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The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

This article investigates the study methodologies, learning strategies, and motives of spelling bee participants. The authors identify several important educational implications of this work.

The authors looked at student perception on various situations involving third-party tracking to personalize recommendations.

The authors looked at student behaviors around disposal of face masks. The goal of the study was to bring awareness to improper mask disposal and how the resulting litter contributes to overall environmental pollution.

In this study a student uses Daphnia magna, or water fleas, to assay the purity of local soil samples. Daphnia magna are a helpful organism to detect potentially harmful levels of toxins in water.

The authors looked at how a student's own background influence their attitude towards integration of diverse cultures and ethnicities. While overall students viewed other groups positively, the authors found that groups still indicated they felt judged by their peers.

This study examines how socioeconomic status (SES) influences student mathematics achievement across countries by conducting multiple regression analyses to the Trends in International Mathematics and Science Study (TIMSS) 2019 dataset.

Although commonly associated with cryptocurrency, blockchains offer security that other databases could benefit from. These student authors tested a blockchain database framework, and by tracking runtime of four independent variables, they prove this framework is feasible for application.

Despite major advances in gender equality, men still far outnumber women in science, technology, engineering and math (STEM) professions. The purpose of this project was to determine whether mindset could affect a student’s future career choices and whether this effect differed based on gender. When looking within the gender groups, 86% of females who had a growth mindset were likely to consider a “male” career, whereas only 16% of females with fixed mindset would likely to consider a “male” career. Especially for girls, cultivating a growth mindset may be a great strategy to address the problem of fewer girls picking STEM careers.

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.