Cognitive load theory (CLT) is an instructional theory based on the human cognitive architecture that combines working memory and long-term memory when learning and problem-solving. The theory acknowledges the limitations and potential of human cognition and uses these to generate design guidelines for optimizing learning. The theory raises awareness for a more differentiated perspective on learning and instruction, thereby holding the potential to sustainably support students, teachers, instructional designers, and the education system in addressing the challenges of the 21st century. Since its introduction by Prof. John Sweller in the 1980s, the theory has become a widely applied framework for designing instructions, learning materials, and learning environments.
Over the years, the theory has undergone continuous development and is now applied in the official policies of several countries, and its guidelines have been replicated and validated by both controlled and field experiments in educational practice around the world. Some of the guidelines of the theory are: worked example effect (give detailed examples to learners before allowing them to engage in problem-solving), redundancy effect (avoid duplicating information or adding decorations that do not help learning), split-attention effect (avoid presenting mutually referring information sources, such as images and text, spatially separated from each other), modality effect (give supplementary information as narrations rather than as written text), transient information effect (prefer slow-paced rather than fast-paced instructional information), human movement effect (use gestures and physical interactions with instructional content to aid learning). Although most of the findings of CLT concern mathematics and science, researchers have expanded the theory to cover additional topics, including language, economy, and arts. CLT has been applied in the full spectrum of learners from preschool age to adult learning.
Recently, CLT has been increasingly linked—both theoretically and empirically—to self-regulated learning (SRL). SRL refers to the competence that enables learners to decide independently whether, when, and how to learn. It emphasizes the learning process itself, highlighting the critical role of metacognition and learning strategies. Metacognition is essential when learners set goals, select and apply learning strategies, and monitor, evaluate, adapt, and reflect on their own learning processes. SRL is associated with greater learning gains and improved mental well-being, making it a key skill for lifelong learning in the 21st century. The connection between CLT and SRL is particularly valuable, as self-regulated learning may impose additional cognitive load—for example, due to the increased responsibility placed on the learner.
This Research Topic in Frontiers will cover two central themes: (1) how CLT is being informed by several approaches from other frameworks (e.g., self-regulated learning, embodied cognition, and motivation), and (2) the ongoing challenges regarding the measurement of cognitive load by reporting on the novel instruments that are being developed for measuring cognitive load (e.g., eye-tracking, and multi-language self-ratings). As such, this research topic aims to demonstrate the state-of-the-art theoretical advancements and methodological developments within CLT research and practice.
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Conceptual Analysis
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Clinical Trial
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy and Practice Reviews
Registered Report
Review
Study Protocol
Systematic Review
Technology and Code
Keywords: Cognitive load theory (CLT); working memory and long-term memory; human cognitive architecture; instructional design and classroom interventions; quantitative experimental methods; learning and problem-solving.
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