关键词: Diversity Generalizability Neuroimaging Research methods Sampling

Mesh : Neurosciences / education Humans Research Design Data Collection

来  源:   DOI:10.1016/j.tine.2024.100231

Abstract:
BACKGROUND: Educational neuroscience research, which investigates the neurobiological mechanisms of learning, has historically incorporated samples drawn mostly from white, middle-class, and/or suburban populations. However, sampling in research without attending to representation can lead to biased interpretations and results that are less generalizable to an intended target population. Prior research revealing differences in neurocognitive outcomes both within- and across-groups further suggests that such practices may obscure significant effects with practical implications.
UNASSIGNED: Negative attitudes among historically marginalized communities, stemming from historical mistreatment, biased research outcomes, and implicit or explicit attitudes among research teams, can hinder diverse participation. Qualities of the research process including language requirements, study locations, and time demands create additional barriers.
METHODS: Flexible data collection approaches, community engaugement, and transparent reporting could build trust and enhance sampling diversity. Longer-term solutions include prioritizing research questions relevant to marginalized communities, increasing workforce diversity, and detailed reporting of sample demographics. Such concerted efforts are essential for robust educational neuroscience research to maximize positive impacts broadly across learners.
摘要:
背景:教育神经科学研究,研究学习的神经生物学机制,历史上纳入了主要来自白色的样本,中产阶级,和/或郊区人口。然而,在不关注代表性的研究中进行抽样可能会导致有偏见的解释和结果,这些结果不太容易推广到预期的目标人群。先前的研究揭示了群体内和跨群体的神经认知结果的差异,进一步表明,这种做法可能会掩盖具有实际意义的重大影响。
历史边缘化社区的消极态度,源于历史虐待,有偏见的研究结果,以及研究团队之间隐含或明确的态度,会阻碍多元化参与。研究过程的质量,包括语言要求,研究地点,时间需求会产生额外的障碍。
方法:灵活的数据收集方法,社区扩张,透明的报告可以建立信任并增强抽样多样性。长期解决方案包括优先考虑与边缘化社区相关的研究问题,增加劳动力多样性,以及样本人口统计数据的详细报告。这种共同努力对于强大的教育神经科学研究至关重要,以最大程度地扩大学习者的积极影响。
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