TY - GEN
T1 - Prediction of Online Mathematics Test Efficiency Based on Stacked Integrated Models
T2 - Poster papers and late breaking results, workshops and tutorials, practitioners, industry and policy track, doctoral consortium, blue sky and wideAIED papers presented at the 26th International Conference on Artificial Intelligence in Education, AIED 2025
AU - Wang, Chengliang
AU - Wang, Haoming
AU - Lai, Yutong
AU - Xiao, Zihan
AU - Xu, Xianlong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This study based on data from the National Assessment of Educational Progress (NAEP) in the United States, developed an online mathematics test efficiency prediction model using stacked ensemble learning. The research compared the performance of two machine learning algorithms—Random Forest and Gradient Boosting—and combined them to construct a stacked ensemble model. Through ten-fold cross-validation, the model achieved an average F1 score of 74.92% and a recall rate of 77.45%. Feature importance analysis based on SHAP (SHapley Additive exPlanations) indicated that question type played a dominant role in predicting student test efficiency, while learning behavior features had a relatively small impact. This study innovatively integrated stacked ensemble learning with explainable artificial intelligence techniques, enhancing prediction accuracy while providing reliable support for educational decision-making.
AB - This study based on data from the National Assessment of Educational Progress (NAEP) in the United States, developed an online mathematics test efficiency prediction model using stacked ensemble learning. The research compared the performance of two machine learning algorithms—Random Forest and Gradient Boosting—and combined them to construct a stacked ensemble model. Through ten-fold cross-validation, the model achieved an average F1 score of 74.92% and a recall rate of 77.45%. Feature importance analysis based on SHAP (SHapley Additive exPlanations) indicated that question type played a dominant role in predicting student test efficiency, while learning behavior features had a relatively small impact. This study innovatively integrated stacked ensemble learning with explainable artificial intelligence techniques, enhancing prediction accuracy while providing reliable support for educational decision-making.
KW - Explainable Artificial Intelligence
KW - Online Mathematics Test
KW - SHAP
KW - Stacked Integrated Model
KW - Student Efficiency Prediction
UR - https://www.scopus.com/pages/publications/105012422900
U2 - 10.1007/978-3-031-99261-2_20
DO - 10.1007/978-3-031-99261-2_20
M3 - 会议稿件
AN - SCOPUS:105012422900
SN - 9783031992605
T3 - Communications in Computer and Information Science
SP - 221
EP - 229
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED - 26th International Conference, AIED 2025, Proceedings
A2 - Cristea, Alexandra I.
A2 - Walker, Erin
A2 - Lu, Yu
A2 - Santos, Olga C.
A2 - Isotani, Seiji
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 July 2025 through 26 July 2025
ER -