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The influence of working memory on auditory category learning in the presence of visual stimuli

Vishag et al. | Sep 18, 2022

The influence of working memory on auditory category learning in the presence of visual stimuli

Here in an effort to better understand how our brains process and remember different categories of information, the authors assessed working memory capacity using an operation span task. They found that individuals with higher working memory capacity had higher overall higher task accuracy regardless of the type of category or the type of visual distractors they had to process. They suggest this may play a role in how some students may be less affected by distracting stimuli compared to others.

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Predicting college retention rates from Google Street View images of campuses

Dileep et al. | Jan 02, 2024

Predicting college retention rates from Google Street View images of campuses
Image credit: Dileep et al. 2024

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

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Eye color, visual acuity and photophobia: How eye color affects light sensitivity

Spencer et al. | May 20, 2026

Eye color, visual acuity and photophobia: How eye color affects light sensitivity

This study examined whether eye color affects photophobia and vision in elementary school students and staff, finding no significant relationship between eye color, light sensitivity, or visual acuity. However, photophobia was common across age groups, highlighting the need for greater awareness of light sensitivity in learning environments.

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Changing electronic use behavior in adolescents while studying: An interventional psychology experiment

Kumar et al. | Mar 02, 2024

Changing electronic use behavior in adolescents while studying: An interventional psychology experiment
Image credit: RAMSHA ASAD

Here, the authors investigated the effects of an interventional psychology on the study habits of high school students specifically related to the use of electronic distractions such as social media or texting, listening to music, or watching TV. They reported varying degrees of success between the control and intervention groups, suggesting that the methods of habit-breaking for students merits further study.

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Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring

Mahatara et al. | May 25, 2026

Algorithmic barriers: Investigating student perceptions of AI bias in subjective “culture fit” hiring
Image credit: JonTyson

This study investigated perceptions of the emerging workforce toward the use of artificial intelligence in hiring, specifically for assessing subjective "culture fit." Through a mixed-methods survey of 150 high school and early-college students in Nepal, we found a significant disconnect between organizational adoption of AI and the profound skepticism of young job candidates, who express deep concerns about fairness, transparency, and the potential for AI to perpetuate systemic discrimination.

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