The concept of "student effort" is foundational to such commonly used assessments of institutional effectiveness as the National Survey of Student Engagement (NSSE) and the Community College Survey of Student Engagement (CCSSE). However, the current measure of student effort omits intercultural effort, which is particularly salient to the academic success of students from underrepresented racial-ethnic groups. The engagement construct thus suffers from construct underrepresentation, and the validity of interpretations based on data from surveys of student engagement is questionable. We illustrate potential starting points for developing student effort measures that include intercultural effort as well as institutional efforts to reduce racial bias.

Understanding visitors is a necessary and complex undertaking. In this article, we present K‐means cluster analysis as one strategy that is particularly useful in unpacking the complex nature of museum visitors. Three questions organize the article and are as follows: 1) What is K‐means cluster analysis? 2) How is K‐means cluster analysis conducted? 3) Most importantly: What are the applications of K‐means cluster analysis for museum practitioners? To answer these questions, we present five steps that are vital to conducting a K‐means cluster analysis. We also present three cases studies to demonstrate differences among the results of three K‐means cluster analyses and provide practical applications of the findings.

As museum staff search for ways to broaden their audience, creative collaborations are emerging among various institutions with the hope that visitors who typically visit science centers, for example, will venture over to their local natural history museum. Typically, front-end evaluation is used for understanding details about visitors in the context of a proposed exhibition. Front-end evaluation can also help collaborating museums understand the nuances among their visitors regarding demographics, attitudes, and preferences for interpretive strategies. Carefully articulating the characteristics of the actual audience, potential audience, and target audience will help exhibit developers fine-tune their exhibitions to meet the needs and expectations of a more diverse public. This article presents partial findings from a front-end evaluation that analyzed the differences between visitors to natural history museums and science centers.articulate details about their actual audience, potential audience, and target audience (Miles, 1986).The findings presented here are from a front-end evaluation for a traveling exhibition about severe weather. This project was being developed through a collaboration among the Smithsonian Institution Traveling Exhibition Service; National Museum of Natural History;Randi Kom is president of Randi Kom &Associates,

This article presents a few salient findings from Phase I of an evaluation conducted at The Tech Museum of Innovation. Phase I focused on articulating visitors' behaviors and experiences in each of the four permanent galleries. Observations showed that visitors are spending about the same amount of time in the galleries as they spend in other museums' nondiorama exhibits, but they visit fewer components. Because some galleries performed better than others, this manuscript provides a rationale for the range of behavioral data by examining behaviors at various component types. In‐depth interviews provide another perspective on the visitor experience. They showed that in some cases visitors are not grasping the individual messages of the galleries. Observation data suggest why visitors failed to obtain the galleries' big ideas. The challenge for The Tech is to consider the unique behaviors that the exhibits promote and to rework their exhibits so they more strongly reflect and convey each gallery's big idea.

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