In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

This paper identifies some of the main challenges of data governance modelling in the context of learning analytics for higher education institutions, and discusses the critical factors for designing data governance models for learning analytics. It identifies three fundamental common challenges that cut across any learning analytics data…

There is much agreement among chemical education researchers that expertise in chemistry depends in part on the ability to coordinate understanding of phenomena on three levels: macroscopic (observable), sub-microscopic (atoms, molecules, and ions) and symbolic (chemical equations, graphs, etc.). We hypothesize this "level-coordination…

Provides a basic overview of the concept of cost in higher education and related issues, discusses major consumers of higher education cost data and their perspectives, outlines the major sources of data on higher education costs, and describes some of the major (and perennial) issues related to higher education costs. (EV)

Exercises were developed using tornado statistics to provide students with a better understanding of the spatial and temporal characteristics of these phenomena in the United States. Four categories of exercises were considered beginning with the simplest and progressing to the more complex. The first set of exercises required students to…