随着MOOC的兴起，教育大数据挖掘正成为一个重要的研究方向。本课题基于北京大学从2013年开始的MOOC实践以及从中不断获得的大量学习行为数据，以困扰目前MOOC发展的课程与学习评价为背景，提出了哪些指标适合评价一门MOOC的优劣？在MOOC进展过程中，哪些迹象表明需要教师及时进行教学策略的调整？除了在线测验和考试的分数外，哪些学习行为有助于判断一个学习者的学习成效等三个问题。项目将结合信息技术与教育技术，采用建立多维数据模型、比对问卷实证关联分析、过程性指标与完成性指标相结合等方法与技术路线，从数据模型的建立、影响评价指标的主要因素、结构化统计数据与非结构化文本内容数据的相互关系、个体学习行为和效果与群体活动的关系等四个方面开展研究，其研究目标包括有关数据模型、计算方法、指标建立与评测等，目标的达到不仅可以加深我们对网络时代学习的认识，而且具有指导改进MOOC教学的应用前景。

As MOOC becoming a hot topic, mining of education big data is becoming an increasingly important research area. This project is based on the MOOC program at Peking University which was started back in 2013. Leveraging the large amount of data on learning behavior acquired from our MOOC program, the project aims to address the questions which most of the current MOOC programs are facing, how to evaluate a MOOC course and the learning effectiveness, what kind of criteria can be used for the evaluation? When a MOOC course is in prioress, what behavior can be used to trigger the teacher to make adjustment to the course strategy? Other than online quiz and test, what other learning behavior can be used for evaluating the effectiveness of the learning? The project will combine information technology and education science, apply multi-dimensional data model, uses comparison and analysis of the survey results and the empirical evidences, utilize the approach and methodology of combining the process indexes with the completion indexes. The research will focus on following areas, creating data model, defining the key factors influencing the evaluation criteria, the relationship between the structured statistical data and the unstructured content, the relationship between the individual learning behavior and effectiveness with respect to group activities. The goal of the research includes data modeling, computing methodology, criteria establishment and evaluation etc. We hope that the results of the project can help to enhance our understanding about learning in the internet era, and advance the development of MOOC.