Learning Analytics in Introductory Programming Courses: A Showcase from the Federal University of Amazonas

Abstract

Introductory computing courses have a high failure rate worldwide. At the Federal University of Amazonas, this also happens and, since 2016 a group of professors decided to reformulate the course at the institution and some learning analytics initiatives have been adopted. The reformulation included a review of the course program and the use of an online judge. After all these years of research, the group has enough material and data and it is a good moment to summarize what has been done and the achievements so far. In this article, the focus will be the learning analytics in three main areas: student performance prediction, classification of difficulty of programming exercises, and gamification. Also, as a contribution, for the first time in a journal, the whole dataset is available to the community.