Explaining skills are among the most important skills educators possess. Those skills have also been researched in recent years. During the same period, another medium has additionally emerged and become a popular source of information for learners: online explanatory videos, chiefly from the online video sharing website YouTube. Their content and explaining quality remain to this day mostly unmonitored, as well is their educational impact in formal contexts such as schools or universities. In this study, a framework for explaining quality, which has emerged from surveying explaining skills in expert-novice face-to-face dialogues, was used to explore the explaining quality of such videos (36 YouTube explanatory videos on Kepler’s laws and 15 videos on Newton’s third law). The framework consists of 45 categories derived from physics education research that deal with explanation techniques. YouTube provides its own ‘quality measures’ based on surface features including ‘likes’, views, and comments for each video. The question is whether or not these measures provide valid information for educators and students if they have to decide which video to use. We compared the explaining quality with those measures. Our results suggest that there is a correlation between explaining quality and only one of these measures: the number of content-related comments.

This paper first constructs a numerical text review score by applying text analytics and machine learning techniques to more than three million online text reviews collected from the Airbnb platform. Next, we employ the text review score to analyze the effect of review length on text review score and obtain insights on the interplay between the text review length and online reputation. The main contributions of this paper include: experimenting with advanced text analytics and machine learning approaches to assess online reputation; constructing an innovative text review score as a new online reputation measure; building a large knowledge-based review corpus with labels; and obtaining important insights about the effects of text review length on online reputation. Further, it has managerial and business implications for all internet platform markets and the sharing economy players seeking to build more effective online reputation systems.