Khan Academy: A social networking and community question answering perspective

Abstract

This paper studies the social networking and community question answering aspects of Khan Academy, a popular yet largely uninvestigated online educational forum. We start with a brief description of our dataset and data collection methodology. We then proceed to construct the underlying network and study its topology based on degree distribution and degree correlation. We examine the performance of different ranking algorithms vis-a-vis user-provided expertise ranking, and explain the observed high correlation with PageRank. Furthermore, we empirically observe how interactions evolve as a course advances, and note that while the network progressively shrinks because low-performing nodes drop out, it also becomes a more tight-knit community. We infer that users who drop out are possibly novice learners, who ask several questions but lack the required expertise to answer many questions themselves. Throughout our work, we draw parallels with existing studies on other web-based question-answering forums which are primarily targeted towards an adult population.