Article Summary: Dion & Restrepo (2017) Neural Correlates of Feedback Processing to Learning

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Motivation 

This article explores how the brain processes feedback during learning, offering insights that complement more common educational theories about learning from feedback from cognitive, behaviorist, sociocultural, and constructivist approaches.

Feedback-Based Learning in Education and Neuroscience

Whether for academic achievement, professional development, or lifelong learning, learning from feedback is essential. As any educator (or parent or mentor or friend or …) knows, providing feedback does not ensure that someone will learn from it. For this reason, much research has studied the efficacy of different factors and types of feedback and whether that efficacy differs based on learners’ characteristics (e.g., this book chapter summary discusses factors related to education technology). These variables are studied from many different perspectives, including cognitivism, behaviorism, socioculturalism, and constructivism. However, the brain mechanisms and neural correlates of feedback processing have rarely been part of our understanding of how people learn from feedback.

This systematic literature review collects papers that bridge the educational and neuroscience research on learning from feedback. Only papers with both functional neuroimaging data and experimental behavioral data were included, connecting traditional educational research methods with those that show the brain mechanisms at play. The results show how brain development affects the processing of feedback and how situational, social, and emotional factors affect learning from feedback.

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Intro to the Learning Sciences: Research Methods for Experimental and Ecological Validity

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In the learning sciences, or any research endeavor, the research methods used are of paramount importance. Research methods and design are critical to the quality and validity of the knowledge produced. We need to use methods that represent the complete learning environment, or we risk making incorrect explanations for our findings, and that represent it accurately, or we risk not measuring what we think we’re measuring. This topic is so important that I wrote a whole blog series about research design. This post will expand upon that series with conventions specific to the learning sciences.

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Intro to the Learning Sciences: Theories of Community and Implications for Community Building

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Learning Environments as Engines of Learning

In the learning sciences, the learning environment is not just a place—it’s part of the learning process itself. Learning environments include not only learners, but also the tools they use, the people they interact with (such as peers, instructors, and mentors), and the broader social and cultural context, including families and communities. These environments shape what is valued, how content is learned, and who feels empowered to participate. Learning scientists design effective learning environments by intentionally shaping these elements to support engagement, equity, and deep understanding.

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Intro to the Learning Sciences: Theories of Cognition and Implications for Instructional Design

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Cognition in Context

Cognition—the mental processes involved in acquiring knowledge and understanding—is central in learning. But it’s not the whole story. The learning sciences emerged in part as a response to the limitations of fields like cognitive science, which often focused narrowly on internal mental processes while overlooking the broader learning environment. Early learning scientists recognized that learning is shaped not only by what happens in the brain but also by the tools, people, and contexts that surround the learner. Still, theories of cognition remain foundational in the learning sciences because they help us understand the internal mechanisms that make learning possible. By integrating these theories with insights about context and environment, we can design more effective and equitable learning experiences.

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