Cognitive load (CL) can be broadly defined as a construct representing an individual's cognitive resources used to learn or perform a task. It is assumed that assessed CL under various experimental conditions represents the working memory resources exerted or required during the task performance. CL is widely studied in diverse disciplines such as education, psychology, and human factors. In educational science, CL is used to guide instructional designs; for instance, when developing instructional designs too high CL should be avoided because it may hinder knowledge construction and understanding.
Several approaches have been proposed to measure CL. Subjective approaches using rating scales, and objective approaches, for example using physiological indicators (e.g., eye-tracking measures), are frequently used to assess CL. However, the validity of different approaches and the extent to which they represent CL are still under debate.
The present Research Topic aims to collect approaches to measure CL and particularly welcomes contributions examining their validity. Validity is defined as the extent to which empirical evidence and theoretical arguments support the intended interpretation of assessment data (e.g., self-reports, physiological measures). Validation studies can draw on different sources of validity evidence, such as evidence based on internal structure (e.g., reliability and factor analysis studies) or on relations to other variables (e.g., examining relationships with conceptually related constructs or criteria). Each approach to measure CL is supposed to be introduced by providing and evaluating a clear argument for the validity of the respective data's interpretation as indicators or measures of CL. Hence, this research topic aims to contribute to the further development of CL measurement by collecting and evaluating the most recent approaches from a validity perspective.
We welcome empirical and theoretical contributions from diverse disciplines like education, psychology, and related fields with a focus on CL measurement and its validity. Hence, theoretical contributions on validity are welcome to provide a theoretical foundation in this field, as well as theoretical contributions on CL, which precisely define the construct (including internal structure and relationships with other variables).
First and foremost, contributions that introduce specific approaches to measure CL are called for to be submitted. Such contributions include, but are not limited to, the following types:
• Development or validation of novel or established CL measures (e.g., subjective or objective measures)
• Comparisons between CL measures (e.g., subjective vs. objective measures)
• Relationship between CL measures and conceptually related constructs (e.g., working memory, motivation, emotion)
• Relationship between CL measures and learners' prior knowledge and learning outcomes (e.g., retention, transfer)
• Comparisons of applicability and sensitivity of CL measures under different conditions (e.g., during different types of learning tasks, when manipulating task complexity)
The contributions may report new validity evidence but may also reframe data from earlier research within a validity perspective. For example, group membership variables, such as low vs. high prior knowledge learners, can be used to discuss validity evidence regarding relationships with criteria in studies on how CL measures perform under different conditions. The contributions may conclude that there is a need for refining the definition of the construct CL, may suggest revisions in the measurement approach or may propose fields for future studies.
Cognitive load (CL) can be broadly defined as a construct representing an individual's cognitive resources used to learn or perform a task. It is assumed that assessed CL under various experimental conditions represents the working memory resources exerted or required during the task performance. CL is widely studied in diverse disciplines such as education, psychology, and human factors. In educational science, CL is used to guide instructional designs; for instance, when developing instructional designs too high CL should be avoided because it may hinder knowledge construction and understanding.
Several approaches have been proposed to measure CL. Subjective approaches using rating scales, and objective approaches, for example using physiological indicators (e.g., eye-tracking measures), are frequently used to assess CL. However, the validity of different approaches and the extent to which they represent CL are still under debate.
The present Research Topic aims to collect approaches to measure CL and particularly welcomes contributions examining their validity. Validity is defined as the extent to which empirical evidence and theoretical arguments support the intended interpretation of assessment data (e.g., self-reports, physiological measures). Validation studies can draw on different sources of validity evidence, such as evidence based on internal structure (e.g., reliability and factor analysis studies) or on relations to other variables (e.g., examining relationships with conceptually related constructs or criteria). Each approach to measure CL is supposed to be introduced by providing and evaluating a clear argument for the validity of the respective data's interpretation as indicators or measures of CL. Hence, this research topic aims to contribute to the further development of CL measurement by collecting and evaluating the most recent approaches from a validity perspective.
We welcome empirical and theoretical contributions from diverse disciplines like education, psychology, and related fields with a focus on CL measurement and its validity. Hence, theoretical contributions on validity are welcome to provide a theoretical foundation in this field, as well as theoretical contributions on CL, which precisely define the construct (including internal structure and relationships with other variables).
First and foremost, contributions that introduce specific approaches to measure CL are called for to be submitted. Such contributions include, but are not limited to, the following types:
• Development or validation of novel or established CL measures (e.g., subjective or objective measures)
• Comparisons between CL measures (e.g., subjective vs. objective measures)
• Relationship between CL measures and conceptually related constructs (e.g., working memory, motivation, emotion)
• Relationship between CL measures and learners' prior knowledge and learning outcomes (e.g., retention, transfer)
• Comparisons of applicability and sensitivity of CL measures under different conditions (e.g., during different types of learning tasks, when manipulating task complexity)
The contributions may report new validity evidence but may also reframe data from earlier research within a validity perspective. For example, group membership variables, such as low vs. high prior knowledge learners, can be used to discuss validity evidence regarding relationships with criteria in studies on how CL measures perform under different conditions. The contributions may conclude that there is a need for refining the definition of the construct CL, may suggest revisions in the measurement approach or may propose fields for future studies.