oflearning behaviors indicate that the POGIL approach resulted in significant gains (p<0.01) innearly all assessed areas over traditional lecture based coursework including: critical thinking,participation, interest, motivation, and reading. Students viewed provided model solutions, takehome problem sets, concept check activities (learning catalytics), lecture, in-class demos, andguided inquiries as significantly supportive of learning. Finally, students found the course andinstructional methods: (1) aided in seeing relevance of engineering to real-world needs, (2)increased their interest in own major, and (3) felt the material presented will be value followinggraduation.Introduction: Despite a general dissatisfaction with large format stand and
to work by themselves), wherestudents pick their team members at the beginning of the semester.For nearly all of the projects, students are expected to work outside of the scheduled lab time inorder to complete the objectives. Grading for the project consists of 60% based on meeting all of theengineering requirements, 30% based on the content of the lab report, and 10% based on spelling,grammar, and writing style. There is a 5% reduction for late lab report submittals. A listing of theprojects for the course is shown below. Lab 1: Software-defined Calculator Project (2 weeks) Lab 2: Thermocouple Project (2 weeks) Lab 3: Waveform Generator Project (2 weeks
and a series ofaccelerometers that measured the response of the floor and the roof. The linear shaker was placedin the middle of the roof to excite the structure at the natural frequency in the transversedirection. Figure 4 shows the FVT setup. The experimental natural frequency was found to be f =6.1 Hz (Table 1) and Figure 2 shows the corresponding mode shape in shaded grey. The studentsquickly concluded that the original computer model overestimated the system stiffness. Pastexperience with FVT analysis and computer modeling generally shows that computer modelstend to overestimate the system stiffness, so a difference in frequency between the computermodel and the experiment of around 30% was not alarming. However, as the students studied
courses, when context,application, and sometimes even notation can be quite different. This is often true forengineering students with respect to the Calculus sequence.In courses such as Calculus, concepts and solution methods are typically presented within amathematical context. While some students can recognize the underlying structure and themathematical construction, others have trouble identifying patterns or parallel thought structures,which makes it difficult for them to generalize the concept to a range of problem types. Forexample, students in an Introduction to Mathematical Statistics course were reported to claimthey do not know how to integrate a probability distribution over a region. The pre-requisite forthe course is Multivariable
concentrations later.FANUC America, as one of the largest companies producing automation products and systems,produces FANUC industrial robots, which are widely used in the fields. The main goal of theselabs is to prepare MET/MCET students to take robotics concentration courses at PurdueUniversity Northwest and to work in automation/robotics fields in the future.All the labs will be performed with FANUC LR Mate 200iD educational robot. It has six axes:base, shoulder, elbow, rotation of the arm, pitch of the wrist, and rotation of the hand plate. Thefirst lab will be a safety lab, which includes rules and guidelines that students need to follow orbe aware of during lab sessions. The educational robots are mainly designed for trainingpurposes. Also, to
but on the underlyingmathematics as well. Examples that were not practical to attempt by anyone but the brighteststudents are now within the reach of motivated and curious students.Student evaluations have shown an improvement since the introduction of the inductive approachalong with Maple and MapleSim. The positive response of students to the use of MapleSim as afront-end tool and Maple as a support tool has encouraged us to use it as the core of a newdistance education course in embedded systems architecture.IntroductionThe ELE 604 Sensors and Measurement class at Ryerson University is presented to Electricaland Computer Engineering students in the third (junior) year of the undergraduate program.1 Theobjective is to expose students to
down orally through classes and peergroups, while published strategies tend to be from faculty or administrative perspectives. Thework presented here codifies the successful and unsuccessful strategies that students acrossnumerous technical disciplines and from different backgrounds have used through theiracademic careers. The advice given is from a range of students at Wentworth Institute ofTechnology with a number of engineering and technical programs, gathered and analyzed bya team consisting of students, faculty, and administrators. The work serves as a guidebook forstudents, by students, in a range of rigorous programs. A survey was distributed to recent graduates and upper-level students from variousengineering and science backgrounds
thecourse made any significant difference in the student’s understanding of the underlyingMathematics and Engineering principles. Results from this study, generally encouraging, werepublished last year18.Having completed Pre-Algebra in 6th grade, algebra in 7th grade, Geometry on line during 8thgrade and Algebra 2 in class during 8th grade, one of the interview subjects was in an advancedlevel Pre-Calculus in 9th grade. He is at least two years ahead in math compared to the regular9th grade cohorts. The other subject was also at a somewhat accelerated math track and hascompleted Pre-Algebra in 6th, Algebra 1 in 7th, Geometry in 8th grade and was scheduled inAlgebra 2 in 9th grade while in the robotics course. The details of the interviews conducted
-level knowledge is then elevated to higher Cognitive Processes ofCreation via Technical Design within the weekly Laboratory sessions associated with theEML4142 course. These are followed by Communication and Collaboration skills with the Lab,each of which is assessed via reports submitted which are graded manually, and also engagediscussions which encourage Metacognition of their learning.Instructor and GTA resources to conduct these relaxing and rewarding learning activities is madeavailable due to the abridged grading burdens of Homework (via McGraw-Hill Connect), CBAQuizzes (via Canvas in the EPC), and CBA Exams (via Canvas in the EPC). The questionformats developed for Quizzes and Exams as described below.4.3 Singular Selection Assessment
have access to literature beforehand and receive a lecture prior to the flight perform better than thosethat only review the literature or only receive a lecture before the simulation. Also, the efficacy of the hands-on learning in a laboratory environment is discussed.Keywords: Flight Training, Simulation, Hands-on Learning, Laboratory learning, Retention 1. IntroductionIn this IRB-approved (Institutional Review Board) study, student learning and retention is assessedusing a motion-based fixed-wing flight simulator. Students are given introduction to the principlesof flight. Then they fly the aircraft flight simulator and are asked to complete a pre-defined mission.Points are given for successfully completing several legs of the mission
engineering or science. Is this a validassumption and does it apply to aerospace engineering students?Literature Review In engineering education, the number of studies exploring professional persistence islimited. Studies by Amelink and Creamer (2010), Eris et al. (2010), and Lichtenstein et al.(2009) indicate that a number of factors impact professional persistence. Between these differentstudies it was found that1,6,7: 1. respect from both peers and instructors was very important in keeping students satisfied with the engineering field and intending to work in the field for years in the future1. 2. the desire to pursue an engineering career increases throughout the education of the student in the case of persisters (those
, wedevelop the mathematical models of the payoff for both the firm and the university.We further give a detailed analysis on the selection of collaborator on innovationproject from the perspectives of both sides, in both non cooperative game and co-operative game settings. Under that assumption that (1) at one time a firm canonly link to one university and a university can only link to one firm, (2) a firmor a university will not enter a collaboration relation if there is limited payoffs tothemselves, the following results are obtained: (1) In general a firm will choose to collaborate with larger universities with better reputations and more relevance to their innovation project. (2) A university’s relevance to a firm’s innovation
of concepts was measured. Additionally, the effect of word familiarity and the number of definitions of word were investigated for their effect on the quantity of concepts generated. It was found that the Analogy Seeded Mind-Map method allowed students to generate a large number of concepts in a relatively short amount of time with only brief introduction and explanation of the method.1. Introduction and Motivation Innovation is often a primary goal during the engineering design process. Various concept generation techniques exist to help designers develop innovative solutions. Techniques such as Brainstorming, 6-3-5/C-sketch and TRIZ8, are widely used in the engineering classroom environment. Brainstorming and 6-3-5/C-sketch require the
materials-led approach and get feed-back from the wider materialscommunity.Introduction Most forms of engineering make use of materials in some way. After all, everything aroundus is made of materials. Even digital and virtual based professions and applications rely onspecialized high-performing materials to transfer and store data [1]. Energy related industriesand electrical engineers are also reliant on advanced materials for generation (magnets) andstorage (electrodes). The world Bank Report 2020 highlighted 17 mineral resources that areessential to a clean energy transition towards renewables [2]. We know that materialproduction and related activities contribute more than 20% to the global greenhouse gasemissions [3].In this paper, we want to
Paper ID #14669Introducing Physics Concepts with Illustrative StoriesProf. Yumin Zhang, Southeast Missouri State University Yumin Zhang is an associate professor in the Department of Physics and Engineering Physics, Southeast Missouri State University. His academic career started in China; in 1989 he obtained master’s degree on Physics from Zhejiang University and then was employed as technical staff in the Institute of Semi- conductors, Chinese Academy of Sciences. After receiving PhD degree on Electrical Engineering from University of Minnesota in 2000, he started to work as a faculty member in University of Wisconsin
to advances intechnology including in infrastructure, storage, and analytical tools and techniques[1][2][3]. Thedemand for the data science field can be seen in various industries including retail, health care,finance and in all areas of economy and society [3]. Data science careers are the top careers inthe U.S. across many disciplines[3]. It is predicted that this demand will continue to increase inthe near future [2][3]. The rise in demand for data science technology has created a demand forgraduates who have the skill set needed to support the data science field [1][2][3]. To meet thisdemand in the data science industry, many colleges are revising current programs or developingnew programs geared for the data science industry[1][2]. A
the protocol of the sensor array instrumentation.As a team, they helped their professor successfully continue the hunt to answer the query ofwhether a Mach Effect (inertial reaction force) is actually detectable. Due partly to their effortsthe science around this instrument is now quite robust and this novel device provides consistent,replicable and predictable results. During the summer research, the students got to apply much oftheir theoretical electrical engineering training to a real-world application in sensor arrays andinstrumentation.Background and MotivationThe roles that our undergraduate engineers played in this research during the summer of 2018was written up in a recent (2019) ASEE Zone 1 conference publication. [1] That paper
is measured by an angularpotentiometer sensor. The sensor signal is measured by a DAQ interface to a Matlab/SimulinkRTWT PID controller. The controller generates an actuator signal via the DAQ analog outputchannel which is connected to the motor amplifier to close the loop. Potentiometer Position Sensor Motor Amplifi- er DC Motor Page 26.798.3 Figure 1. Photograph of DC servo control apparatus
strategy being usedis to ask the students at the beginning of class a general question or two about the day’s topic,which can inform the instructor about prior knowledge of a topic or even familiarity with realworld examples. As such, the use of a new or recent development in materials science, “CuttingEdge Technology,” is being added to each class to improve the relevance of the course, as well asshowing students the types of new materials, processes, or characterization that they might beusing after graduation. Such topics have included atomic force microscopy (1 out of 40 familiar),scanning tunneling microscopy (1 out of 40 familiar), and additive manufacturing (0 out 40familiar). Students have shown moderate to strong interest in discovering
, combustion testing, flares, process heaters, processburners, flare gas recovery, metallurgy, and equipment fabrication. Some instructors taughtmultiple topics. Each topic was covered in one or two 75-minute face-to-face sessions taughttwice a week at the local university. Two of the sessions, combustion testing and equipmentfabrication, were held at the industrial company where students were given a lecture and then atour of world-class combustion testing and manufacturing facilities (see Figure 1), respectively.All lectures including the tours were video-taped and uploaded to a server at the remoteuniversity for their students to watch at their convenience. (a) (b)Figure 1
Difference Between Nanowire and Macroscale Metal. When responding to the promptconcerning the physical difference between the nanowire and the macroscale metal that wouldaccount for differences in the yield strength, students made reference to one or two main ideas:(1) the crystal microstructure (single or polycrystalline) and (2) the presence or absence ofdislocations. Ten students made reference to the nanowire being a single crystal; two of thesestudents left their explanation at this. Six students went on to refer to the macroscale metalhaving a polycrystalline structure; one of these students left their explanation there. One studentmade a general reference to the crystalline structure being at the root of the difference but did notindicate how
. Such direct and active peer-to-peer learningaffords iPodia students a unique opportunity to co-construct contextual knowledge of importantsocio-technical engineering subjects. As a result, iPodia enhances students' ability and skills toexplore cultural diversity as an inspiration for global engineering innovation, whilesimultaneously enlarging their personal networks to become future global engineering leaders.2. What is iPodia?2.1 Pedagogical approachThe iPodia pedagogy is developed based on three basic hypotheses, as illustrated in Figure 1,that (1) contextual understanding is best achieved via direct engagements, hence the "inverted"learning; (2) what you learn depends on with whom you learn, hence the "interactive" learning;and (3
that uses discourse to support knowledge acquisition throughmodeling cycles 10, 12. Modeling physics instructors are trained and prepared with an agenda for studentprogress in order to guide student inquiry and discussion towards understanding through “Socratic”questioning and remarks. During the modeling cycle, technical terms and representational tools areintroduced by the instructor as needed to refine models, facilitate activities that use and develop themodels, and to improve quality of student discourse 12. By structuring the “talk” that students do, theinstructor ensures that students are working towards learning objectives through conversations they havewith each other. The instructor is taught to listen to the language and use of
when the class clearly needed additional work on a lesson topic.Table 2 shows the number of units and number of lessons for each of the three courses. A web-based, online, multi-media content system provide by the textbook publisher was used to assignlessons and homework. The system provides algorithmically generated questions with the abilityto score those questions automatically even when answers are mathematical expressions.Students were assigned a score for each course component and a weighted sum determined theirclass weighted average, which in-turn was the basis for the course grade. These weights areshown in Table 1. The 10% weight for class activities (CA) is due the instructor’s concern thatstudents might easily dismiss the importance
2 Strongly Disagree 1 Writing Skills Programming SkillsFigure 3. Students’ perceptions of their future use of writing and programming skills in theirfuture classes, career, or non-professional areas of life (such as hobbies). Error bars indicate 95%confidence intervals.The final part of the survey investigated the perceived importance of programming skills.Specifically, students responded to the following question: “How do you think it benefits anengineer to develop computer code or programming well?” Many discussed the ubiquitousnessof computers and programming for technical engineering careers (dubbed the “technologicalera” by
the computationalmodules, lectures, and their integration within the broader MatSE curriculum.1. Introduction and BackgroundThe rise of materials modeling has generated a nationally recognized need for materials scientistsand engineers with computational training 18;23;24 . In industry and academic settings alike,computational materials science skills are in high demand as researchers seek to acceleratematerials design with computational tools 24 . Yet, a 2009 survey revealed that, on average,employers desire for 50% of new hires to have computational training, while only 37% of recentgraduates actually have such training 24 . These trends mandate that materials science andengineering departments around the country must better serve their
2012. Between 2012 and 2014, she held a postdoctoral appointment in the Department of Mechanical Engineering at Johns Hopkins University. Her current research explores the interplay between phase or morphological evolution and material functionality in structural materials under extreme conditions. She also maintains interest in engineering education, specifically in outreach and design thinking.Prof. Robert Maass, University of Illinois, Urbana-Champaign Robert Maass received a triple diploma in Materials Science and Engineering from the Institut National Polytechnique de Lorraine (INPL-EEIGM, France), Lule˚a Technical University (Sweden) and Saarland University (Germany) in 2005. In 2009, he obtained his PhD from
classesare described along with examples, lessons learned, student performance data and the impact onthe students and program.Introduction 3D printing (3DP), also known as additive manufacturing, is an important manufacturingmethod that has become more accessible for academic lab facilities in the last ~five to sevenyears [1]-[5]. Traditional manufacturing techniques, such as injection molding and forging,involve fixed molds or dies that are expensive and present limitations to the 3D shapes that canbe fabricated. In 3DP, no molds or dies are required. Parts are designed using a computer-aideddesign (CAD) program and then the digital part file is loaded into a slicer program that preparesthe part file for printing on a 3D printer. From idea
selected through a rigorous application and interview process in order toensure student’s qualification and motivation. The class size was limited to be 16 students ineach participating university in the interest of quality control, except the American and Indianclass where 32 students were recruited. Because of wide time differences on multiple locations,the class was divided into two parallel sessions: Session A and Session B. The Session Aenrolled 16 American, 16 Israelis, and 32 Indian students, and the Session B enrolled 16American, 16 Chinese, and 16 Korean students. The 112 course participants were assembled into16 project teams, each with 7 members (i.e., 2 American, 2 Indian, 1 Israelis, 1 Chinese, and 1Korean students). The membership of
increased the level of distraction aswell. Even if computers were brought to class with the purpose of taking notes, or access classmaterial, too many students were using theirs for activities not related to the lecture (e.g. surfingthe web, checking emails, instant messaging, etc.). We knew we were not alone, as many of ourcolleagues were facing the same issues, but this was of little avail. [1,2]What we didIn 2013 we received a grant from our institution to “flip the classroom” and we decided to use itfor our 4 credit course in Ordinary Differential Equations. The main reasons were 1) both of ushad been teaching the course for several semesters, and 2) the natural structure of the lecture: model of differential equation à