Technologies Supporting Self-Regulated Learning

The ubiquity and widespread use of advanced learning technologies (ALTs) poses numerous challenges for learners. Learning with these nonlinear, multirepresentational, open-ended learning environments typically involves the use of a multitude of self-regulatory processes such as planning, reflection, and metacognitive monitoring and regulation. Unfortunately, learners do not always monitor and regulate these processes during learning with ALTs, which limits these environments’ effectiveness as educational tools to enhance learning about complex and challenging topics and domains such as science, math, and medicine. This entry provides an overview of ALTs that support self-regulated learning (SRL). It also gives examples of key self-regulatory skills used when learning with ALTs, provides brief examples of four contemporary ALTs that are designed to support SRL, and discusses implications for the future of ALTs and SRL.

Self-regulation comprises a set of key processes that are critical for learning about conceptually rich domains with ALTs such as open-ended hypermedia environments, intelligent tutoring systems, multiagent systems, serious games, and other hybrid systems. Learning with ALTs involves a complex set of interactions between cognitive, affective, metacognitive, and motivational (CAMM) processes. For example, regulating one’s learning involves analyzing the learning context, setting and managing meaningful learning goals, selecting which learning strategies to use and when, monitoring whether the use of these strategies is effective in meeting the learning goals, monitoring and making accurate judgments regarding one’s emerging understanding of the topic and contextual factors, and determining whether there are aspects of the learning context (e.g., engaging in help seeking by interacting with either a human tutor or an embedded pedagogical agent) that could be used to facilitate learning.

During self-regulated learning, students need to metacognitively monitor and accurately judge whether they understand what they are learning and perhaps modify their plans, goals, strategies, and efforts in relation to dynamically changing contextual conditions. In addition, students must also monitor, modify, and adapt to fluctuations in their motivational and affective states and determine how much social support (if any) may be needed to perform the task. Also, depending on the learning context, instructional goals, perceived task performance, and progress made toward achieving the learning goal(s), they may need to modify certain aspects of their cognition, metacognition, motivation, and affect (e.g., regulate confusion induced by a complex science diagram in a hypermedia system).