Current Work

Current C-MEASURE projects are listed below. Questions regarding these projects or the role of C-MEASURE in these projects should be sent to our director at james.houston@mtsu.edu.

Graduate AI Literacy Architecture (GALA)

Project description: This project assesses AI-related skills training in graduate education and perceptions of workforce preparation among students and faculty.

Funding source: Department of Education

Funding status: Pending

C-MEASURE role: External program evaluation

Safe Practice Adult IV Push Medications in Acute Care

Project description: This project assesses outcomes related to the delivery of an intravenous therapy safety program

Funding source: N/A

C-MEASURE role: Statistical analysis, data visualization, manuscript support

MT Engage ePortfolio Revision

Project description: This project assesses the potential benefits of a new ePortfolio program. Differences in the perceived use of the ePortfolio and attitudes towards the perceived usefulness of the new program relative to the standard program will be tested prospectively. C-MEASURE services requested to audit study survey components, conduct statistical analyses, and contribute to manuscript write-up for the purpose of disseminating the results of the project to an academic journal.

Funding source: MTSU Internal

Funding status: Pending

C-MEASURE role: Research methods support, statistical analysis, data visualization, manuscript support

Transforming Alzheimers Detection and Care with Advanced Machine Learning

Project description: This project aims to develop a deep learning-based framework for early-stage detection and survival analysis of Alzheimer’s disease (AD), which affects an estimated 55 million people globally. This initiative focuses on exploring the power of deep learning to analyze complex datasets, including neuroimaging, genetic, and clinical information, aiming to uncover biomarkers and predictive patterns of AD progression. Current research on MRI classification often overlooks interslice dependencies, limiting classification performance. Our project aims to fill this gap by developing innovative techniques to model these dependencies accurately, thereby enhancing diagnostic accuracy.

Funding agency: National Science Foundation

Funding status: In preparation

C-MEASURE role: Content expertise, research methods support