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Description
Engineering education has long sought to incorporate greater diversity into engineering programs to prepare the profession to meet the engineering challenges of society. Increasing or retaining the conative diversity of engineering programs may help extend other kinds of diversity in the profession as well (Marburger, 2004). One measure of conation is the Kolbe ATM index.
Kolbe ATM is an index developed by Kathy Kolbe to measure the conative traits on an individual. The index assigns each individual a value in four categories, or Action Modes, that indicates their level of insistence on a scale of 1 to 10 in that Action Mode (Kolbe, 2004). The four Action Modes are:
• Fact Finder – handling of information or facts
• Follow Thru – need to pattern or organize
• Quick Start – management of risk or uncertainty
• Implementor – interaction with space or tangibles
The Kolbe A (TM) index assigns each individual a value that indicates their level of insistence with 1-3 representing resistant, preventing problems in a particular Action Mode; 4-6 indicating accommodation, flexibility in a particular Action Mode; and 7-10 indicating insistence in an Action Mode, initiating solutions in that Action Mode (Kolbe, 2004).
To promote retention of conative diversity, this study examines conative diversity in two engineering student populations, a predominately freshmen population at Chandler Gilbert Community College and a predominately junior population at Arizona State University. Students in both population took a survey that asked them to self-report their GPA, satisfaction with required courses in their major, Kolbe ATM conative index, and how much their conative traits help them in each of the classes on the survey. The classes in the survey included two junior level classes at ASU, Engineering Business Practices and Structural Analysis; as well as four freshmen engineering classes, Physics Lecture, Physics Lab, English Composition, and Calculus I.
This study finds that student satisfaction has no meaningful correlation with student GPA.
The study also finds that engineering programs have a dearth of resistant Fact Finders from the freshmen level on and losses resistant Follow Thrus and insistent Quick Starts as time progresses. Students whose conative indices align well with the structure of the engineering program tend to consider their conative traits helpful to them in their engineering studies. Students whose conative indices misalign with the structure of the program report that they consider their strengths less helpful to them in their engineering studies.
This study recommends further research into the relationship between satisfaction with major and conation and into perceived helpfulness of conative traits by students. Educators should continue to use Kolbe A (TM) in the classroom and perform further research on the impacts of conation on diversity in engineering programs.
Kolbe ATM is an index developed by Kathy Kolbe to measure the conative traits on an individual. The index assigns each individual a value in four categories, or Action Modes, that indicates their level of insistence on a scale of 1 to 10 in that Action Mode (Kolbe, 2004). The four Action Modes are:
• Fact Finder – handling of information or facts
• Follow Thru – need to pattern or organize
• Quick Start – management of risk or uncertainty
• Implementor – interaction with space or tangibles
The Kolbe A (TM) index assigns each individual a value that indicates their level of insistence with 1-3 representing resistant, preventing problems in a particular Action Mode; 4-6 indicating accommodation, flexibility in a particular Action Mode; and 7-10 indicating insistence in an Action Mode, initiating solutions in that Action Mode (Kolbe, 2004).
To promote retention of conative diversity, this study examines conative diversity in two engineering student populations, a predominately freshmen population at Chandler Gilbert Community College and a predominately junior population at Arizona State University. Students in both population took a survey that asked them to self-report their GPA, satisfaction with required courses in their major, Kolbe ATM conative index, and how much their conative traits help them in each of the classes on the survey. The classes in the survey included two junior level classes at ASU, Engineering Business Practices and Structural Analysis; as well as four freshmen engineering classes, Physics Lecture, Physics Lab, English Composition, and Calculus I.
This study finds that student satisfaction has no meaningful correlation with student GPA.
The study also finds that engineering programs have a dearth of resistant Fact Finders from the freshmen level on and losses resistant Follow Thrus and insistent Quick Starts as time progresses. Students whose conative indices align well with the structure of the engineering program tend to consider their conative traits helpful to them in their engineering studies. Students whose conative indices misalign with the structure of the program report that they consider their strengths less helpful to them in their engineering studies.
This study recommends further research into the relationship between satisfaction with major and conation and into perceived helpfulness of conative traits by students. Educators should continue to use Kolbe A (TM) in the classroom and perform further research on the impacts of conation on diversity in engineering programs.
ContributorsSmith, Logan Farren (Author) / Seager, Thomas P. (Thesis director) / Adams, Elizabeth A. (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2015-05
Description
In the Spring 2013 and Fall 2013 semesters, a survey was taken of students enrolled in the principal undergraduate civil engineering structures course, CEE 321: Structural Analysis and Design, to assess both the prevalence of technology in the lives of the students and the potential ways this information could be use to improve the educational experience. The results of this survey indicated that there was a considerable demand for additional online resources outside of the formal classroom. The students of CEE 321 requested online lecture videos in particular, and so a project was launched at the start of the Spring 2014 semester to deliver a large body of academic instructional videos. In total, a collection of 30 instructional videos which covered all key topics covered over a semester of CEE 321 was published. The driving interest behind this creative project is to increase the level of understanding, comfort, and performance in students enrolled in the class. Although the quantity of initial student feedback is relatively small, the reactions are distinctly positive and reflect an improvement in understanding amongst the responding students. Over the course of upcoming semesters, qualitative and quantitative assessments of the impact of the videos are expected to provide a better indication of their quality and effectiveness in supporting student comprehension and performance in CEE 321. Above all, the success of these videos is directly tied to their ability to function as living, adaptable resources which are continuously molded and improved by student feedback.
ContributorsReasor, Drew Donn (Author) / Rajan, Subramaniam (Thesis director) / Hjelmstad, Keith (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2014-05
Description
Clean and accessible drinking water is a crucial and limited resource. As the world's population grows and demand increases, water resources will become more limited. This project aims to educate students on water resources, drinking water, and how biomimicry can allow society to improve its water usage. The project consists of a ten day unit plan which addresses several water topics such as: the various uses of water, water distribution, where drinking water comes from, the water treatment process, and more. After establishing background knowledge on water and surrounding issues, the students will be challenged to design a water bottle using biomimicry. Biomimicry is looking at nature to draw and inspire solutions to human problems. This unit has been optimized for use by elementary teachers. The ten day unit consists of a lesson summary, objectives, standards, and recommended activities for each day. Of the ten days, three lesson plans were fully developed using the 5E format. The research supporting this project is compiled in the following report.
ContributorsSalik, Rachael (Co-author) / Burke, Aurora (Co-author) / Walters, Molina (Thesis director) / Larson, Kelli L. (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Productivity in the construction industry is an essential measure of production efficiency and economic progress, quantified by craft laborers' time spent directly adding value to a project. In order to better understand craft labor productivity as an aspect of lean construction, an activity analysis was conducted at the Arizona State University Palo Verde Main engineering dormitory construction site in December of 2016. The objective of this analysis on craft labor productivity in construction projects was to gather data regarding the efficiency of craft labor workers, make conclusions about the effects of time of day and other site-specific factors on labor productivity, as well as suggest improvements to implement in the construction process. Analysis suggests that supporting tasks, such as traveling or materials handling, constitute the majority of craft labors' efforts on the job site with the highest percentages occurring at the beginning and end of the work day. Direct work and delays were approximately equal at about 20% each hour with the highest peak occurring at lunchtime between 10:00 am and 11:00 am. The top suggestion to improve construction productivity would be to perform an extensive site utilization analysis due to the confined nature of this job site. Despite the limitations of an activity analysis to provide a complete prospective of all the factors that can affect craft labor productivity as well as the small number of days of data acquisition, this analysis provides a basic overview of the productivity at the Palo Verde Main construction site. Through this research, construction managers can more effectively generate site plans and schedules to increase labor productivity.
ContributorsFord, Emily Lucile (Author) / Grau, David (Thesis director) / Chong, Oswald (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020