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基于null importance和stacking模型的知识追踪研究
首发时间：2023-12-28
摘要：为应对基于游戏的学习平台在知识追踪应用方面的不足，本研究利用field day lab提供的教育游戏用户日志进行深入分析。采用方差法和null importance方法对数据集进行降维处理，并结合k折交叉验证与lightgbm算法，建立了一个高效的预测模型。此外，通过集成logistic模型，构建起stacking模型。研究表明，该模型在验证集上的macro-f1值显著提升至0.699，同时也在测试集上显示出优异的泛化能力。本研究为教育游戏领域的知识追踪提供了创新方法，并为游戏开发与教育实践提供了宝贵参考，支持教育游戏的开发者为学生创造更有效的学习体验。
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a study of knowledge tracing based on null importance and stacking models
abstract：in order to address the inadequaciesin knowledge tracing applications on game-based learning platforms, this study conducts an in-depth analysis using user logs from educational games provided by field day lab. we applied the variance method and null importance method for dimensionality reduction of the dataset, and combined k-fold cross-validation with the lightgbm algorithm to develop an efficient predictive model. furthermore, we constructed a stacking model by integrating a logistic model. the study reveals that this model significantly improved the macro-f1 score to 0.699 on the validation set and also demonstrated excellent generalization capabilities on the test set. this research offers innovative methods for knowledge tracing in the field of educational games and provides valuable insights for game development and educational practice, which also supports developers of educational games in creating more effective learning experiences for students.
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基于null importance和stacking模型的知识追踪研究
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