Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments
Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments
复制标题
流程挖掘与专家特征工程相结合，可预测高风险评估中时间的有效利用
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Nathan A. Levin
中科院分区：
文献类型：
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作者：
Nathan A. Levin
教育大数据中心 NSF 东北大数据创新中心和 ETS 共同主办了一场教育数据挖掘竞赛，参赛者被要求根据学生在评估前一部分的操作日志文件，预测 NAEP 八年级数学计算机评估的有效时间利用情况。在这项工作中，使用流程挖掘和专家特征工程的组合方法来构建大量特征，然后使用极限梯度提升机器学习模型对这些特征进行训练，以根据学生是否有效利用时间对他们进行分类。在整个比赛过程中，我们对一半的隐藏数据集进行预测评估，然后最终结果基于另一半的隐藏数据集。这里使用的方法在比赛中获得了最高分。这项工作详细阐述了分析基于计算机的评估日志文件数据的组合技术，希望这种方法能为未来教育数据挖掘中的预测模型构建提供有价值的见解。
The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student’s actions on a prior portion of the assessment. In this work, a combined approach of process mining and expert feature engineering was used to build a large set of features that were then trained with an Extreme Gradient Boosting machine learning model to classify students based on whether they would use their time efficiently. Predictions were evaluated throughout the competition on half of a hidden data set and then the final results were based on the second half of the hidden data set. The approach used here earned the top score in the competition. The work presented elaborates on the combined technique for analyzing computer-based assessment log-file data with the hope that this approach will offer valuable insights for future predictive model building in educational data mining.
影响因子：
3.3
作者：
Bannert, Maria;Reimann, Peter;Sonnenberg, Christoph
通讯作者：
Sonnenberg, Christoph