数据驱动研究范式下的MOOC教学研究问题初探

Study on data-driven research methods for MOOC

MOOC教育打破了传统教育的时空界限和学校围墙,跨区域推动了高校共享优质学习资源。在MOOC发展过程中,课程平台积累的在线学习数据为开展数据驱动式的MOOC教学研究提供了基础。采用定性与定量相结合、数据科学与教育理论相结合的方式,综合应用统计学与网络科学等理论方法,分析学习行为,挖掘有效学习因素,度量学习有效性,探索有效方法评价课程效用与开展个性化定制教学,对国家推进精品在线开放课程建设以及"互联网+教育"发展等方面具有紧迫的实践意义。

MOOC learning, a new form of education in the information era, breaks through the space-time limits and the boundary of schools, and promotes sharing of high quality study resources among universities. In the developing process of MOOCs, massive online learning data have been collected, and analyzing these data can provide support for research on MOOC. Combining qualitative and quantitative methods, data science and theory of education, statistics and network science, we analyze learning behavior patterns, measure the effectiveness of learning behaviors, establish an effectiveness evaluation model for MOOCs, and explore customized teaching methods. Our data-driven methods have a practical significance for the national project of constructing excellent online courses, and help to improve “Internet＋Education” quality.