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題名 ML-based validation of experimental randomization in learning games
作者 謝佩璇
Hsieh, Pei-Hsuan
貢獻者 資訊系
關鍵詞 randomization; experimental design; sample assignment; scenarios; machine learning (ML) model; classification performance; learning game
日期 2025-10
上傳時間 1-Oct-2026 15:21:16 (UTC+8)
摘要 Randomization is a standard method in experimental research, yet its validity is not always guaranteed. This study introduces machine learning (ML) models as supplementary tools for validating participant randomization. A learning direction game with dichotomized scenarios was introduced, and both supervised and unsupervised ML models were evaluated on a binary classification task. Supervised models (logistic regression, decision tree, and support vector machine) achieved the highest accuracy of 87% after adding synthetic data to enlarge the sample size, while unsupervised models (k-means, k-nearest neighbors, and ANN—artificial neural networks) performed less effectively. The ANN model, in particular, showed overfitting, even with synthetic data. Feature importance analysis further revealed predictors of assignment bias. These findings support the proposed methodology for detecting randomization patterns; however, its effectiveness is influenced by sample size and experimental design complexity. Future studies should apply this approach with caution and further examine its applicability across diverse experimental designs.
關聯 Frontiers in Artificial Intelligence, Vol.8, 1541087
資料類型 article
DOI https://doi.org/10.3389/frai.2025.1541087
dc.contributor 資訊系
dc.creator (作者) 謝佩璇
dc.creator (作者) Hsieh, Pei-Hsuan
dc.date (日期) 2025-10
dc.date.accessioned 1-Oct-2026 15:21:16 (UTC+8)-
dc.date.available 1-Oct-2026 15:21:16 (UTC+8)-
dc.date.issued (上傳時間) 1-Oct-2026 15:21:16 (UTC+8)-
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=185561-
dc.description.abstract (摘要) Randomization is a standard method in experimental research, yet its validity is not always guaranteed. This study introduces machine learning (ML) models as supplementary tools for validating participant randomization. A learning direction game with dichotomized scenarios was introduced, and both supervised and unsupervised ML models were evaluated on a binary classification task. Supervised models (logistic regression, decision tree, and support vector machine) achieved the highest accuracy of 87% after adding synthetic data to enlarge the sample size, while unsupervised models (k-means, k-nearest neighbors, and ANN—artificial neural networks) performed less effectively. The ANN model, in particular, showed overfitting, even with synthetic data. Feature importance analysis further revealed predictors of assignment bias. These findings support the proposed methodology for detecting randomization patterns; however, its effectiveness is influenced by sample size and experimental design complexity. Future studies should apply this approach with caution and further examine its applicability across diverse experimental designs.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Frontiers in Artificial Intelligence, Vol.8, 1541087
dc.subject (關鍵詞) randomization; experimental design; sample assignment; scenarios; machine learning (ML) model; classification performance; learning game
dc.title (題名) ML-based validation of experimental randomization in learning games
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.3389/frai.2025.1541087
dc.doi.uri (DOI) https://doi.org/10.3389/frai.2025.1541087