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Observational dataset uncovers memory, anxiety, and self-efficacy metrics in female first-year learners, highlighting data critical for diverse predictive educational models.
Findings of educational science are too often defined by the contribution of student populations of the Global North. This imbalance is particularly notable in an understudied population of female first-year learners from a country that has only recently emerged from a strict patriarchal order. For these students, the freshman year is considerably challenging, as it requires countering persisting gender stereotypes. This dataset (https://doi.org/10.7910/DVN/KT8UTE) offers a snapshot of their functioning. It contains demographic information, self-reported memory functioning, anxiety, and self-efficacy. Data can enrich existing data sets, thereby contributing to the development of prediction models inclusive of human diversity, and, more broadly, to cross-cultural research.
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Pilotti et al. (2026) studied this question.
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