论文标题
在线一对一的数学课上判断学生表现的广泛和深度学习
Wide & Deep Learning for Judging Student Performance in Online One-on-one Math Classes
论文作者
论文摘要
在本文中，我们研究了在线一对一数学课中自动化判断过程的机会。我们建立了一个宽阔而深入的框架，以从有限数量的嘈杂的课堂对话数据中学习细粒度的预测性表示，这些数据可以执行更好的学生判断。我们进行了实验，以预测学生掌握示例问题水平的任务，结果证明了我们模型的优越性和可用性，从各种评估指标来看。
In this paper, we investigate the opportunities of automating the judgment process in online one-on-one math classes. We build a Wide & Deep framework to learn fine-grained predictive representations from a limited amount of noisy classroom conversation data that perform better student judgments. We conducted experiments on the task of predicting students' levels of mastery of example questions and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.
