从观看角度分析MOOC
Profiling MOOCs from viewing perspective
摘要：
我们从观看行为的角度概述了MOOC的三个方面，这是MOOC学习中最突出和最常见的。它们是学习者分类、课程吸引力、教序和学序。基于观察行为数据，提出了一种非参数的学习者分类算法，缩小了发现潜在多面手的范围;提出了一种教序与学序相关性的度量方法，有助于教学内容的分配。利用信息熵，提出了一种综合课程观看时间和课程视频观看次数的课程吸引力指标。该指数描述了重复观看的边际效用递减和观看新视频的信息增加。它有可能成为评估课程成绩的辅助方法。
英文摘要：
We profiled three aspects of MOOCs from the perspective of viewing behaviors, the most prominent and common ones of MOOC learning. They were learner classification, course attraction, teaching order and learning order. Based on viewing behavior data, we provided a non-parametric algorithm to categorize learners, which helped to narrow the scope of finding potential all-rounders, and a method to measure the correlations between teaching order and learning order, which helped to assign teaching contents. Using information entropy, we provided an index to measure course attraction, which integrated the viewing time invested on courses and the number of viewed course videos. This index describes the diminishing marginal utility of repeated viewing and the increasing information of viewing new videos. It has potential to be an auxiliary method of assessing course achievements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
