Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system

Abstract

In this study, we examined the influence of achievement goals and scaffolding on self-regulated learning (SRL) and achievement within MetaTutor, a multi-agent intelligent tutoring system. Eighty-three (N=83) undergraduate students were randomly assigned to either a control or prompt and feedback condition and engaged in a 1-h learning session with MetaTutor to learn about the human circulatory system. Process and product data were collected from all participants prior to, during, and following the session. MANCOVA analyses revealed that students in the prompt and feedback condition deployed more SRL strategies and spent more time viewing relevant science material compared to students in the control condition. Results also revealed a significant interaction between achievement goals and condition on achievement outcomes, such that learners adopting a dominant performance-approach demonstrated higher achievement in the prompt and feedback condition. Findings are discussed in relation to the role of motivation in self-regulated learning within computer-based learning environments. Implications for the design of pedagogical agents are also discussed.