Learning Resources

Abstract: Data analytics related to student and institutional performance have evolved quite rapidly—and continue to advance—as the field of data science captures more attention across the higher education sector. And while data-informed decisions can help institutional leaders achieve their goals, there are increasing examples of analyses or visualizations that, when presented without the proper framework, result in misinterpretation and inaccurate conclusions. Context is critical, and erroneous deductions may lead to decisions that adversely affect student performance, program development, and policy changes.

Abstract: U.S. transportation data suggest that the number of vehicle miles traveled has far surpassed new capacity, resulting in increased traffic congestion in many communities throughout the country. This article reports on traffic congestion around a university campus located within a small town. The mix of trip purposes varies considerably in this context, with the majority of trips related to student movement to and from classes. The university itself becomes a major traffic generator, but in a complex way. This article describes how congestion in a university setting differs from that in a nonuniversity setting; what components drive this congestion; how best to reduce this congestion while adhering to overall university planning objectives; and how to set a foundation for traffic management strategies that provide environmental, social, and economic benefit to the university and, importantly, to the surrounding community. The information presented here applies beyond the campus setting to any community that contains nontraditional traffic generators and shows why context does matter when analyzing and managing traffic.

Abstract: A simple and readily understandable visual display of quantitative measures of academic efficiency and productivity is demonstrated in this article. This graphical construction facilitates annual comparisons of unit efficiency and productivity as well as an analysis of temporal changes in unit activity. By establishing a common framework upon which a data-driven conversation regarding unit activity is constructed, this method produces a single graphical representation of the activities of any academic unit. As such, this technique assists academic decision makers with goal setting, resource allocation and reallocation, and the program prioritization process.