"What If I Had Participated?" A Machine Learning-Based Counterfactual Analysis of Non-Participants' Success Potential in STEM Competitions: The Case of the German Physics Olympiad
Abstract
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.
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BibTeX
@misc{tschisgale2026what,
title = {{"What If I Had Participated?" A Machine Learning-Based Counterfactual Analysis of Non-Participants' Success Potential in STEM Competitions: The Case of the German Physics Olympiad}},
author = {Paul Tschisgale and Knut Neumann},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.35151},
eprint = {2609.35151},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35151},
}
RIS
TY - UNPB
TI - "What If I Had Participated?" A Machine Learning-Based Counterfactual Analysis of Non-Participants' Success Potential in STEM Competitions: The Case of the German Physics Olympiad
AU - Paul Tschisgale
AU - Knut Neumann
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.35151
UR - https://arxiv.org/abs/2609.35151
AB - 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.
ER -