Enhancing Team-Based Senior Capstone Projects: Opportunities and Challenges

Generally, capstone courses focus on the integration and application of technical knowledge and skills acquired in previous coursework along with a consideration of multiple realistic constraints. However, capstone courses also require students to focus on a variety of professional skills, including teamwork, unstructured task completion, and project management. Because students are often new at these skills, they may find it difficult to resolve issues as they arise, particularly when working with an actual industrial client. Capstone courses also pose challenges to faculty. Finding a consistent stream of projects that are at the appropriate level for senior level students can be difficult for faculty given the limited time frame of most courses. A fair and consistent method to evaluate student work on an individual and group level is also a challenge for instructors. This paper will outline the challenges and best practices learned in the development and implementation of a senior-level capstone course in engineering technology, based on qualitative data gathered over several years. Specifically, strategies for sourcing student projects, student team formation and management, and options for ensuring accountability among student teams will also be discussed. Ideas on fair and consistent assessment methods for group and individual work will also be emphasized

Factors Affecting College Students\u27 Knowledge and Opinions of Genetically Modified Foods

The use of biotechnology in food and agricultural applications has increased greatly during the past decade and is considered by many to be a controversial topic. Drawing upon a previous national study, a new survey was conducted of U.S. and international college students at a large, land-grant, Research University to determine factors that may affect opinions about genetically modified (GM) food products. Factors examined included nationality, discipline area of study, perceptions of safety, and awareness and levels of acceptance regarding GM food. Results indicated students born outside the United States had more negative opinions about genetically modified foods than did American-born students. Students who were studying a physical science-based curriculum had a more positive opinion of GM food than did students studying a curriculum that was not based in the physical science. In addition, students who reported a higher level of acceptance of genetically modified foods felt more positively about the safety of the technology

Applied machine learning in agro-manufacturing occupational incidents

Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to serious and life-threatening injuries. The aim of this study is to give insight into safety risks in grain handling facilities through information processing of workers\u27 compensation data on agro-manufacturing occupational incidents within commercial grain elevators in the Midwest region of the United States between 2008 and 2016. The severity of occupational incidents is determined by total dollar amount incurred on medical, indemnity, and other expenses in workers\u27 compensation claims. The most important factors that affect the cost escalation of occupational incidents are extracted using bootstrap partitioning method, and are applied as input for constructing two machine learning models: random forests decision trees, and naïve Bayes. Both models show high accuracy (87.64% and 92.78% respectively) in predicting that a future claim is classified as either low or medium, severity. The models contribute to identifying high injury risk groups, and prevalent incident causes, allowing a more research-based focused intervention effort in grain handling workplaces. In addition, the results are applicable in forecasting cost severity of future claims, and identifying factors that contribute to the escalation of claims costs

Applied machine learning in agro-manufacturing occupational incidents

Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to serious and life-threatening injuries. The aim of this study is to give insight into safety risks in grain handling facilities through information processing of workers\u27 compensation data on agro-manufacturing occupational incidents within commercial grain elevators in the Midwest region of the United States between 2008 and 2016. The severity of occupational incidents is determined by total dollar amount incurred on medical, indemnity, and other expenses in workers\u27 compensation claims. The most important factors that affect the cost escalation of occupational incidents are extracted using bootstrap partitioning method, and are applied as input for constructing two machine learning models: random forests decision trees, and naïve Bayes. Both models show high accuracy (87.64% and 92.78% respectively) in predicting that a future claim is classified as either low or medium, severity. The models contribute to identifying high injury risk groups, and prevalent incident causes, allowing a more research-based focused intervention effort in grain handling workplaces. In addition, the results are applicable in forecasting cost severity of future claims, and identifying factors that contribute to the escalation of claims costs

Applied machine learning in agro-manufacturing occupational incidents

Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to serious and life-threatening injuries. The aim of this study is to give insight into safety risks in grain handling facilities through information processing of workers\u27 compensation data on agro-manufacturing occupational incidents within commercial grain elevators in the Midwest region of the United States between 2008 and 2016. The severity of occupational incidents is determined by total dollar amount incurred on medical, indemnity, and other expenses in workers\u27 compensation claims. The most important factors that affect the cost escalation of occupational incidents are extracted using bootstrap partitioning method, and are applied as input for constructing two machine learning models: random forests decision trees, and naïve Bayes. Both models show high accuracy (87.64% and 92.78% respectively) in predicting that a future claim is classified as either low or medium, severity. The models contribute to identifying high injury risk groups, and prevalent incident causes, allowing a more research-based focused intervention effort in grain handling workplaces. In addition, the results are applicable in forecasting cost severity of future claims, and identifying factors that contribute to the escalation of claims costs

Application of Quality Management Systems to Grain Handling: An Inventory Management Case Study

To help meet the production gap of a growing population, the agricultural industry is incorporating new quality management practices to improve operational efficiency. In the agricultural supply chain, operations management within the grain handling industry represents an important area for quality management improvement to meet the growing needs for food safety and security. The strong growth in the use of quality management systems in agricultural environments reflects interest from the production agricultural industry. The present case study examines the impact of implementing a quality management system at a large, multi-site grain elevator company by comparing selected quality metrics before and after QMS adoption. After adoption, the company statistically reduced the grain quality measurement error in grading damage and foreign material, resulting in significantly greater value to shipped grain. The company was also able to add monetary value to low-value grain by using quality metrics to optimize their inventory management and blending strategy. Significant gains were not made in all areas examined, but generally, quality management systems added internal efficiencies and provided a means of adding value to low-quality grains within the grain elevator studied