The sum is greater than the parts: ensembling models of student knowledge in educational software
The sum is greater than the parts: ensembling models of student knowledge in educational software
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DOI：
10.1145/2207243.2207249
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Z. Pardos;S. M. Gowda;R. Baker;N. Heffernan
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文献类型：
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作者：
Z. Pardos;S. M. Gowda;R. Baker;N. Heffernan
在过去的十年中，已经提出了许多相互竞争的模型来预测教育软件中的学生知识。最近的研究试图将这些模型联合收割机以努力提高性能，但产生了不一致的结果。虽然2010年KDD Cup数据集的工作显示了集成方法的好处，但遗传学导师的工作未能显示出类似的好处。我们假设关键因素是数据集的大小。我们探索的潜力，以提高学生的表现预测与合奏方法从不同的辅导系统，ASSISTments平台，其中包含15倍的遗传学导师数据集的响应数的数据集。我们评估了八个学生模型和八种集成预测方法的预测性能。在这个数据集中，集成方法比任何单一的方法更有效，最好的集成方法产生的学生表现的预测比最好的个人学生知识模型好10%。
Many competing models have been proposed in the past decade for predicting student knowledge within educational software. Recent research attempted to combine these models in an effort to improve performance but have yielded inconsistent results. While work in the 2010 KDD Cup data set showed the benefits of ensemble methods, work in the Genetics Tutor failed to show similar benefits. We hypothesize that the key factor has been data set size. We explore the potential for improving student performance prediction with ensemble methods in a data set drawn from a different tutoring system, the ASSISTments Platform, which contains 15 times the number of responses of the Genetics Tutor data set. We evaluated the predictive performance of eight student models and eight methods of ensembling predictions. Within this data set, ensemble approaches were more effective than any single method with the best ensemble approach producing predictions of student performance 10% better than the best individual student knowledge model.