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Updated: Jan 10, 2026

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
Published on: July 1, 2015
Reconceptualizing learning engagement: evidence for a context-sensitive structure in STEM education.
Eric Trevor McChesney1, Christian D Schunn2, Gerard Dorvè-Lewis3
1Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, United States.
Student engagement in STEM is better understood through activity contexts, not just behavioral or cognitive dimensions. This challenges traditional models and suggests context-sensitive interventions for improved learning.
Area of Science:
- Educational Psychology
- STEM Education
- Learning Sciences
Background:
- Student engagement is a key psychological construct in higher education STEM.
- Traditional models of engagement (behavioral, cognitive, affective) may not fully capture its complexity.
- The influence of specific learning activities on engagement structure is often overlooked.
Purpose of the Study:
- To introduce and test a novel, activity space-based model of student engagement.
- To investigate if engagement dimensions are organized by academic environments (e.g., lectures, exams).
- To challenge conventional engagement theory and propose a reconceptualization.
Main Methods:
- Developed and applied new, contextually meaningful engagement survey instruments.
- Conducted exploratory factor analysis (EFA) on 1,176 students from two institutions.
- Performed confirmatory factor analysis (CFA) on 772 students in a third course.
Main Results:
- An activity space-based model demonstrated a better fit to the data than traditional distinctions.
- The proposed model generalized effectively across different STEM disciplines.
- Findings indicate engagement is partially sensitive to the learning activity context.
Conclusions:
- Reconceptualizes student engagement as a construct influenced by specific academic contexts.
- Challenges traditional engagement theories, advocating for context-aware psychological models of learning.
- Supports the design of more precise and equitable interventions for STEM education.
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