This Work-In-Progress research paper presents the investigation of a decision tree model that was trained to predict engineering students' first-semester math performance using demographic and survey data. This is a small step in a larger project that will develop a predictive AI model that can identify students at risk of leaving engi-neering. Ultimately, we will pair a predictive model with an explanation method to identify targeted interventions that can be implemented within the first year of engineering school. Our findings from this project indicate that we may be able to successfully identify students at risk of low performance in first-semester math courses, and design effective individualized interventions.

This Research to Practice Full Paper presents an investigation of engineering retention using machine learning models. We use random forests and artificial neural networks in the form of multilayer perceptrons to analyze the interaction between different factors, such as demographic information, standardized test scores, first semester grades, and surveys to predict student retention in engineering. We find that obtained models can predict with good accuracy if students will remain in engineering, with F1 scores of at least 75 percent. We find that each model places different levels of importance on distinct factors.

As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behavior on the Web. In response to an increase in hate speech, harassment, and other antisocial behaviors, many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, i.e., subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event such as a political election or a market crash. Overall, we find that most subreddit bans prompt a small, but statistically significant, number of active users to leave the platform; the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for online content moderation.

When a group of people strives to understand new information, struggle ensues as various ideas compete for attention. Steep learning curves are surmounted as teams learn together. To understand how these team dynamics play out in software development, we explore Git logs, which provide a complete change history of software repositories. In these repositories, we observe code additions, which represent successfully implemented ideas, and code deletions, which represent ideas that have failed or been superseded. By examining the patterns between these commit types, we can begin to understand how teams adopt new information. We specifically study what happens after a software library is adopted by a project, i.e., when a library is used for the first time in the project. We find that a variety of factors, including team size, library popularity, and prevalence on Stack Overflow are associated with how quickly teams learn and successfully adopt new software libraries.