In the educational domain, processing and analyzing large-scale datasets are crucial for enhancing teaching quality and student learning outcomes. This study proposes a novel hybrid ensemble learning framework incorporating ordered feature processing—the Ordered Adaptive Extra-Forest (OAEF) model. Leveraging the UCI Machine Learning Repository's Portuguese higher education evaluation dataset, we establish a dual-channel predictive architecture through feature space decomposition: A Regularized Extreme Machine Tree (REMT) constructs probability distribution predictions for ordered features, while Extra-Trees algorithm models unordered features in parallel. Predictive outcomes are integrated via a dynamic weighted fusion strategy. Comparative experiments against benchmark models (Random Forest, Support Vector Machine, etc.) demonstrate OAEF's superior performance, achieving accuracy (98.73%), precision (98.66%), recall (98.43%), and F1-score (98.13%), with respective improvements of 0.72%, 0.56%, 0.49%, and 0.26% over conventional methods.
Key words
education /
performance analysis /
ordered random /
extreme random forest
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References
[1] 王劲,彭君晟,龙江,等.高等教育大数据背景下学生成绩分析研究现状[J].中国教育技术装备,2024(14):32-36.
[2] 叶伊,许新华,陈苏娜,等.基于数据挖掘的学业成绩分析与预测研究[J].电脑与信息技术,2024,32(2):78-81.
[3] 张峰,陈静静.适用于学生成绩预测的学习数据特征综述[J].软件工程,2023,26(10):1-4.
[4] 任东海.主成分分析和聚类分析在高职学生成绩综合评价中的应用[J].计算机时代,2023(11):64-67+70.
[5] 谭叶明. 普通高校体育教学学生成绩评价机制改革[J].内江科技,2020,41(2):107-108.
[6] 胡柳青. 基于机器学习的学生学业成绩预测研究与分析[D].南昌:南昌航空大学,2022.
[7] Jiang Z,Zhang Y,Li X.Analysis and prediction of learning behavior based on MOOC data[J].Journal of Computer Research and Development,2015,52(3):614-628.
[8] 马玉玲. 基于机器学习的高校学生成绩预测方法研究[D].济南:山东大学,2020.
[9] 李梦莹,王晓东,阮书岚,等.基于双路注意力机制的学生成绩预测模型[J].计算机研究与发展,2020,57(8):1729-1740.
[10] Bhardwaj B K,Pal S.Data Mining:A prediction for performance improvement using classification[J].arXiv preprint arXiv:1201.3418,2012.
[11] Thiele T,Singleton A,Pope D,et al.Predicting students’academic performance based on school and socio-demographic characteristics[J].Studies in Higher Education,2016,41(8):1424-1446.
[12] Hu Q,Che X,Zhang L,et al.Rank Entropy-Based Decision Trees for Monotonic Classification[J].in IEEE Transactions on Knowledge and Data Engineering,2012,24(11):2052-2064.
[13] Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
[14] Liaw A,Wiener M.Classification and regression by randomForest[J].R News,2002,2(3):18-22.
[15] 吴兴惠,周玉萍,邢海花.利用随机森林算法对学生成绩评价与预测研究[J].电脑知识与技术,2020,16(4):254-255+258.
[16] Geurts P,Ernst D,Wehenkel L.Extremely randomized trees[J].Machine Learning,2006,63(1): 3-42.
[17] 徐先峰,李芷菡,刘状壮,等.基于半监督学习标签传播-极端随机树算法的光伏阵列故障诊断及定位[J].电网技术,2023,47(3):1038-1047.
