KDD杯是一项年度性数据挖掘和知识发现竞赛，其中一些世界上最好的数据挖掘团队竞争解决一个具有一定重要性的实际数据挖掘问题。

The KDD Cup is the annual Data Mining and Knowledge Discovery competition in which some of the best data mining teams in the world compete to solve an practical data mining problem of some importance. 

2010年的挑战是发现学生的学习范围究竟如何？

The 2010 challenge was about discovering how generally or narrowly do students learn? 

究竟快还是慢？

How quickly or slowly?

学生之间的进步率会有所不同吗？

Will the rate of improvement vary between students?

一个问题与另一个问题之间的相似意味着什么？

What does it mean for one problem to be similar to another? 

这可能取决于一个问题所需的知识是否与另一个问题所需的知识相同。

It might depend on whether the knowledge required for one problem is the same as the knowledge required for another.

但是，在没有人为分析任务的情况下，是否可以直接从学生成绩数据推断问题的知识需求？

But is it possible to infer the knowledge requirements of problems directly from student performance data, without human analysis of the tasks? 

2010年的挑战要求你从学生与智能辅导系统互动的日志中预测学生在数学问题上的表现。

This year's challenge asks you to predict student performance on mathematical problems from logs of student interaction with Intelligent Tutoring Systems. 

这项任务提出了有趣的技术挑战，具有实际意义，并且在科学上很有趣。

This task presents interesting technical challenges, has practical importance, and is scientifically interesting.

你将对KDD数据集应用各种机器学习技术，以提高预测数据测试部分“正确首次尝试值”的准确性。

You will apply the various machine learning techniques to the KDD datasets to improve the accuracy on predicting "Correct First Attempt values" for the test portion of the data. 

请给出分析的均方根误差（RMSE）。

Please report the Root Mean Squared Error (RMSE).

数据集下载地址：

https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

竞赛要求及更多细节内容：

https://pslcdatashop.web.cmu.edu/KDDCup/rules.jsp

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