发表机构
Leibniz Institute for Science and Mathematics Education (IPN)（莱布尼茨科学教育研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用机器学习反事实分析德国物理奥林匹克竞赛，发现约3.9%的非参与者可能成功，但其中74%已参与其他STEM竞赛，仅1%完全未被触达。
AI 中文摘要
STEM学生竞赛旨在识别有能力、对科学感兴趣的学生，并进一步支持他们发展其与STEM相关的能力和兴趣。当此类学生不参与时，他们可能不仅被竞赛忽视，而且被这种支持形式整体忽视；除非他们通过其他竞赛被触达。因此，本研究调查了在德国物理奥林匹克竞赛中可能取得成功的学生在多大程度上被该竞赛遗漏。研究样本包括282名奥林匹克竞赛参与者和来自学术型中学的1,103名非参与者，所有样本均基于31项指标进行评估，涵盖社会人口背景、认知能力、自我相关信念、动机变量、人格特质、职业兴趣以及在其他STEM竞赛中的先前参与情况。三个机器学习模型（弹性网络、随机森林、梯度提升）在参与者样本上训练，基于这些指标预测第一轮成功。弹性网络表现最佳。尽管预测准确性适中，但该模型被证明校准良好，支持有效的群体层面推断。仅有少数变量预测成功：数学技能、物理和工程相关技能以及跳级是正向预测因子，而传统职业兴趣是负向预测因子。当应用于非参与者时，模型识别出43名学生（3.9%）具有潜在成功可能。关键的是，这些学生中有74%此前曾参加过至少一项选拔性STEM竞赛，仅剩11名学生（约占所有非参与者的1%）完全未被选拔性STEM竞赛系统触达。这些发现表明，大多数潜在成功的非参与者并非缺席于选拔性STEM竞赛系统，而是通过其他领域参与其中。
英文摘要
STEM student competitions aim to identify capable, science-interested students and further support them in developing their STEM-related abilities and interests. When such students do not participate, they may appear to be missed not only by the competition but by this form of support altogether; unless they are reached through other competitions. The present study therefore investigated to what extent students who would likely have succeeded in the German Physics Olympiad are missed by it. The study sample comprised 282 Olympiad participants and 1,103 non-participants from academic-track secondary schools, all assessed on 31 indicators spanning sociodemographic background, cognitive abilities, self-related beliefs, motivational variables, personality traits, vocational interests, and prior participation across other STEM competitions. Three machine learning models (elastic net, random forest, gradient boosting) were trained on the participant sample to predict first-round success based on those indicators. The elastic net was found to perform best. Although predictive accuracy was modest, the model proved well-calibrated, supporting valid group-level inferences. Only a few variables predicted success: mathematics skills, physics- and engineering-related skills, and having skipped a grade were positive predictors, whereas conventional vocational interests were a negative predictor. When applied to the non-participants, the model identified 43 students (3.9%) as potentially successful. Crucially, 74% of these students had previously entered at least one selective STEM competition, leaving only 11 students (ca. 1% of all non-participants) entirely unreached by the selective STEM competition system. These findings indicate that the majority of potentially successful non-participants are not absent from the selective STEM competition system but are engaged by it through other domains.
