Thisstudy explored the effects that life and work stress have on employee safety decision-makingunder three cognitive biases that hinder safety participation. Utilityemployees of municipalities and companies responded to (1) a survey regardingtheir work and global life stress levels and (2) decision simulationsconcerning safety orientation. The simulation scenarios were framed tofacilitate conditions highlighting Recency, Melioration, and Free-ride SocialDilemma biases.Perceived life stress was not a significant predictor of atendency to engage in safety in any of the biases. Work stress was asignificant predictor of reducing engagement in safety in the Recency biasonly. The study was conducted during the peak of COVID-19. COVID-19, primarilythe loss of someone close due to COVID-19, had significant effects under allthree bias conditions. Opposite effects of several variables in differentbiases hint at the need to further research the effects of these biases onsafety participation.

Creating safe work environment is significant in saving workers’ lives, improving corporates’ social responsibility and sustainable development. Pattern identification in occupational accidents is vital in elaborating efficient safety countermeasures aiming at improving prevention and mitigating outcomes of future incidents. The objective of this study is to identify patterns related to the occurrence of occupational accidents in non-farm agricultural work environments based on workers’ compensation claims data, using latent class clustering method as an unsupervised machine learning modeling approach. The result showed injury profiles and incident dynamics have low, average, and high levels of risks based on the main causes and outcomes of the injuries and the affected body part(s).

The Scholarship of Teaching and Learning (SoTL) aligns with many institutionally espoused values regarding the value of teaching and learning excellence. SoTL has increased in prestige and value in the past decade, but less information about the proliferation of SoTL within one institution is known. Through an examination of 10 years of curriculum vitae submitted for successful promotion and tenure, our study investigated faculty members’ engagement in SoTL over time and differences in engagement by rank, race, gender, discipline, and type of SoTL artifact. Over the 10-year period, the percentage of faculty engaged in SoTL did not differ significantly. We did uncover differences by disciplinary type and race. Faculty were most likely to engage in grants and presentations related to SoTL and least likely to have a peer-reviewed journal artifact. Our findings suggest that although SoTL efforts continue to gain acceptance within the higher education milieu, institutional and disciplinary realities may be powerful determiners of SoTL participation.

AbstractThis article describes equity‐focused online facilitation. Online approaches for fostering equity and strategies for inclusive practices are explained.

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. One of the leading causes of occupational incidents in all industries, including grain elevators, is slip, trip, and fall (STF). Therefore, prediction of STF incidents prior to occurrence is significant in occupational safety analysis. Despite high frequency of STF incidents at work, exploring their dominant factors via machine learning algorithms in agro-manufacturing environments is relatively new or unaddressed. Safety professionals may utilize the prediction and analysis of determinant factors of occupational incidents for actionable prevention and safety mitigation planning and practices. The objective of this research is to describe the slip-trip-fall (STF) injuries and trends in a population of agribusiness operations workers within commercial grain elevators in the Midwest of the United States, identify risk factors for STF injuries, and develop prevention strategies for STF hazards.

Due to the high frequency and costs of occupational incidents in agro-manufacturing operations, as well as substantial impact of occupational injuries on labor-market outcomes, predicting the post-incident state of an injury and identifying its contributory factors is vital to protect workers, improve workplace safety, and reduce overall costs of injuries. This study evaluates the performance of machine learning algorithms in classifying post-incident outcomes of occupational injuries in agro-manufacturing operations. Injury factors extracted from 14,000 workers’ compensation claims recorded between 2008 and 2016 in the Midwest region of the United States were used to develop machine learning models. The models predicted incident outcomes based on injury details with an overall accuracy rate of 78%, and high accuracy rate for medical post-incident state (97–98%). The results emphasize the significance of quantitative analysis of empirical injury data in safety science, and contributes to enhanced understanding of occupational incidents root causes using predictive modeling along with safety experts’ perspective.

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’ 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’ 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.

Agribusiness industries are among the most hazardous workplaces for non-fatal occupational injuries. The term "post- incident state" is used to describe the health status of an injured person when a non-fatal occupational injury has occurred, in the post-incident period when the worker returns to work, either immediately with zero days away from work (medical state) or after a disability period (disability state). An analysis of nearly 14,000 occupational incidents in agribusiness operations allowed for the classification of the post-incident state as medical or disability (77% and 23% of the cases, respectively). Due to substantial impacts of occupational incidents on labor-market outcomes, identifying factors that influence the severity of such incidents plays a significant role in improving workplace safety, protecting workers, and reducing costs of the post-incident state of an injury. In addition, the average costs of a disability state are significantly higher than those of a medical state. Therefore, this study aimed to identify the contributory factors to such post-incident states with logistic regression using information from workers' compensation claims recorded between 2008 and 2016 in the Midwest region of the United States. The logistic regression equation was derived to calculate the odds of disability post-incident state. Results indicated that factors influencing the post-incident state included the injured body parts, injury nature, and worker's age, experience, and occupation, as well as the industry, and were statistically significant predictors of post-incident states. Specific incidents predicting disability outcomes included being caught in /between/under,fall/slip/trip injury, and strain/injury by. The methodology and estimation results provide insightful understanding of the factors influencing medical/disability injuries, in addition to beneficial references for developing effective countermeasures for prevention of occupational incidents.

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers' compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents' risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

One of the principle objectives in occupational safety analysis is to identify the key factors that affect the severity of an incident. To identify risk groups of occupational incidents and the factors associated with them, statistical analysis of workers’ compensation claims data is performed using latent class clustering, for the segmentation of 1031 severe occupational incidents in agribusiness industries in the Midwest region of the United States between 2008–2016. In this study, severe incidents are those with workers’ compensation costs equal to or greater than $100,000 (USD). Based on the latent class clustering results, three risk groups are identified with injury nature as the most statistically distinctive classifier. The highest cost injuries include strain, tear, fracture, contusion, amputation, laceration, burn, concussion, and crushing. The most prevalent and statistically significant injury type is permanent partial disability. The study introduces a novel application of latent class clustering in the segmentation of high severity occupational incidents. The analytical approach and results of this study will aid safety practitioners in identifying occupational risk groups and analyzing injury patterns, and inform safety intervention plans to avoid the occurrence of similar incidents in agribusiness industries.

Educational literature has long supported strong correlations between student motivation and academic success. STEM literature has more recently shown mechatronic experiences to have positive impacts on these constructs, albeit limited empirical grounding. Therefore, the purpose of this study was to conduct a pilot experiment to empirically quantify differences in undergraduate student motivation and academic success in a mechatronic vs. a non-mechatronic experience, as well as examine the correlation between student motivation and academic success in both groups. We used a quasiexperimental, non-equivalent control vs. treatment design to collect n = 84 responses from multiple sections of a single undergraduate course. The multivariate dependent variable of student motivation was measured using the Motivated Strategies for Learning Questionnaire’s motivational orientation items. Our multivariate dependent variable of academic success was based on final course grades, final project scores, and quiz scores. Using ANCOVA and differences of proportions, we found no statistical difference in motivational orientation—specifically value choices and expectancy beliefs—in the mechatronic vs. non-mechatronic experience. In contrast, statistically significant differences in project scores and final course grades were observed in the mechatronic experience group. Additionally, we found no significant correlation between student motivation and academic success. These results indicated that students in the mechatronic experience, while earning significantly higher grades, did not exhibit different levels of motivation, leading to no association between student motivation and academic success. Even so, future research is needed to further understand the nuanced dynamics of motivational orientation within a mechatronic experience.

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied. The models can predict injury severity classification based on injured body part, body group, nature of injury, nature group, cause of injury, cause group, and age and tenure of injured workers with the accuracy rate of 92–98%. The results emphasize the significance of quantitative analysis of empirical injury data in safety science, and contribute to enhanced understanding of injury patterns using predictive modeling along with safety experts’ perspectives with regulatory or managerial viewpoints. The predictive models obtained from this study can be used to augment the experience of safety professionals in agribusiness industries to improve safety intervention efforts.

Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers' compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R-2 = 0.79). Injury characteristics and worker's occupation were predictive of large claims' occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers' compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.

This study investigated the role of a new paradigm in teaching large introductory, fundamental engineering mechanics (IFEM) courses that combined student-centered learning pedagogies and supplemental learning resources. Demographic characteristics in this study included a total of 405 students, of whom 347 (85.7%) are males and 58 are (14.3%) females. The students’ majors included aerospace engineering, agricultural engineering, civil engineering, construction engineering, industrial engineering, materials engineering, and mechanical engineering. Results of this study, as tested using an independent samples t-test, validated using a nonparametric independent samples test, and a general linear multivariate model analysis, indicated overwhelmingly that there is a difference between a class taught passively using the teacher-centered pedagogy and a class taught actively using student-centered pedagogy. The principal focus of this work was to determine if the new paradigm was successful in improving student understanding of course concepts in statics of engineering using student-centered pedagogies in large classes. After evaluating the effects of several variables on students’ academic success, the results may provide important information for both faculty members and researchers and present a convincing argument to faculty members interested in academic reform but hesitant to abandon conventional teaching practices. By promoting a new paradigm, the potential for improving understanding of engineering fundamentals on a larger scale may be realized.

The development and adoption of promotion and tenure (P&T) policies supporting scholarship of teaching and learning (SoTL) activities began in earnest at our large, Midwestern, land-grant university, over fifteen years ago. The purpose of this research was to present the results of a five year project collecting and analyzing the evidence of SoTL on P&T vitas. Every occurrence of SoTL in peer-reviewed publications, presentations, and funded external and internal grants was counted. Of the 343 successful cases, there were 201 to associate professor and 142 to professor. Of these cases, 48% (n=163) had at least one SoTL artifact included on their vitas. These results suggest SoTL is a meaningful form of scholarship performed by faculty, however, there is additional cultural change necessary for greater integration of SoTL in faculty work.

Experiential learning achieved through work experiences has become essential to engineering and engineering technology programs as they seek methods to assess a student's preparedness for entry into the workplace and to evaluate programs for the achievement of learning outcomes. These experiences allow students to apply the knowledge and skills they have acquired in the classroom to actual workplace environments and obtain feedback on the achievement of these competencies through direct employer assessments. Experiential learning environments also provide students the opportunity for personal reflection via self-assessments. This longitudinal study compared the strength of the relationship between aggregated workplace competency assessment ratings collected from employment supervisors and the paired undergraduate internship or coop student's self-assessment ratings. The principal focus of the study examined the reliability of student self-assessment ratings based on the comparison with paired supervisor assessment ratings. This study compared the assessment ratings across 14 workplace competencies identified in an earlier study by more than 200 constituents for the College of Engineering and validated through the expertise of a global leader in talent focused programs to assess and address workforce development. A fifteenth competency was later added to the COE competencies to address safety awareness. These 15 workplace competencies were further validated using a combined total of 64 clearly observable and measurable key actions that could be quantified using a 5-point Likert scale. The data were collected from undergraduate student internships and co-ops, which included 29 internship assessment terms from fall 2001 through Fall 2011, and organized by the 2001-05 and 2006-11 assessment terms relative to the corresponding accreditation cycle. Wilcoxon signed rank tests were administered as a non-parametric equivalent to the paired t-test, which determined the median difference between supervisor assessment and student self-assessment ratings for the 15 workplace competencies. Testing was further refined using the Bonferroni correction for multiple comparisons to correct for multiple statistical tests performed simultaneously. Results from the data validate that the university's assessment method has the ability to track student self-assessment ratings reliably for workplace key actions. Results from this study could be used to establish a baseline for post-graduate surveys given within the first 1-3 years following a graduate's entry into the workplace and to study the change in self-assessments as young alumni transition from novice toward expertise within their field of study. The data validate the use of student self-assessment ratings as reliable feedback to support continuous improvement of program curricula.

In the past ten years, engineering classrooms have seen an exponential growth in the use of technology, more than during any other previous decade. Unprecedented advancements, such as the advent of innovative gadgets and fundamental instructional alterations in engineering classrooms, have introduced changes in both teaching and learning. Student learning in introductory, fundamental engineering mechanics (IFEM) courses, such as statics of engineering, mechanics of materials, dynamics, and mechanics of fluids, as in any other class, is influenced by the experiences students go through in the classroom. Thus, bold new methodologies that connect science to life using student-centered approaches and scaffolding pedagogies need to be emphasized more in the learning process. This study is aimed to gain insight into the role of student-centered teaching, particularly the implementation of scaffolding pedagogies into IFEM courses. This study also attempts to contribute to the current national conversation in engineering education of the need to change its landscape—from passive learning to active learning. Demographic characteristics in this study included a total of 3,592 students, of whom 3,160 (88.0%) are males and 432 (12.0%) are females, over a period of six years, from 2007 to 2013. The students’ majors included aerospace engineering, agricultural engineering, civil engineering, construction engineering, industrial engineering, materials engineering, and mechanical engineering. Results of the study, as tested using a general linear univariate model analysis, indicated that overwhelmingly the type of class in statics of engineering is a significant predictor of student “downstream” performance in tests measuring their knowledge of mechanics of materials. There is a statistically significant difference in students’ performance in mechanics of materials depending on whether they were taught passively using the teacher-centered pedagogy or taught actively using the student-centered pedagogy in statics of engineering. Mechanics of materials is commonly the next immediate course, or a downstream course, following statics of engineering.

IntroductionIFEM classes, which include statics of engineering, mechanics of materials, dynamics, and mechanics of fluids are essential components of many engineering disciplines (Steif & Dollar, 2008). This study is an evaluation of a new paradigm incorporating a pedagogical reform that was performed over a period of six years at Iowa State University (ISU) in its College of Engineering. The focus of the new paradigm was on using student-centered and scaffolding approaches to promote better understanding of conceptual fundamental knowledge for students and to see whether there were significant predictors in student performance from an upstream class (statics of engineering) to a downstream class (mechanics of materials) in the same sequence.For many decades now, engineering education has heard some loud discussions of a new paradigm, which involve learning-centered community in classrooms, transformational faculty development, and institutional change (Mayer et al., 2012). These discussions are centered around two popular paradigms-teachercentered and student-centered (Huba & Freed, 2000). The teacher-centered paradigm involves knowledge transmission from teacher to students who passively receive information. In the teacher-centered paradigm, assessments are used to monitor with an emphasis on getting the correct answer and the culture is competitive and individualistic. These features are contrasted by the student-centered paradigm, which actively involves students in constructing knowledge. The student-centered method emphasizes generating questions, from errors, and assessments that are used to diagnose and promote learning. The student-centered culture is cooperative, collaborative, and supportive-wherein both the students and instructor learn (Huba & Freed, 2000). For many decades experiments on a new paradigm involving learning-centered community have been numerously performed, however none in the size and scope of this study. The authors wish to demonstrate and replicate the positive effectiveness of scaffolding in small classes by demonstrating it over 6 years, involving thousands of students.This quantitative study was designed to explore variables affecting student academic success, with the hope of effectively investigating the most fruitful way to teach IFEM courses, and to determine whether an experimental pedagogy class centered on scaffolding and cooperative pedagogies is a strong predictor of student performance. The variables included demographic characteristics and grades earned in two classes-the upstream class (statics of engineering) and the downstream class (mechanics of materials). This study was conducted using data over a period of 6 years, from 2007 to 2013, in both statics of engineering (EM 274) and mechanics of materials (EM 324) at Iowa State University from multiple instructors teaching multiple sections.In the past, statics of engineering has often been taught in a traditional lecture and note-taking approach. This study echoes the works of others in the field of engineering education and makes use of student-centered in statics of engineering (Benson, Orr, Biggers, Moss, Ohland, & Schiff, 2010). The key element of this study is the use of active and cooperative engagements in class.Literature ReviewScaffolding in TeachingThe concept of scaffolding in recent years has become the topic of much discussion and the focus of new research in engineering education. Researchers and educators (Mayer et al., 2012; Schmidt, Loyens, Van Gog, & Paas, 2007) are beginning to take a new perspective to the nature and the importance of scaffolding and how it ties with student-centered learning. Scaffolding refers to the learning supports and aids put in place to allow students to more easily come to grips with new course material that would otherwise be too complex to readily understand (Putnam, O'Donnell, & Bertozzi, 2010, p. …