Paper ID #36772Neuromorphic VLSI design courseAnu Aggarwal (Dr) Hello © American Society for Engineering Education, 2022 Powered by www.slayte.com Neuromorphic VLSI design courseThis paper describes a novel Neuromorphic VLSI design course that was added to theElectrical and Computer Engineering curriculum at our university. 1. SignificanceNeuromorphic VLSI design has been a research area for over 3 decades. It started withattempts at building silicon chips that could emulate functions of various brain regions likeeye and cochlea [1]. With Moore’s law hitting physical limits, the industry is
approach to study how engineering faculty understand the role of teacher empathy, howstudents understand teacher empathy, the potential barriers to demonstrating teacher empathy tostudents, and how teacher empathy impacts student success.Introduction: Empathy and Teacher EmpathyEmpathy has been explored in multiple fields, including psychology [1] and business [2], and inthe so-called “helping professions” such as nursing [3] and teaching [4]. The extensiveapplication of empathy in various disciplines implies its potential significance for developingstudent-professor relationships [5]. A considerable part of teaching includes interacting withstudents during classes and office hours. In these environments, professors and students usuallydiscuss
-year students in EP. He also enjoys wilderness backpacking beyond the reach of cell phones. © American Society for Engineering Education, 2022 Powered by www.slayte.com Specifications Grading in General Physics and Engineering Physics CoursesAbstractSpecifications grading (“specs grading,” or “standards-based grading”) bases course assessmenton students’ meeting various course objectives, each at or above a minimum level of proficiency.While there can be a wide range of variability among courses that pursue “specs grading,” almostall share the following features: (1) all assessments are graded pass/fail; (2) students are givenmultiple
toCOVID-19 complications in recruiting targeted students, many of the 10 students participating inthe camp did not intend to follow the manufacturing employee career pathway, although all 10students passed the course with above-average grades. Funding for the camp, which includesscientific and safety demonstrations, is secured for the 2022 summer term, during which a moreengineering-focused cohort of students will be an enrollment goal.IntroductionRecent employment trends have shown an increase in the demand for employees for jobs in thescience and engineering fields in the United States [1]. This demand for employees in scienceand engineering has the potential to increase due to the increasing numbers of employees atretirement age in these
. © American Society for Engineering Education, 2022 Powered by www.slayte.com Work in Progress: Exploring Students’ Misconceptions of Cache MemoriesIntroductionCaches are small memories inside a computer’s processor that reduce the average time to accessmemory for a program. Caches store a small amount of recently accessed data inside theprocessor so that it can be accessed quickly by the processor. ACM Computer Science Curricula2013 [1] classifies the purpose and operation of caches as a core topic. Programmers need toknow how caches work and its implications for the order in which data should be accessed tomaximize computing performance. Prior research shows that cache
chain, product, andquotient rules), Thermodynamics (explaining entropy), Differential Equations, Control Systems,Digital Signal Processing, Newton’s Laws of Motion, and Computer Algorithms. In all of thesecases, students found this approach to be very effective for learning, and they highly praised theintuitive and engaging examples. 1. Introduction Most mathematics textbooks are loaded with mathematical formulas and explanationswith little focus on conceptual understanding. Textbooks focusing on differential equations areno different. This method is useful because it is written in a precise manner, but at the same timestudents may become frustrated with the material as they do not intuitively grab some of theconcepts and miss the
from traditional reviews and commentaries” [1].14 main types of reviews and their methodologies using the Search, Appraisal, Synthesis, andAnalysis (SALSA) framework have been analyzed [2]. The authors described each review,detailing their strengths and weaknesses, and the kind of activities that the researchers undertakewhen searching, appraising, synthesizing, and analyzing. In describing the analysis of systematicliterature review, they postulated that the aim of the researcher is to examine “what is known,what should be recommended for practice, what remains unknown, uncertainties aroundfindings, and recommendation for future research” [2]. Undertaking a systematic review involves6 steps. The steps include deciding to do a systematic
people perceive and learn new information, adaptation to the students'new styles of learning should be explored. This paper focuses on introducing a basic math concept, the inverse function, by linkingit to daily experience using relevant analogies. It includes several examples of visualizationsintended to aid comprehension of the concept prior to delving into purely mathematical formulasand proofs. The paper starts with a general visual explanation of the concept of an inversefunction, followed by visual, intuitive, and experience-based examples, including (1) non-mathematical examples, where the inverse exists, such as the idea of “negative” of a developedfilm, as well as where the inverse does not exist, such as getting a haircut; (2
idea is to introduce the topic in an intuitive and engaging manner beforetransitioning to conventional textbook material. Examples are drawn from (1) Art, usingexamples such as 2d perspective views and vanishing points in images, (2) Physics, using time-related examples such as the tendency towards equilibrium in nature, e.g., approachingtemperature and pressure equilibria, (3) Engineering and Technology, using energy relatedexamples such as charging the battery of a mobile phone, (4) Geometry, using images asobtained from two parallel mirrors, and fractals, and (5) Algebra and Calculus, using limit toexplain the Golden Ratio, and the concepts of derivative and integration. The paper concludeswith related math and engineering brain teasers
. Effectiveness analysis of the method used for designing such questionsis also important. Efforts have been made in understanding and improving engineering students’ability to respond calculus questions in (STEM) fields that require knowledge of more than onecalculus concept [1-11] and more research results are added every year to these results forunderstanding students’ approach to solve these problems. In this work, 26 undergraduateengineering students’ written and oral responses to a calculus question that involves multiplecalculus concepts are recorded after Institutional Review Board (IRB) approval. Triangulationmethod [1] and Action-Process-Object-Schema (APOS) theory [10] are used for analysis of thecollected data. The students are tested on
needs to be treated carefully in calculations. The data analyzed in this work wascollected from 24 STEM students at a mid-sized Northeastern university that either enrolled orcompleted the second 4-credit course in the United States during 2020 and 2021 years. Theparticipants completed a questionnaire and had gone through video recorded interviews to explaintheir written questionnaire responses by following an Institutional Review Board (IRB) processattained for the research. Action-Process-Object-Schema (APOS) theory is used for evaluation ofthe research question, along with the concept image and concept definition approach of Dreyfuset. al [1]. The written responses alone were not sufficient neither for APOS classification nor forconcept
or Analysis course at a large Midwest university during a particular semesterin the United States. Qualitative data is displayed by using sample interview responses of theparticipants. Quantitative and qualitative responses are incorporated into the Action-Process-Object-Schema (APOS) theory classification for the specific questions. Participants are asked toexplain their written questionnaire responses during the interviews. Concluding remarks withsuggestions to the mathematics educators are provided for designing exercise and assignmentquestions in the conclusion and future work section.1. IntroductionStudents’ pedagogical integral knowledge can be evaluated from different perspectives. Oneaspect of such an evaluation is by stating the
engineering course.For instructors and researchers, the answer to "how to make statics relevant to engineers?"appears elusive.This paper recommends specific strategies, with several examples, to increase engineeringrelevance. These strategies are simple to incorporate and designed to improve student learning.They form a five-step approach that aims to help students develop skills beyond basicalgorithmic problem-solving. These steps are: 1. Start with the purpose. 2. Foster qualitative reasoning. 3. Nurture quantitative problem-solving skills. 4. Create design and research experiences. 5. Integrate digital tools.These steps build on each other to help students develop and retain skills and solve ill-definedengineering problems. This
emphasize technical concepts, which reinforces tolearners that problem-solving efforts are solely technical undertakings that are devoid ofsocioeconomic, environmental, and political dimensions [1] [2]. This narrow emphasis fuelsasocial, apolitical, and apathetic attitudes in engineering, which is glaringly incompatible withthe real-world complexity of engineering activities amidst the increasingly multi-ethnic nature ofthe nation [3]. These deficiencies have informed leaders in engineering education to call foralternative instructional approaches to prepare engineering learners to undertake engineeringactivities with broadened awareness of (and motivation to resolve) societal inequities [2] [4] [5].Engineering educators can adopt ill-structured
disciplines. Students will work in interdisciplinary teams to (1)understand the physics and the computational theory relevant to quantum computing, (2) developcomputer code that simulates a quantum computer, (3) understand the relevance and importanceof existing quantum computing algorithms, and (4) appreciate the need for future research inquantum computing. Our approach is informed in part by our experience working with a pair ofundergraduate students this past summer on the development of a Python-based quantumcomputer simulator. This experience showed that building a simulator was an effective way toteach the theory underlying quantum computing. Building a simulator also provides an excellentfoundation upon which to explore student-accessible
[1].What unifies these challenges is that they are all data-driven and requires design thinking.Design thinking is imperative in solving 21st century engineering problems, regardless of thetype of engineer involved [2]–[4]. Thus, it is important that students are engaged in theengineering design process, in hopes that they will have a smooth transition from school to theworkforce[5]. The design process provides a framework for scoping problems consideringconstraints, brainstorming possible solutions, selecting among the best options, prototypingsolutions, iteratively testing, and effectively communicating outcomes, which will all be helpfulto undergraduate engineering students when entering the workforce [2], [6].While the design process is
discussed.IntroductionInstruction is a primary role for engineering librarians. Information literacy is considered such animportant part of the discipline itself that that the Accreditation Board for Engineering andTechnology (ABET), the official U.S. accreditor for post-secondary engineering and computerscience programs, has made it a part of their standards that all American engineering programsmust follow if they are to obtain and maintain their credentials: Criterion 3.7: [Students must have] an ability to acquire and apply new knowledge as needed, using appropriate learning strategies. [1]The most common way for engineers to acquire new knowledge is by conducting research.Although considered "soft skills", the ability to formulate a research question
-yearstudents was designed and implemented. This paper outlines the details of implementation ofsuch a virtual experience, the challenges encountered, and students' overall experience with thevirtual program. The virtual shadowing experiences consisted of virtual meetings between thefirst-year student and a near peer mentor. During the meeting, the mentor and first-year studentconversed about the company where the co-op student was working, major-specific coursework,career-related information, skills required by the profession, goal-setting strategies, and how toovercome challenges. The main questions investigated include: (1) what learning experienceswere provided to first-year students?; (2) to what extent were students satisfied with their
expectancy-value theoryEngineering PersistenceFor the last several decades, engineering educators have been striving to both understand andimprove the rate of student persistence in engineering. As it stands, only approximately 55% ofthe students who enroll in engineering programs persist to graduation [1], [2]. Research hasrevealed that persistence is based on a wide variety of predictors from pre-college mathpreparation [3] to engineering school climate [4], engineering identity [5] and more. Severalholistic models have been proposed over the years that have attempted to organize the factors[6]–[8], and these models have been instrumental in advancing our understanding of persistenceand attrition for different student groups.One key indicator of
thestudent may have made during their high school career and so benefit those who may have had agood performance relative to their context, which in turn is a product of the educationalestablishment they went to, and the type of education received. Each University that forms partof the Sistema Único de Admisión (SUA - Unified Admission System) in Chile, defines aweighting to each of their degrees, giving the ranking a relative weight within the total weightedvalue [1].Given the above, it becomes relevant to analyze the role of the ranking, as well as othervariables, as academic performance predictors for students in their first year of study. In order todo this, the constructs associated with Chilean university admission system and some
is an Assistant Professor in the Department of Engineering Education at the University at Buffalo, SUNY his lab focuses on engineering design, advancing research methods, and technology innovations to support learning in complex domains. Major research strands include: (1) analyzing how expertise develops in engineering design across the continuum from novice pre-college students to practicing engineers, (2) advancing engineering design research by integrating new theoretical or analytical frameworks (e.g., from data science or complexity science) and (3) conducting design-based research to develop scaffolding tools for supporting the learning of complex skills like design. He is the Program Chair for the Design in
forgetting to upload a document, and increased value when reviewing labslater.1 IntroductionMany universities utilize MATLAB in a number of laboratory classes, in different departments.These can include disciplines such as electrical and computer engineering [1], freshman yearexperience courses [2, 3], and mechanical engineering [4, 5, 6]. While the courses in questionmay focus on programming [7, 8, 9], many courses use MATLAB more as a means to an end inteaching other material. In particular, MATLAB is used in many dynamic systems courses in themechanical engineering curriculum, and has been for some time [10, 11, 12, 13, 14], includingboth modeling courses [15, 13] and controls courses [16, 12].While lab classes are universally regarded as
(Guerrero et al.,2016; Guo et al., 2019; Hayter, Nelson, et al., 2018; Klofsten et al., 2019; Liu, 2018).In a review of academic entrepreneurship ecosystems worldwide, Hayter and colleagues(2018) summarized the research on the predictors of activity in academicentrepreneurship into eight independent variable categories; (1) characteristics ofacademic entrepreneurs, (2) human capital, (3) social networks, (4) entrepreneurialenvironment, (5) financial resources, (6) scientific, technical, and productcharacteristics, (7) academic entrepreneurship programs, and (8) university managementand policies. The authors concluded that researchers should not rely on “the linear,patent-focused technology transfer context” (p. 1073) and instead conceptualize it as
?This question particularly helps with the determination of participants’ interest in usingcalculator noting that functions are commonly covered as a part of high school education ofSTEM majors. STEM students’ knowledge of various technologies to solve engineering andmathematics problems can be an important part of their learning practices. These students areobserved to face major obstacles as a part of pedagogical research when they solve calculusrelated problems by Tokgöz (2017, 2019-1, 2019-2, 2016-1, 2016-2, 2015-1, 2015-2) andTokgöz et. al (2021-2, 2021-3, 2020-2, 2018, 2018-1, 2018-2, 2017, 2015). Solving some of theSTEM problems by hand can be challenging and technology can be used to solve such problems(Tokgöz et. al (2021-1, 2020-1
program has demonstrably improved the academic performance andgenerally provided a positive social experience for the students. Lastly, the paper also provides abrief discussion on the findings of a survey of first-generation students at Texas A&M universitywith respect to the challenges they face in maneuvering their academic and social lives as auniversity student.IntroductionFirst-generation (FG) university students face a variety of challenges, including a lack of parentalguidance, financial and social burdens, isolation, a lack of sense of belonging, and low self-confidence, all of which put them at a higher risk of dropping out than their continuing-generation college student peers. Mobley et al. [1] categorized students according to
contribute to this growth. The US government hasestablish goals for offshore wind energy by 2030, and more than a dozen projects are alreadyinitiated or are in advanced levels of planning for the East Coast [1]. In the Commonwealth ofVirginia, the renewable energy industry started to grow rapidly, and the endeavors wereaccelerated by the adoption of the “Virginia Clean Economy Act” in April 2020 [2]. The regionis expected to be home of two large projects for a total of over 5,000 megawatts, and to becomethe leader of offshore wind development for the entire Mid-Atlantic [1]. Currently, 7% ofVirginia’s electricity is generated from renewable energy, and has set forth policy to generate100 percent of their electricity from clean energy sources [3, 4
throughacquisition of knowledge to how knowledge is accessed and incorporated into engineeringsolutions. This combined with a need for more engineers and the need for the demographics ofengineers to match that of society’s has led to over three decades of calls for change inengineering education to take a proactive response to the ever-increasing rate of societal change[1]. Now more than ever engineering educators need to explore and innovate with models andpedagogical approaches that will move engineering education systematically into positions tomeet these rapidly changing needs.While studies, concept position papers, and reviews of innovative approaches are not new in theliterature, this paper will explore one program’s journey in parallel to changes in
teams has been an important topic in business and education for many years[1], [2]. Intellectual conflict can prevent teams from making coherent and efficient decisions.However, directed constructively, it can also be a great source of creativity and innovation inengineering design [3], [4]. Constructive controversy is a method developed to systematicallyexpose intellectual conflict among team members and create a collaborative atmosphere fosteringdifferent perspectives.Constructive controversy is implemented in multiple ways. One of the most commonimplementations consists of dividing the team into two or multiple sub-teams. For a designproblem, each sub-team proposes a design solution and presents their design to the other sub-team.The second
lessons learned are being used to inform further changes in content and lab-to-lab knowledge recall.1 IntroductionThe act of taking a measurement is ubiquitous in engineering practice and in the collection ofexperimental data. With measurement comes uncertainty – the variability of the difference betweenthe measurement taken and its true value. The ability to analyze and make sense of large volumesof experimental data is critical to prepare engineering graduates for the modern workplace. Thetopics of error, precision, and uncertainty are frequently introduced in Physics and Chemistrycourses [1], [2], [3]. Many engineering programs include an Experimental Methods course in theircurriculum where students focus on calibration, data
learning outcomes andefficacy.1. IntroductionOnline education becomes more and more accessible thanks to the advancement in computingtechnologies such as networking. During the COVID-19 pandemic, many universities turn toonline conference tools such as ZOOM and WebEx. Online educational resources are usuallymore applicable for teaching theories or concepts, such as teaching theorems or solvingequations. However, instructors and students have a relatively negative experience with coursesinvolving labs. In most cases, instructors use live streams or recorded videos for labdemonstrations. However, live stream or recorded video does not allow the learner to participatein the experimental process, thus the content and concepts learned by the students