Information Engineering with E-Learning Datasets

Abstract: The rapid growth of the E-learning industry necessitates a streamlined process for identifying actionable information in the user databases maintained by E-learning companies. This paper applies several traditional mathematical and some machine learning techniques to one such dataset with the goal of identifying patterns in user proficiency that are not readily apparent from simply viewing the data. We also analyze the applicability of such methods to the dataset in question and datasets like it. We find that many of the methods can reveal useful insights into the dataset, even if some methods are limited by the database structure and even when the database has fundamental limits to the fraction of variance that can be explained. We also find that such methods are much more applicable when dataset records have clear times and student grades have fine resolution. We also suggest several changes to the way data is gathered and recorded in order to make mass-application of machine learning techniques feasible to more datasets.