论文标题
大数据解释的知识追踪模型：正在结合答案吗？
Explainable Knowledge Tracing Models for Big Data: Is Ensembling an Answer?
论文作者
论文摘要
在本文中，我们描述了2020年神经教育挑战的知识追踪模型。我们结合了22个模型来预测学生是否会正确回答给定问题。我们的不同方法的结合使我们获得的精度比任何单个模型都高，并且模型类型的变化使我们的解决方案更好地解释性，与学习科学理论更加一致以及高预测能力。
In this paper, we describe our Knowledge Tracing model for the 2020 NeurIPS Education Challenge. We used a combination of 22 models to predict whether the students will answer a given question correctly or not. Our combination of different approaches allowed us to get an accuracy higher than any of the individual models, and the variation of our model types gave our solution better explainability, more alignment with learning science theories, and high predictive power.
