This article is a review of literature for skills comprised within the Accreditation Board of Engineering and Technology (ABET) criteria determined 26 topics necessary for the entry-level and continued success of engineers. The findings suggest that although entry-level engineers have proficiency in all of these ABET required skills, the entry-level engineers were not meeting the level of importance expressed by the organization for 24 of these 26 skills.

This article argues that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well.

In this article, a topic model was explored using unsupervised machine learning to summarized free-text narrative reports of 77,215 injuries that occurred in coal mines in the USA between 2000 and 2015. Latent Dirichlet Allocation modeling processes identified six topics from the free-text data. The modeling success enjoyed in this exploratory effort suggests that additional topic mining of these injury text narratives is justified, especially using a broad set of covariates to explain variations in topic emphasis and for comparison of surface mining injuries with injuries occurring during site preparation for construction.