Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Multidisciplinary Undergraduate Nanoscience and Nanotechnology Program at the University of North Dakota
299
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
This paper describes some of the results of a National Science Foundation Nanotechnology Undergraduate Education project that aims to establish a nanoscience and nanotechnology program at the University of North Dakota. The goal is to generate new interest in nanoscience and nanotechnology among engineering and science students and prepare them with the knowledge and skills necessary for the next generation of graduates to compete in the global market and contribute to the nanoscience and nanotechnology field. The project explored several aspects of student learning, including students motivations for investigating nanotechnology through interdisciplinary coursework. To collect this information, a survey was administered to students who enrolled to two nanoscience and nanotechnology courses. Data collected from the survey will be used to improve the design and delivery of future courses as part of constructing a complete nanoscience and nanotechnology curriculum.
rate research
Read More
The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelors level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities.
Nanotechnology has emerged as a broad, exciting, yet ill-defined field of scientific research and technological innovation. There are important questions about the technologys potential economic, social, and environmental implications. We discuss an undergraduate course on nanoscience and nanotechnology for students from a wide range of disciplines, including the natural and social sciences, the humanities, and engineering. The course explores these questions and the broader place of technology in contemporary societies. The course is built around active learning methods and seeks to develop the students critical thinking skills, written and verbal communication abilities, and general knowledge of nanoscience and nanoengineering concepts. Continuous assessment was used to gain information about the effectiveness of class discussions and enhancement of student understanding of the interaction between nanotechnology and society.
With the recent implementation of the K to 12 Program, academic institutions, specifically, Colleges and Universities in the Philippines have been faced with difficulties in determining projected freshmen enrollees vis-a-vis decision-making factors for efficient resource management. Enrollment targets directly impacts success factors of Higher Education Institutions. This study covered an analysis of various characteristics of freshmen applicants affecting their admission status in a Philippine university. A predictive model was developed using Logistic Regression to evaluate the probability that an admitted student will pursue to enroll in the Institution or not. The dataset used was acquired from the University Admissions Office. The office designed an online application form to capture applicants details. The online form was distributed to all student applicants, and most often, students, tend to provide incomplete information. Despite this fact, student characteristics, as well as geographic and demographic data based on the students location are significant predictors of enrollment decision. The results of the study show that given limited information about prospective students, Higher Education Institutions can implement machine learning techniques to supplement management decisions and provide estimates of class sizes, in this way, it will allow the institution to optimize the allocation of resources and will have better control over net tuition revenue.
The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tintos Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).
We detail an experimental programme we have been testing in our university. Our Advanced Hackspace, attempts to give all members of the university, from students to technicians, free access to the means to develop their own interdisciplinary research ideas, with resources including access to specialized fellows and biological and chemical hacklabs. We assess the aspects of our programme that led to our community being one of the largest collectives in our university and critically examine the successes and failures of our trial programmes. We supply metrics for assessing progress and outline challenges. We conclude with future directions that advance interdisciplinary research empowerment for all university members.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
