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Measurement of Extraneous and Germane Cognitive Load in the Mathematics Addition Task: An Event-Related Potential Study

Cognitive load significantly influences learning effectiveness. All the three types of cognitive load—intrinsic, extraneous, and germane—are important for guiding teachers in preparing effective instructional designs for students. However, the techniques used to assess the relationship between brain...

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Detalles Bibliográficos
Autores principales: Wang, Chao-Chih, Cheng, Peter Kuan-Hao, Wang, Tzu-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406012/
https://www.ncbi.nlm.nih.gov/pubmed/36009099
http://dx.doi.org/10.3390/brainsci12081036
Descripción
Sumario:Cognitive load significantly influences learning effectiveness. All the three types of cognitive load—intrinsic, extraneous, and germane—are important for guiding teachers in preparing effective instructional designs for students. However, the techniques used to assess the relationship between brain activity and cognitive load during learning activities require further investigation. This study preliminarily examined cognitive load during mathematics computations based on cognitive-load theory. We used event-related potentials to compare carryover and without carryover additions under three types of stimuli (uncoloured Arabic numerals, colourful Arabic numerals, and Chinese numerals) to measure learners’ cognitive load. According to the concept and rationale of cognitive-load theory, the design defined the extraneous and germane cognitive load to measure the N1 and P2 components and the relevant behavioural data. The highest P2 amplitude was observed in the Chinese numerals condition as extraneous cognitive load, and the N1 component was observed in the colourful Arabic numerals condition as germane cognitive load. Thus, both components may play an important role in extraneous and germane cognitive load. Additionally, these exhibit negative correlations during mathematical computations. This study’s findings and implications offer insights into future ways for assessing cognitive load using brain imaging techniques and potential applications for brain–computer interfaces.