Supporting Self-Regulated Learning Using Pedagogical Agents’ Adaptive Scaffolding in an Intelligent Tutoring System

• In Event: Supporting Self-Regulated Learning in Digital Learning Environments

Abstract

Objectives and Theoretical Framework: Supporting self-regulation through adaptive scaffolding delivered by pedagogical agents (PAs) during learning with intelligent tutoring systems (ITS) and other advanced learning technologies is of critical importance (Johnson & Lester, 2018). Using Winne’ (2018) theoretical perspective on self-regulated learning (SRL) and extensive empirical research on scaffolding SRL (Azevedo et al., 2022) with ITSs such as MetaTutor (Taub et al., 2021; Wiedbusch et al., in press) we designed and tested the effectiveness of PAs to scaffold students in regulating their own learning about human biology with MetaTutor. We converged product data with several on-line measures of cognitive, affective, and metacognitive processes (e.g., log files, eye tracking, facial expressions of emotions) to test the effectiveness of scaffolding with (i.e., the adaptive version of MetaTutor) or without the PAs (i.e., the non-adaptive version of MetaTutor).

Method and Data Sources: A total of 130 college students participated in a 2-day experiment with MetaTutor to learn about the human circulatory system. They were instructed to use several key SRL processes during their learning session (e.g., activating prior knowledge and setting learning goals, metacognitive monitoring [e.g., FOK, JOL], using learning strategies). Participants were randomly assigned either to the adaptive or non-adaptive condition. The effectiveness of PAs’ metacognitive scaffolding was assessed based on analyses of participants’ 2-hour session with MetaTutor where we collected the following data from each participant: eye tracking, video recording of the face (for affect detection), log files (e.g., quiz results, metacognitive judgments, learner-agent dialogue), and notes. We also collected pretest and posttest data and several self-report measures on agent likeability and metacognitive knowledge about specific SRL processes, and motivational traits (e.g., self-efficacy). Our results focus on describing the effectiveness of PAs’ metacognitive scaffolding by using multi-level trace (process) data, participants’ self-regulatory behaviors, and how they are related to learning outcomes. For example, how on-line metacognitive judgments, log-file data, facial expressions of emotional states, and eye-tracking data, relate to underlying metacognitive processes and their relation to learning outcomes.

Results: Results indicated statistically significant differences between MetaTutor conditions (p < .05), such that learners in the adaptive scaffolding condition had higher learning gains and sub-goal quiz scores, spent more time engaging in each learning sub-goal, engaged in more help-seeking behavior from the PAs, inspected more relevant and less irrelevant multimedia materials during learning, deployed more sophisticated learning strategies, and made higher metacognitive judgments, compared to participants in the non-adaptive condition. These results suggest the importance of adaptive scaffolding on learning about complex topics and promoting the effective use of SRL strategies while interacting with ITSs.

Scientific and Educational Significance: Affording learners opportunities to be scaffolded by PAs acting as external regulating agents is effective in fostering learning and self-regulated learning. In addition, multimodal data sources provide evidence that has the potential to advance current conceptual, theoretical, methodological, and analytical frameworks related to scaffolding and SRL, based on self- and external regulation (Azevedo & Gasevic, 2019; Winne & Azevedo, 2022) based on advances in AI (Baker, in press; Kay, in press).

Authors

• Roger Azevedo, University of Central Florida

• Megan Wiedbusch, University of Central Florida

• Daryn Dever, University of Florida

• Sarah Romero, University of Central Florida

• Ancuta Margondai, University of Central Florida