ABSTRACT
It is widely accepted that cognitive load plays a crucial role in online learning. However, despite neurodevelopmental conditions being the largest category of qualifying disabilities in education, and the rise of online learning, there is little understanding of the factors impacting cognitive load in online learning for neurodivergent students and how these factors differ from those affecting neurotypical students. This study used qualitative comparison groups with neurotypical and neurodivergent students to examine their experiences of cognitive load in online learning. A sample of 26 university students (14 neurotypical and 12 neurodivergent) participated in focus group discussions. While neurodivergent students reported many similar experiences of cognitive load in online learning compared to their neurotypical peers-such as confusion in navigating the content and technical issues-some difficulties were more present for neurodivergent students-such as transcripts including mistakes and inaccessible content presentation-creating additional barriers in effectively engaging with the educational content. The results suggest that neurotypical and neurodivergent students experience similar challenges, albeit to differing degrees of intensity, and that more research is needed to explore the relationship between neurodiversity and cognitive load in online learning.
Subject(s)
Education, Distance , Humans , Focus Groups , Education, Distance/methods , Students/psychology , Peer Group , CognitionABSTRACT
In this paper, we developed an experimental checklist for laboratory experiments including neurodiverse participants, particularly those with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and dyslexia. The checklist suggests additions to the basic requirements for ethical laboratory-based studies with human participants. The suggestions emphasize physical comfort, the agency of participants concerning environmental adjustments, clarity of communication, and a focus on participants' overall well-being. Those methodological guidelines aim to help researchers in facilitating inclusive and accessible laboratory environments for neurodiverse participants in order to: â¢Enhance research validity by minimizing the influence of factors that affect responses in neuroscience experiments.â¢Facilitate research recruitment by encouraging continued participation in future studies and increasing word-of-mouth.â¢Improve research dissemination by fostering a more positive perception of the research process amongst neurodiverse individuals and encouraging community involvement.
ABSTRACT
Working memory is integral to a range of critical cognitive functions such as reasoning and decision-making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near-infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye-tracking. Notably, eye-related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level-dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye-related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well-being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.
