about a complex STEM topic.

Authors

• Roger Azevedo, North Carolina State University

• Michelle Taub, North Carolina State University

• Nicholas Vincent Mudrick, North Carolina State University

• Jesse James Farnsworth, North Carolina State University

• Seth A. Martin, North Carolina State University