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1. Effects of the physical and social environment on youth cognitive performance

   https://doi.org/10.1002/dev.22258  

   Meredith, Wesley J.; Cardenas‐Iniguez, Carlos; Berman, Marc G.; Rosenberg, Monica D. (March 2022, Developmental Psychobiology)

   Abstract Individual differences in children's cognitive abilities impact life and health outcomes. What factors influence these individual differences during development? Here, we test whether children's environments predict cognitive performance, independent of well‐characterized socioeconomic effects. We analyzed data from 9002 9‐ to 10‐year olds from the Adolescent Brain Cognitive Development Study, an ongoing longitudinal study with community samples across the United States. Using youth‐ and caregiver‐report questionnaires and national database registries (e.g., neighborhood crime, walkability), we defined principal components summarizing children's home, school, neighborhood, and cultural environments. In two independent samples (ns = 3475, 5527), environmental components explained unique variance in children's general cognitive ability, executive functioning, and learning/memory abilities. Furthermore, increased neighborhood enrichment was associated with an attenuated relationship between sociodemographics and general cognitive abilities. Thus, the environment accounts for unique variance in cognitive performance in children and should be considered alongside sociodemographic factors to better understand brain functioning and behavior across development. 

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2. Deep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR Environment

   https://doi.org/10.2312/egve.20211326  

   Asish, S. M.; Hossain, E.; Kulshreshth. A. K.; Borst, C. W. (January 2021, International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments (ICAT-EGVE))

   Educational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8\% for classifying distraction, per educational activity section, into one of three levels. 

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3. Peak frequency of the sensorimotor mu rhythm varies with autism-spectrum traits

   https://doi.org/10.3389/fnins.2022.950539  

   Strang, Caroline C.; Harris, Alison; Moody, Eric J.; Reed, Catherine L. (August 2022, Frontiers in Neuroscience)

   Autism spectrum disorder (ASD) is a neurodevelopmental syndrome characterized by impairments in social perception and communication. Growing evidence suggests that the relationship between deficits in social perception and ASD may extend into the neurotypical population. In electroencephalography (EEG), high autism-spectrum traits in both ASD and neurotypical samples are associated with changes to the mu rhythm, an alpha-band (8–12 Hz) oscillation measured over sensorimotor cortex which typically shows reductions in spectral power during both one’s own movements and observation of others’ actions. This mu suppression is thought to reflect integration of perceptual and motor representations for understanding of others’ mental states, which may be disrupted in individuals with autism-spectrum traits. However, because spectral power is usually quantified at the group level, it has limited usefulness for characterizing individual variation in the mu rhythm, particularly with respect to autism-spectrum traits. Instead, individual peak frequency may provide a better measure of mu rhythm variability across participants. Previous developmental studies have linked ASD to slowing of individual peak frequency in the alpha band, or peak alpha frequency (PAF), predominantly associated with selective attention. Yet individual variability in the peak mu frequency (PMF) remains largely unexplored, particularly with respect to autism-spectrum traits. Here we quantified peak frequency of occipitoparietal alpha and sensorimotor mu rhythms across neurotypical individuals as a function of autism-spectrum traits. High-density 128-channel EEG data were collected from 60 participants while they completed two tasks previously reported to reliably index the sensorimotor mu rhythm: motor execution (bimanual finger tapping) and action observation (viewing of whole-body human movements). We found that individual measurement in the peak oscillatory frequency of the mu rhythm was highly reliable within participants, was not driven by resting vs. task states, and showed good correlation across action execution and observation tasks. Within our neurotypical sample, higher autism-spectrum traits were associated with slowing of the PMF, as predicted. This effect was not likely explained by volume conduction of the occipitoparietal PAF associated with attention. Together, these data support individual peak oscillatory alpha-band frequency as a correlate of autism-spectrum traits, warranting further research with larger samples and clinical populations. 

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4. “It Seems Like I’m Doing Something More Important”—An Interpretative Phenomenological Analysis of the Transformative Impact of Research Experiences for STEM Students with ADHD

   https://doi.org/10.3390/educsci13080776  

   Zaghi, Arash E.; Grey, Alexander; Hain, Alexandra; Syharat, Connie M. (August 2023, Education Sciences)

   Recent studies have suggested a link between attention deficit hyperactivity disorder (ADHD) and increased creativity and ingenuity. Clinical work suggests that individuals with ADHD generally gravitate toward exploratory rather than exploitative thinking. Yet, these theories have not been tested in the field. This paper is a case study of a transformative undergraduate summer research program that allows engineering students with ADHD to spend 10 weeks in engineering labs at a research-intensive (R1) university. The program intends to show that students with ADHD can excel in engineering and STEM fields when placed in research environments that align with their natural cognitive processes and preferences. Using an interpretative phenomenological analysis (IPA) on post-program participant interviews, this paper suggests that engineering students with ADHD perceive that they learn more effectively when given the opportunity to explore academic material via hands-on research. The traditional structure of STEM education, with its focus on lectures and rigid curricula, causes significant harm to these students, leading to struggles, anxiety, and even thoughts of dropping out. In contrast, the research environment appears to foster creativity and motivation in students with ADHD, as it allows for exploration, provides real-world problem-solving opportunities, and offers tangible, hands-on experiences. This paper highlights the need for a paradigm shift in engineering pedagogy to better engage with and retain this neurodiverse student population and fully harness their creative potential. 

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5. Development and Implementation of Experiment Centric Active Learning Experiments/Activities in Transportation During the Pandemic and Beyond

   Owolabi, O.; Chavis, C.; Nwachukwu, N.; James-Okeke, P.; Ahangari, S.; Shourabi, N.; Freeman, M.; Bello, M.; Bista, K.; Gaulee, U.; et al (January 2022, Transportation Research Board 2022 Annual Meeting)

   The COVID-19 pandemic forced many colleges and universities to remain on a completely online or remote educational learning for more than a year; however, due to distraction, lack of motivation or engagement, and other internal/external pandemic contributing factors, learners could not pay attention 100% to the learning process. Additionally, given that transportation classes are very hands-on, students could not do the experiment from home due to limited resources available, thereby hampering all three phases of learner interactions. The limitation of the implementation of physical, hands-on laboratory exercises during the pandemic further exacerbated students’ actualization of the critical Accreditation Board for Engineering and Technology (ABET) outcomes in transportation: An ability to develop and conduct experiments or test hypotheses, analyze and interpret data and use scientific judgment to draw conclusions. Subsequently, this paper highlights the development and implementation of experiment centric pedagogy (ECP) home-based active learning experiments in three transportation courses: Introduction to Transportation Systems, Traffic Engineering, and Highway Engineering during the pandemic. Quantitative and qualitative student success key constructs data was collected in conjunction with the execution of classroom observation protocols that measure active learning in these transportation courses. The results reveal a significant difference between the pre, and post- tests of key constructs associated with student success, such as motivation, critical thinking, curiosity, collaboration, and metacognition. The results of the Classroom Observation Protocol for Undergraduate STEM (COPUS) show more active student engagement when ECP is implemented. 

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