Assessing the Attractions of MOOCs From the Perspective of Scientometrics

A range of empirical observations show that the right-skewed phenomenon emerges in the citation distributions of papers. The phenomenon also exists in the length distributions of learners’ time spent on viewing the videos of a massive open online course. The mechanisms underlying this phenomenon in both cases are the same, namely cumulative advantage, known as the Matthew effect and the Lindy effect respectively. These similarities make it possible to apply the theories and methods in scientometrics to attraction assessments of massive open online courses. Massive data of learning behaviors are recorded on the MOOC platforms with high measurability and wide coverage, setting the precondition for this study. Based on the data derived from the log records of viewing behavior, we showed how to utilize the ideas of typical scientometric indicators, such as the impact factor and
$h$
-index, to assess courses’ attraction. Our work adumbrates not only the practicability but also the limitation of the provided indicators.