Dissertation Defense Announcements

The expectation for educators to engage in evidence-based decision-making has become standard protocol in public education, yet translating research into effective practice can often be mired with implementation challenges. Research-practice partnerships (RPP) support research-informed practice by engaging key stakeholders to address real and contextual problems encountered by K-12 educators. This qualitative critical realist case study investigated the inner workings and attributes of a teacher-centric RPP formed to address ongoing implementation challenges with the North Carolina Early Learning Inventory. Data sources included observations of RPP meetings, teacher interviews, communications, and analysis of artifacts. Thematic findings suggest that teacher-centric RPP models strengthen trust and credibility between educational agencies through a series of preconditions: Expanding access, diversifying perspectives, developing alliances, and deepening knowledge. This resulted in increased implementation practices and enhanced the production of usable information to address implementation fidelity. Simultaneously, this framework also heightened teachers’ sense of professional identity. This study contributes to a dearth of literature on applying RPP models to support evidence-based policy mandates and offers a new model for leveraging classroom practitioners. This investigation contributes to the field of evaluation by providing a sustainable model to maintain implementation fidelity and strengthen teachers’ perceptions of their professional identity and agency.

Autonomous vehicles have gained huge interest across private industry, academia, government, and the public because they promise higher road efficiency, improved safety, better energy consumption, and improved emissions. However, the widespread adoption of autonomous vehicle technology will likely take place over several years (if not decades) as the technology becomes more widely accepted by the general public and more cost-effective. Therefore, there will be a long period of time when we have both autonomous and human-driven vehicles sharing the same road and it is essential to develop traffic management strategies that take the uncertainty associated with the heterogeneity in the traffic networks into account. Furthermore, it is crucial to understand the extent to which these control strategies improve the performance of the traffic network.
This research aims to develop, enhance, and validate hierarchical infrastructure-based control framework designs for improving the mobility of large-scale heterogeneous traffic networks. In this work, heterogeneity is defined as a multi-vehicle traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs), distinguished by their operational characteristics and controllability. To capture the realistic nature of large-scale heterogeneous traffic networks, we adopt the heterogeneous (multi-class) METANET model wherein the density and velocity dynamics of each vehicle class in each cell are described mathematically.
Moreover, in this research, we propose a hierarchical distributed infrastructure-based control framework to manage large-scale heterogeneous traffic networks. At the lower-level, we employed the Distributed Filtered Feedback Linearization (D-FFL) controller which only requires limited information from the plant model. The purpose of this controller is to track the desired density of each vehicle class in the target cells which is set by the upper-level controller. D-FFL tracks the reference density by controlling the suggested velocity of vehicles in the target cell and its upstream cell. At the upper-level, in our initial design, a Distributed Extremum-Seeking (D-ES) controller is designed and implemented to find the optimal operating densities of each vehicle class in the target cells over time. Gradient-based D-ES is a model-free, real-time adaptive control algorithm that is useful for adapting control parameters to unknown system dynamics and unknown mappings from control parameters to an objective function. To improve the performance of the designed hierarchical controller and reduce the convergence time, we designed and implemented Lyapunov-based Switch Newton Extremum Seeking (LSNES) at the upper level of the hierarchy to feed the optimal density of each vehicle class in the target cells to the lower-level controller. One of the key distinctions between the Newton algorithm and the gradient algorithm is that the convergence of the former is not solely contingent on the second derivative (Hessian) of the cost map and it is user-assignable.
Finally, we established a MATLAB-VISSIM COM interface that allows closed-loop control of a simulated traffic scenario in PTV-VISSIM to test and validate the effectiveness of the distributed control approaches in large-scale traffic networks. The simulation results show that our control framework design can effectively reduce congestion and prevent congestion back-propagation during peak hours in large-scale traffic networks.

Corneal abrasions are one of the most common anesthesia-related adverse events. There is limited evidence about the effect of virtual education on anesthesia providers’ knowledge regarding corneal abrasion prevention. The purpose of this study project was to assess if a web-based educational module effectively improved anesthesia providers’ awareness of evidence-based preventative measures of corneal abrasions. This was a quasi-experimental pretest-posttest study design. All participants completed a questionnaire regarding their knowledge of corneal abrasion prevention before and after the educational module. A student T-test was used to compare the differences in pre- and post-education knowledge scores. There were 55 participants in the study. Among them, 23.6% were anesthesiologists and 76.4% were certified registered nurse anesthetists. There was a significant pretest-posttest difference in the total knowledge score (6.49±1.23 vs. 8.24± 1.08, p< .001). The average number of correct answers increased from 6.49 ± 1.23 to 8.24 ± 1.108 (t = 9.94, p < .001) Overall, the web-based educational module was effective in enhancing anesthesia providers’ knowledge in preventing surgical-induced corneal abrasions. The finding may add evidence for developing effective guidelines and standardizing provider education in corneal abrasion prevention.

Background
Sugammadex is a useful reversal agent of neuromuscular blockade during surgery. However, its interaction with hormonal contraceptives could lead to undesirable outcomes in surgical patients using birth control pills. The feasibility and effectiveness of web-based education targeting post-anesthesia care unit (PACU) nurses are limited. PACU nurses are responsible for patient education related to the interaction between Sugammadex and hormonal contraceptives.
Purpose
The purpose of the project is to examine the effect of a web-based education on PACU nurses’ awareness and knowledge about the interaction between Sugammadex and hormonal contraceptives.
Methods
A quantitative, quasi-experimental, pre-/post-test design study was conducted among PACU nurses who work at a healthcare facility located in the southeast region of the United States. An online survey was used to investigate the effect of web-based education on PACU nurses’ knowledge about the interaction between Sugammadex and hormonal contraceptives.
Results
Among 27 PACU nurses, their years of experience ranged from 0 to 26 years (4.43 ± 6.65). There was a significant pretest-posttest difference on Question 1 (the mechanism of action of Sugammadex) (χ 2 (1) = 6.22, p = .013). The average number of correct answers increased from 4.00 ± 0.87 to 4.70 ± 0.54 (t = 3.99, p < .001).
Conclusion
The web-based education was effective in improving PACU nurses’ knowledge of drug interactions. This finding may contribute to the development of a standardized online education program for anesthesia providers, enhancing their skills and competence in providing patient education on anesthetic agents.