Using Neural Networks To Motivate The Teaching Of Matrix Algebra For K 12 And College Engineering Students

ABSTRACTUnderstanding the brain is a fascinating challenge, captivating the scientific community and the public alike. The lack of effective treatment for most brain disorders makes the training of the next generation of neuroscientists, engineers and physicians a key concern. Over the past decade there has been a growing effort to introduce neuroscience in primary and secondary schools, however hands-on laboratories have been limited to anatomical or electrophysiological activities. Modern neuroscience research labs are increasingly using computational tools to model circuits of the brain to understand information processing. Here we introduce the use of neurorobots - robots controlled by computer models of biological brains - as an introduction to computational neuroscience in the K-12 classroom. Neurorobotics has enormous potential as an education technology because it combines multiple activities with clear educational benefits including neuroscience, active learning, and robotics. We describe an introductory neurorobot workshop that teaches high school students how to use neurorobots to investigate key concepts in neuroscience, including spiking neural networks, synaptic plasticity, and adaptive action selection. Our do-it-yourself (DIY) neurorobot uses wheels, a camera, a speaker, and a distance sensor to interact with its environment, and can be built from generic parts costing about $150 in under 4 hrs. Our Neurorobot App visualizes the neurorobot's visual input and brain activity in real-time, and enables students to design new brains and deliver dopamine-like reward signals to reinforce chosen behaviors. We have tested the Neurorobot Workshop with high school students (n = 3 workshops, 9 students total) and have found that students were able to complete all exercises in under 3 hrs. In a post-workshop survey, students reported having gained the ability to develop neural networks that perform specific functions, including goal-directed behavior and memory. Here we provide DIY hardware assembly instructions, discuss our open-source Neurorobot App and demonstrate how to teach the Neurorobot Workshop. By doing this we hope to accelerate research in educational neurorobotics and promote the use of neurorobots to teach computational neuroscience in high school.

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Service and Design as Mechanisms to Impassion the Study of Engineering, from K-12 to Higher Education

International Journal for Service Learning in Engineering Humanitarian Engineering and Social Entrepreneurship ◽

10.24908/ijsle.v5i1.2149 ◽

2010 ◽

Vol 5 (1) ◽

pp. 25-46

Author(s):

Brent C Houchens

Keyword(s):

Engineering Education ◽

Graduate Education ◽

Personal Development ◽

Engineering Students ◽

Team Leadership ◽

Leadership Strategies ◽

Mentoring Program ◽

Traditional Course ◽

K 12 ◽

The Impact

Service and design provide mechanisms to introduce students to successive stages of engineering education. These activities positively influence outreach to K-12 students, recruiting of women and underrepresented minorities to engineering, retention of undergraduate engineering students, and encouragement and funding for graduate education. Furthermore, service and design provide continuity and motivation across engineering education. These offer experiential learning opportunities in practical problem solving, while simultaneously promoting personal development of communication skills and team leadership. Strategies are discussed for implementing service and design components in engineering education at all levels, from K-12 to graduate education. For K-12 outreach, a mentoring program called DREAM is highlighted. Opportunities for outreach and externally reviewed proposal writing and presentations are discussed in the context of undergraduate design. These can be implemented through both traditional course work and alternative design projects. Finally, the impact of all of the above activities on graduate education, particularly graduate funding, is discussed.

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Bridging Education and Engineering Students through a Wind Energy-Focused Community Engagement Project

Sustainability ◽

10.3390/su13169334 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9334

Author(s):

Maija A. Benitz ◽

Li-Ling Yang

Keyword(s):

Wind Energy ◽

Community Engagement ◽

Student Learning ◽

Engineering Students ◽

Fourth Graders ◽

Regional Growth ◽

Offshore Wind ◽

Science Standards ◽

Wind Energy Development ◽

K 12

Regional growth in offshore wind energy development, changes to the state’s K-12 science standards, and a desire to deepen undergraduate student learning coalesced to inspire an interdisciplinary community engagement project bridging university courses in engineering and education. The project consists of three main activities: a professional development event for local fourth grade teachers, five classroom lessons designed and taught by undergraduate engineering and education majors, and a final celebration event, all focused around the topics of wind energy and engineering design. This spring, the project was carried out for the third consecutive year, though each year’s implementation has been unique due to the timing of the onset of COVID-19. Analysis of responses from the Teaching Engineering Self-Efficacy Scale and an end-of-semester course survey demonstrate growth in student learning and transferrable skills from participating in the semester-long project. Additionally, exploration of students’ narrative work provides a richness to further understanding their growth and challenges they confronted. This interdisciplinary community engagement project will continue into future years, with improvements informed by the findings of this work, most notably with the hope of returning to a fully in-person delivery of lessons to fourth-graders.

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Teaching an Undergraduate Introductory Finite Element Analysis Course: Successful Implementation for Students Learning

Volume 5: Education and Globalization ◽

10.1115/imece2015-50091 ◽

2015 ◽

Cited By ~ 2

Author(s):

Awlad Hossain

Keyword(s):

Finite Element Analysis ◽

Finite Element ◽

Matrix Algebra ◽

Engineering Students ◽

Parametric Design ◽

Successful Implementation ◽

Element Analysis ◽

Entire Class ◽

Structural Problems ◽

Engineering Problems

In our institution, we offer a one-quarter long finite element analysis (FEA) class for Mechanical Engineering curriculum. This course teaches computational methods to solve engineering problems using the state of art FEA software ANSYS. The coursework involves teaching fundamental mathematical theories to build the concept, analyzing simple structural problems using matrix algebra, and then solving a wide variety of engineering problems dealing with statics, dynamics, heat transfer and others. Students enrolled in this class solve varieties of problem by analytical approach, finite element approach using matrix algebra, using APDL (ANSYS Parametric Design Language) and ANSYS Workbench. As we are in quarter system, it is challenging to solve additional multidisciplinary complex engineering problems in regular class lectures. Therefore, students enrolled in this class are required to conduct a project solvable by student version of ANSYS within very short time. The project must have adequate engineering complexity conveying interesting knowledge or technical concepts to the entire class. Students have to prepare a brief written report, and share what they have learned with the entire class giving an oral presentation. While a course in FEA could be a common offering in many universities, the author of this paper presents the pedagogical approaches undertaken to successfully implement the course objectives to the undergraduate engineering students. The topics and techniques applied to teach different concepts of FEA to enhance students learning outcomes are addressed in this paper.

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SMART RISK ANALYST

10.20850/9781534299504 ◽

2019 ◽

Author(s):

Dr. SHILPA LADDHA

Keyword(s):

Neural Network ◽

Neural Networks ◽

Credit Risk ◽

Engineering Students ◽

Arbitrary System ◽

Back Propagation Algorithm ◽

Feed Forward Neural Network ◽

Practical Applications ◽

Area Of Interest ◽

Artificial Neural

The book is a special practical guide to all who want to learn the Artificial Neural Networks from a perspective with its practical applications. Having the deep knowledge of theoretical concept, one can apply it for creative decision making. The book is demonstration of a case study which can be implemented on range of topics. This is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (India). She is Doctorate in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

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Diversity in k-12 initiatives to attract a diverse pool of engineering students

33rd Annual Frontiers in Education, 2003. FIE 2003. ◽

10.1109/fie.2003.1264736 ◽

2004 ◽

Author(s):

S.E. Powers ◽

M. Graham ◽

T. SchWob ◽

J. Dewaters

Keyword(s):

Engineering Students ◽

K 12

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4539 Building a Translational Science pipeline: The Indiana CTSI STEM K-12 Program

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.203 ◽

2020 ◽

Vol 4 (s1) ◽

pp. 57-58

Author(s):

Elmer Sanders ◽

Vanessa Barth ◽

Leigh-Ann Cruz ◽

Ilesha Sherrer ◽

Jacob Olson ◽

...

Keyword(s):

Science Teachers ◽

Health Informatics ◽

Engineering Students ◽

Professional School ◽

Underrepresented Minorities ◽

List Type ◽

Translational Science ◽

Public Health Informatics ◽

One Year ◽

K 12

OBJECTIVES/GOALS: Develop strong network of science teachers interested in promoting scientific research to their students.Place students in an immersive summer research internship that, when possible, matches their career interests.Expose students to the numerous career paths within the STEM field.METHODS/STUDY POPULATION: The program recruits socio-economically disadvantaged students and provides them a stipend, and also accepts students who can participate unpaid.Local school teachers are engaged in a summer fellowship to learn biotechnologies and research. In Spring these teachers help recruit students and during the subsequent Fall help students with college and scholarship applications.Students are placed in a variety of laboratories within the Schools of Medicine, Science, Dentistry, Public Health, Informatics, Health and Human Sciences, Engineering and Technology, especially in biomedical engineering. Students are also placed in industry laboratories such as Eli Lilly and the Indiana Bioscience Research Institute.Long-term program follow-up is done through post-internship surveys to assess impact on graduate and professional school admission.RESULTS/ANTICIPATED RESULTS: Since the Indiana CTSI was established in 2008, 872 students have participated in the summer internship.71% of past interns are underrepresented minorities in science or classified as disadvantaged by NIH criteria.17% of students interned during grade 10, 72% during grade 11, and 11% during grade 12.21% of students engage in the program for more than one year.100% of past interns are currently enrolled in or have graduated college.Over 60% of those with a bachelors degree proceed to graduate and professional schools and over 80% stay in STEM related fields. These rates are equal for interns from underrepresented minorities or those classified as disadvantaged by NIH criteria.DISCUSSION/SIGNIFICANCE OF IMPACT: Students engaged in the Indiana CTSI STEM program are progressing through the translational science pipeline based on their graduating from college and remaining in the STEM field.

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