This paper addresses the transition of community college students to degree programs in science, technology, engineering, and mathematics (STEM). The paper presents the results of an evaluation of a two-week residential summer bridge program that recruited community college students from a wide range of academic, ethnic, and socioeconomic backgrounds and included traditional and innovative elements to address academic, social, and career needs. Evaluation data were obtained from preand postsurveys, focus groups, and annual tracking surveys about subsequent academic choices and course completion. Results identify the factors that increase the confidence and motivation of students to pursue STEM undergraduate degrees. Student rankings indicate that they found the innovative elements of the bridge program to be the most valuable and transformative in their academic success.A number of recent national reports, such as Rising above the gathering storm: Energizing and employing America for a brighter economic future (National
This study explores how graduate students learn to participate in collaborative international science research. As part of an NSF-funded program, 21 graduate students participated in extended research visits to China, completing surveys before and after traveling, and participating in semistructured interviews upon returning to the U.S. These survey and interview data were qualitatively analyzed to determine how graduate student participants defined collaboration and how they positioned their own research experience in an international context. Data were coded using emergent thematic analysis via a first pass open-coding to generate a comprehensive list of descriptive codes for collaboration and then a synthesis of these codes through discussions guided by theories of situated learning in communities of practice. Findings suggest that all graduate students emphasized the importance of effective communication in collaboration. Graduate students also described collaboration as including at least one of the following elements: complementary expertise, shared goals, joint publications, and mutual learning. These findings provide insight into graduate students’ experiences with collaboration, and, in turn, how to support graduate students so that they have successful international research experiences and collaborations with international colleagues.
In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning individuals to the modal state, based on the estimated posterior probabilities. This procedure is not satisfactory, however, when various types of classification errors have different costs. For example, Lenaburg used Bayesian network methods to forecast students’ grades in a college statistics course to identify students who were likely to benefit from extra tutoring, and was most concerned with incorrectly predicting students would pass. We recommend a simple post hoc classification method, based on discrete loss functions, that can lead to improved classification. We further propose that Cohen’s weighted kappa statistic be used to evaluate the quality of the classification decisions. We illustrate the approach using Lenaburg’s data.
Tyler Susko is a Lecturer PSOE at the University of California Santa Barbara in the department of mechanical engineering where he is responsible for the mechanical engineering design program. Prior to this appointment, he completed his PhD from MIT in mechanical engineering where his research focused on the development of a novel robotic system for the treatment of neurological injuries affecting movement, specifically gait. He has previously held positions as a design engineer at Ingersoll Rand and an adjunct professor at Augusta State University. A coupled course design to strengthen multidisciplinary engineering capstone design projects Abstract Multidisciplinary Capstone Design courses are becoming a focus of engineering institutions as multidisciplinary skills have become a priority for accreditation and have shown promise for the development of young engineers. Most of the implementations are done using a stand-alone course or a dedicated section of a capstone course which involves a high institutional resource cost. Here we propose a Supplementary Multidisciplinary Capstone Course (SMCC) to be coupled to the departmental capstone courses to promote quick adoptions of multidisciplinary capstone projects without sacrificing discipline specific rigor. Two student surveys and one endof-quarter grading rubric are used to assess the merits of the coupled course design through the first quarter of a three quarter capstone series. Results of the surveys show that the SMCC course structure resolves student meeting scheduling problems by mandating attendance and retains departmental rigor by having advisors directly assigned in the departmental capstone course. We found that highly motivated teams with defined projects thrive with this model but that industry-defined projects require increased communication for all involved faculty and industry mentors at the onset of the project.
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