Learning Resources

Abstract: In a dynamic higher education landscape, attracting and retaining students becomes paramount amid projected enrollment declines. This article explores Texas Wesleyan University’s journey in assessing student life needs on campus, engaging key stakeholders, and implementing an expedited, cost-effective mini-master plan design process. The authors demonstrate how the university and design team gathered research and developed and presented program recommendations to enhance TWU’s overall student experience and meet evolving student needs.

Abstract: Smith College commissioned MNLA to develop its 20-year Landscape Master Plan to address climate change impacts, inclusiveness, and pedagogy. The process was grounded in robust campus engagement over 18 months. More than 1,600 students and alumnae provided input through online mapping tools and in-person workshops that used cutouts and puzzles to reimagine the open spaces on campus. Staff and faculty were interviewed in person or via phone on ways to incorporate scholarship in the landscape, and 6,400 members of the campus community were reached through Instagram. An online magazine published by MNLA kept the campus community updated on findings, alternative plans, and conclusions.

Abstract: Higher Education Business Models Under Stress: Achieving Graceful Transitions in the Academy
by Melody Rose and Larry D. Large
AGP: Washington, DC: 2021
140 Pages
ISBN: 978-1-951635-12-1

Abstract: Campus space planning generally involves the use of linear models that apply a simple square-foot-per-person calculation to determine capacities for different room sizes. Most return-to-campus strategies following the easing of COVID-19 shelter-in-place restrictions involve modification of these models, increasing square-footage-per-person to accommodate six feet of distancing. This provides reasonable upper and lower capacity estimates, but it does not yield accurate estimates across different room sizes and room types. Columbia College Chicago has developed a non-linear model that is both scalable and accurate, resulting in estimates that match test fits across all observed spaces.