Online education is fast growing due to its accessibility and scalability, but engineering has fallen behind other fields in adopting and researching the online educational format. Student course-level attrition is a significant issue in online courses. The goal of this dissertation is to better understand the factors that impact course level persistence decisions among online undergraduate engineering students. Three different research methodologies were employed for this study: a systematic literature review (SLR), learning analytics and data mining, and multi-level modeling.The SLR focuses on understanding the temporal trends and findings from research in online engineering education. A total of thirty-nine articles published between 2011 to 2020 met inclusion criteria, and the synthesis of these articles revealed five themes: content design and delivery, student engagement and interactions, assessment, feedback, and challenges in online engineering. Theoretical, methodological, and publication trends across the forty articles were also summarized. Data for the second study was compiled from 81 courses contained within three online, ABET-accredited undergraduate engineering degree programs at a large, public institution in the southwestern United States. The students' learning management system (LMS) interaction data was utilized to create features that represent the amount of time students spent on different course activities and how those times differed from “typical” interaction patterns among students in the same course. Association rule mining was used to develop rules that describe the behavior of students who completed the course (i.e., completers) and those who opted to withdraw (i.e., leavers). The best measure of student engagement was determined to be the mathematical difference between the percentages of completer and leaver rules met by each student. Finally, multi-level modeling was used to examine the impact of interpersonal interactions on online undergraduate engineering students' course-level persistence intentions. The data for this study was gathered from online courses during the 2019-2020 academic year. Students completed questionnaires about their course and related persistence intentions twelve times during their 7.5-week online course. Students’ perceptions of the course LMS dialog, instructor practices, and peer support were found to significantly predict their course persistence intentions.

Prior research has provided evidence to suggest that veterans exhibit unique assets that benefit them in engineering education and engineering industry. However, there is little evidence to determine whether their assets are due to military service or other demographic factors such as age, maturity, or gender. The aim of this study is to discover, better understand, and disseminate the unique assets that veterans gained through military service and continue to employ as engineering students or professional engineers. This strength-based thematic analysis investigated the semi-structured narrative interviews of 18 military veterans who are now engineering students or professionals in engineering industry. Using the Funds of Knowledge framework, veterans’ Funds of Knowledge were identified and analyzed for emergent themes. Participants exhibited 10 unique veterans’ Funds of Knowledge. Utilizing analytical memos, repeated reflection, and iterative analysis, two overarching themes emerged, Effective Teaming in Engineering and Adapting to Overcome Challenges. Additionally, a niche concept of Identity Crafting was explored using the unique narratives of two participants. This study provides empirical evidence of military veterans experientially learning valuable assets in engineering from their military service. A better understanding of the veterans’ Funds of Knowledge presented in this study provides valuable opportunities for their utilization in engineering education and engineering industry.

Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on the cycle time distribution of a semiconductor or high volume electronics manufacturing system. This will help the decision makers to implement system changes to improve the predictability of their cycle time distribution without having to run simulation models. In order to understand how input parameters impact the cycle time, Design of Experiments (DOE) is performed. The response variables considered are the attributes of cycle time distribution which include the four moments and percentiles. The input to this DOE is the output from the simulation runs. Main effects, two-way and three-way interactions for these input variables are analyzed. The implications of these results to real world scenarios are explained which would help manufactures understand the effects of the interactions between the input factors on the estimates of cycle time distribution. The shape of the cycle time distributions is different for different types of systems. Also, DES requires substantial resources and time to run. In an effort to generalize the results obtained in semiconductor manufacturing analysis, a non- complex system is considered.

This thesis explores the impact of different experimental design strategies for the development of quantile regression based metamodels of computer simulations. In this research, the objective is to compare the resulting predictive accuracy of five experimental design strategies, each of which is used to develop metamodels of a computer simulation of a semiconductor manufacturing facility. The five examined experimental design strategies include two traditional experimental design strategies, sphere packing and I-optimal, along with three hybrid design strategies, which were developed for this research and combine desirable properties from each of the more traditional approaches. The three hybrid design strategies are: arbitrary, centroid clustering, and clustering hybrid. Each of these strategies is analyzed and compared based on common experimental design space, which includes the investigation of four densities of design point placements three different experimental regions to predict four different percentiles from the cycle time distribution of a semiconductor manufacturing facility. Results confirm that the predictive accuracy of quantile regression metamodels depends on both the location and density of the design points placed in the experimental region. They also show that the sphere packing design strategy has the best overall performance in terms of predictive accuracy. However, the centroid clustering hybrid design strategy, developed for this research, has the best predictive accuracy for cases in which only a small number of simulation resources are available from which to develop a quantile regression metamodel.

Preoperative team briefings have been suggested to be important for improving team performance in the operating room. Many high risk environments have accepted team briefings; however healthcare has been slower to follow. While applying briefings in the operating room has shown positive benefits including improved communication and perceptions of teamwork, most research has only focused on feasibility of implementation and not on understanding how the quality of briefings can impact subsequent surgical procedures. Thus, there are no formal protocols or methodologies that have been developed.

The goal of this study was to relate specific characteristics of team briefings back to objective measures of team performance. The study employed cognitive interviews, prospective observations, and principle component regression to characterize and model the relationship between team briefing characteristics and non-routine events (NREs) in gynecological surgery. Interviews were conducted with 13 team members representing each role on the surgical team and data were collected for 24 pre-operative team briefings and 45 subsequent surgical cases. The findings revealed that variations within the team briefing are associated with differences in team-related outcomes, namely NREs, during the subsequent surgical procedures. Synthesis of the data highlighted three important trends which include the need to promote team communication during the briefing, the importance of attendance by all surgical team members, and the value of holding a briefing prior to each surgical procedure. These findings have implications for development of formal briefing protocols.

Pre-operative team briefings are beneficial for team performance in the operating room. Future research will be needed to continue understanding this relationship between how briefings are conducted and team performance to establish more consistent approaches and as well as for the continuing assessment of team briefings and other similar team-related events in the operating room.