An ex-pos-facto non-experimental quantitative study was conducted to examine the academic, financial, and student background factors that influence first-to-second year retention of engineering and engineering technology students at U.S. community colleges. Analysis of the five research questions was done using a chi-square test and multiple logistic regressions. Data were obtained from the National Center for Education Statistics (NCES) Beginning Postsecondary Students 2012/2014 (BPS: 12/14) study. Computations were performed using PowerStats, a web-based statistical tool provided by the NCES, as well as IBM SPSS 25. The sample population consisted of students who entered postsecondary education for the first time in the 2011-2012 academic year and enrolled in an engineering or engineering technology program at a community college. Predictor variables were identified from the dataset and grouped into the categories of academic, financial, and student background variables. These groupings were used as individual models to predict first-to-second year retention of community college engineering and engineering technology students using logistic regressions. Finally, individual variables that displayed statistical significance were then combined and were used as a model to predict student retention with a logistic regression. Results indicate that community college engineering and engineering technology students are not retained at a significantly different rate than non-engineering and engineering technology majors. In addition, the groupings of academic and student background variables did not have a significant impact on the retention of community college engineering and engineering technology students, while the grouping of financial variables did have a significant impact on retention. The variables attendance pattern (academic), TRIO program eligibility criteria and total aid amount (financial), and dependency status (student background) were all statistically significant to their respective predictor models. Finally, the combination of these statistically significant academic, financial, and student background variables were significant predictors of retention.

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Dr. Joseph Shrestha, Assistant Professor, ETSU Department of Engineering Technology shares that U.S. roads received a D-grade in the latest report card from American Society of Civil Engineers (ASCE). His presentation will discuss various aspects of U.S. roads; including funding sources, cost estimation, cost overruns, speed limits, and crash statistics.

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Nonnormality in the form of skewness and kurtosis was examined in lot acceptance quality characteristics data from seven state highway agencies for their highway construction quality assurance programs. Lot skewness and kurtosis varied significantly. For most lot data sets, skewness values varied in the range of 0.0 ± 1.0, whereas most kurtosis values varied in the range of 0.0 ± 2.0. The analysis also reveals that, on average, 50% of lot test data sets were nonnormal with 15% of lot data sets having skewness greater than ±1.0 and kurtosis greater than ±2.0. This is a significant finding because most state transportation agencies' pay factor algorithms assume normally distributed lot. Further investigation showed that high skewness and kurtosis were associated with higher lot variability. This variability produced misleading results in regard to inflated Type I error and low power for the F-test. However, the t-test was found to be quite robust for distinguishing mean differences. Significant deviation was observed in lot pay factors based on percent within limits between assumed normal data and normalized data. Effects of nonnormal distribution on the lot pay factor were found to be varied on the basis of the specification limits, the distribution of defective materials on the tails in the case of two-sided limits, and the orientation of the nonnormal distribution itself.

The study of this research has a challenge of learning data gathering sensor programming and design of electronic sensor circuit. The cost of autonomous vehicle development is expensive compared to purchasing an economy vehicle such as the Hyundai Elantra. Keeping the development cost down is critical to maintaining a competitive edge on vehicle pricing with newer technologies. Autonomous vehicle sensor integration was designed and then tested for the driving vision data-gathering system that requires the system to gather driving vision data utilizing area scan sensors, Lidar, ultrasonic sensor, and camera on real road scenarios. The project utilized sensors such as cheap cost LIDAR, which is that drone is used for on the road testing; other sensors include myRIO (myRIO Hardware), LabVIEW (LabVIEW software), LIDAR-Lite v3 (Garmin, 2019), Ultrasonic sensor, and Wantai stepper motor (Polifka, 2020). This research helps to reduce the price of usage of autonomous vehicle driving systems in the city. Due to resolution and Lidar detecting distance, the test environment is limited to within city areas. Lidar is the most expensive equipment on autonomous vehicle driving data gathering systems. This study focuses on replacing expensive Lidar, ultrasonic sensor, and camera to drone scale low-cost Lidar to real size vehicle. With this study, economic expense autonomous vehicle driving data acquisition is possible. Lowering the price of autonomous vehicle driving data acquisition increases involving new companies on the autonomous vehicle market. Multiple testing with multiple cars is possible. Since multiple testing at the same time is possible, collecting time reduces.

Many of our students enrolled in our Master of Science in Technology program have expressed an interest in learning about entrepreneurship and the development and management of a technology driven company. Students interested in entrepreneurship can pursue a 12 credit concentration that includes classes in developing a cohesive marketing and technology strategy, comparing and contrasting technology strategies for companies within the same market niche, developing an entrepreneurial business plan and coursework in either small business management or entrepreneurial finance. One critical component of this concentration is the utilization of the Marketplace™ Venture Capital simulation game to provide students with real world management experience in running a technology driven company. Teams of students playing roles as CEO, Marketing Manager, Manufacturing Manager, Financial Manager and R and D Manager develop the technology and marketing strategies for their companies as they compete against each other in a global environment. After four quarters of operation, students are required to prepare and deliver a 15 minute presentation to venture capitalists detailing their marketing and technology strategies, performance to date and expectations in the market for the remaining two quarters in the game simulation. They are competing against the other teams for the venture capitalist’s money and must not only have a good presentation but also demonstrate conceptual understanding of what the financial and market data means. The roles of the venture capitalists are played by retired professionals in the community that have run businesses with revenues exceeding $50 M/year, have started new technology based ventures or have managed researchers in a commercial environment. We instruct the venture capitalists to play the role as tough managers who require data and not fluff before they part with their precious venture capital financing. VC and Technology business managers must negotiate on the purchase price for shares of their company with lesser performing companies giving up a greater share of their company in the negotiation. Students utilize techniques presented in the first two classes in their curriculum (Investigations in Technology and Strategic Management of Technology and Innovation) to develop their marketing and technology strategies. The students appreciate the fact that they are able to take risks and make mistakes in a simulation environment where financial disasters are made with fake money. After utilizing this simulation program for three years, we have found that non- traditional students who have been working in an engineering field typically perform better than the traditional graduate students who are entering their graduate program immediately after receiving their bachelor’s degree. Our experience is that all engineering technology students (regardless of when they enter the program) are weak in their comfort and understanding of financial data and that this is a weakness that we need to correct in both the undergraduate and graduate programs.

Construction education is dynamic and practice oriented. As such, effective construction programs require significant collaboration with the construction industry. This collaboration, in the form of internship or cooperative programs, increase the student readiness for the job market by providing valuable field experience. Construction programs in the United States (US) established several internship requirements that range from being optional to multiple required internships. This study focuses on scanning the current internship requirements set by construction and engineering technology programs in the US by gathering information including; 1) Number and length of required internships, 2) Internship prerequisites, 3) Internship deliverables, 4) assessment method. The outcome of this study is expected to help construction programs improve their internship or cooperative requirements by considering the prevailing practices developed by other schools. Additionally, the study provides recommendations to enhance the effectiveness of internship for positive experiential learning.

A proper understanding of the local construction market is essential for making appropriate project budgeting and planning decisions. State highway agencies typically use highway construction cost indexes (HCCIs) to understand the current market conditions. In the U.S. highway construction industry, the Federal Highway Administration (FHWA) pioneered the concept of a HCCI as an indicator of the national construction market. State Departments of Transportation (DOT) also started developing their state level HCCIs to better represent their state level construction markets. But, some state DOTs noted the lack of guidance to develop and update their HCCIs. This paper summarizes literature review and nationwide questionnaire survey results to identify the current practices of calculating and using HCCIs. There are two methods to generate basket of construction items for HCCI calculation: a) categorized market basket and b) item level market basket. The Fisher index is the most popular indexing formula among the state DOTs and is also recommended by the FHWA and International Monetary Fund (IMF). Despite many potential users of HCCIs, the current use of HCCIs is very limited in state DOTs.

Theft of construction equipment, materials, and tools from construction sites results in approximately one billion dollars in direct annual losses to the U.S. construction industry per year. A better understanding of theft characteristics is vital to reducing this figure. This study analyzes over 15,000 incidents from the National Incident-Based Reporting System (NIBRS) to understand characteristics such as theft prevalence, average monetary losses, and recovery rates. The study finds that contractors lost an average of about $6,000 per incident. Trucks are the most expensive theft targets, with an average loss of about $42,000 per incident, and also the most likely item to be recovered (55% of the time). However, recovery rate across all targets was less than 7%. The results of this study provide the most accurate and extensive statistics to date on construction theft characteristics. The study also identifies best practices to reduce thefts such as the use of survellience systems. Further, the use of advanced marking and tracking systems to safeguard expensive equipment and vehicles and aid their recoveries are discussed. The findings are expected to aid contractors and law enforcement agencies in formulating methods for reducing thefts of construction items and improving the likelihood of their recoveries.