Latent Class Profiling of Self-Regulated Learning in MetaTutor: A Technology-Rich Learning Environment

• In Event: 42.032 - Technology Experiences That Cognitively and Metacognitively Engage Learners

Abstract

Students engaging in technology-rich environments (TREs) can experience optimal learning. TREs establish environments that enrich thinking and learning, allowing students to flexibly use technology to strengthen these processes (Hannafin & Land, 1997). MetaTutor is a meta-cognitive tool that enhances self-regulated learning (SRL) through engaging students to select the necessary processes they require to learn. Latent class analysis was applied to understand SRL from a person-centered perspective and logistic regression to predict outcomes. This study identified three student SRL profiles that predicted performance outcomes. Findings of this study identified students who demonstrate high-levels of SRL show better performance on the post-test. These results contribute to evaluating the effectiveness of MetaTutor for fostering and understanding the role of SRL in TREs.

Authors

• Clarissa Lau, University of Toronto

• Eunice Eunhee Jang, University of Toronto

• Jeanne Sinclair, University of Toronto

• Roger Azevedo, North Carolina State University

• Michelle Taub, North Carolina State University