, students can quickly forget them because we usually do not use them daily inour busy life.However, if the professor or instructor adds some comments that for any soil/rock larger than thelength of his or her foot or 12 inches, the soil/rock is defined as boulder, the students will have alonger and maybe even a lifelong memory of boulder size in the USCS soil classification.Next size boundary is 3 inches length, about one of a person’s figure length, also equal to 1 foot(12 inches) divided by number four (#4 sieve). So any rock/soil larger than 3 inches, a finger’slength, but smaller than 12 inches or one foot is considered as cobbles.The number 4 sieve, roughly 5 mm (4.75mm) is approximately the width of a person’s pinkyfinger nail width.So if the
so they can fully appreciate their capabilities as well as their limitations. Studentassessment has shown that our approach greatly enhances understanding of helical antennasystems and has caused significant increase in student enthusiasm for selected topics in antennas.Introduction The helical antenna was invented by Dr. John D. Kraus in the 1940s [1]. The uniquedesign has given this type of antenna several advantages over other directional antennas. Theseadvantages include universal polarization, relatively high gain, broad band capability -withrespect to both directionality and SWR- greater immunity to multipath interference, as well ashaving a relatively simple structure and feed system. Helical antennas are widely used in
, studieshave shown that it has some significant educational disadvantages. Over the past severaldecades, physics education research has shown that students were not learning the conceptsand/or were not engaged by the methods used in “traditional” physics education.1-4 Those andother studies have motivated a significant amount of research on physics education and muchprogress has been made. A significant body of physics education research has focused ondeveloping and incorporating classroom techniques that reduce or eliminate lecture and replace itwith active learning methods.5,6 Often the focus of the active learning strategies has been tomove away from methods that lead to students memorizing facts and mimicking solutions andtoward developing
second semester. These metrics will be used to compare the effectiveness of the MLMs on both an individual and department wide basis.KeywordsMultimediaLearningModules,Prelectures,PhysicsEducation,PhysicsPedagogy.IntroductoryPhysics,EngineeringPhysics.1. Introduction Over the last twenty years, numerous advances in physics education research(PER) as well as the development of non-traditional learning tools have changed thelandscape of STEM education [1]. A short time ago, the most widely accepted approachto physics education was the traditional lecture, supported by a hands-on laboratorycomponent, with typical homework exercises. Demonstrations and group work added anactive component to these techniques, but the overall learning
Paper ID #20358Analysis of reasoning paths of engineering studentsProf. Genaro Zavala, Tecnologico de Monterrey, Monterrey, Mexico & Universidad Andres Bello, Santiago,Chile Genaro Zavala is Full Professor of Physics and Director of Educational Innovation in the School of Engi- neering and Sciences at Tecnologico de Monterrey. Also, he is currently collaborating with the School of Engineering of the University Andres Bello at Santiago, Chile. Professor Zavala is National Researcher Level 1 of the National System of Researchers of Mexico and leads the Physics Education Research and Innovation Group. He works with the
. The laboratory exam measured student ability to work with an air track or with electric field equipment, both commonly used in undergraduate physics education. Results illustrate that large percentages of students majoring in technology, and in the health sciences, need to improve their basic math skills and their ability to use laboratory equipment to meet the expected learning outcomes. 1) Introduction This paper presents assessment results on how well three groups of STEM students learned aparticular set of outcomes expected across physics courses. The assessment was conducted atthe end of the fall semester of 2014 at the Queensborough Community College (QCC); QCC ispart of the City University of New York (CUNY). In the fall
result demonstrated that there was a strongcorrelation between the scores of the two sections on conceptual questions and problem solving.IntroductionEngineering Thermodynamics is a very challenging course to many students, since this courserequires a new approach in solving problems. Beginning from their first physics course, studentsare used to solving problems with equations. However, most of the working substances inengineering applications are not ideal gases, and they cannot be described by equations. Instead,students have to rely on the data tables to find the solutions [1]. Furthermore, this courseintroduces many new concepts, which cannot be well understood without reflecting on themwhen working on the exercise problems. Unfortunately
of the kinetic theory of gasesConsider an ideal gas consisting of a large number N of identical particles, each of mass m,inside a container of volume V. The number of particles per unit volume is then N/V. Theparticles collide elastically with each other and the walls of the container. Pressure isexplained by the kinetic theory of gas as arising from the force exerted by the particlesimpacting on the walls of the container. According to Newton’s laws the time rate of changeof the momentum of a colliding particle is equal to that force dp d (mv ) F= = . (1
community around Stanford University’s d.School (Carleton and Leifer, 2009) and DesignFactory Global Network initiated and coordinated by Aalto University (Oinonen, 2012)Before joining the course, the students were asked about their experience in similar projectcourses, project work, and international collaboration, and over half of them (n=26) had workedin an international team several times, and only a small fraction (n=8) had no internationalteamwork experience.Their average self-reported time of using a computer was 6,5 hours per day, and most of themidentified being active in social media (28 yes, 17 sometimes, 1 no). The students also had afairly positive approach towards computer-based learning tools, averaging to 8.4 out of 10 inLikert
since 1994 VHDL Based Digital Design and taught up to 2001, till Dr. Terence Kelly (received his doctorate under supervision of Pro- fessor Prasad) took over. From spring 1998, Professor Prasad also developed and taught 16.517, MMIC Design and Fabrication course to meet the growing demand of regional semiconductor industries. He is the recipient of Zone I best paper award by American Society of Engineering Education (ASEE) in 2008. He has been appointed as honorable member of IAAB of the MEGHE group of Institution and Shree Baba Ramdeo College of Engineering and Management (Nagpur) in India. He has also received the Best Teaching award for the New England Region, and the Best Campus award for the Zone 1 from ASEE dur
objects,theories of flight and physics of energy. The instructions are followed with hands on activities orfield trips that can enhance the student experiences.Every year, while this institute helps to recruit 1-2 students from the pool of 25-30 participantsfor the engineering and science programs, it also helps to support the few undergraduate studentsas mentoring counselors in summer as a form of retention. The impact of instructing engineeringphysics at early stages on performance in the college is strong and could be systematized withexpanding such instruction to include additional engineering physics.IntroductionDevising techniques to recruit, retain, educate and graduate students in less established or lessknown disciplines that demand
the assignment was graded for theformat). They were not told which assignments were going to be graded so they assumed eachassignment was likely to be graded. Students were expected to improve on using the structured layout by getting feedbackfrom the instructor on homework and exams. Assessment of this method was done in threedifferent ways: 1. Handwritten homework 2. Exams 3. Feedback from students Feedback from students was collected three times: 3.1. Anonymous feedback collection on Moodle in the first weeks of the semester. 3.2. Small group instructional diagnosis (SGID) in the middle of the semester. 3.3. Final anonymous feedback collection
700 students (394 females and 306 males) enrolled in thealgebra-based sequence and 395 students (109 females and 286 males) enrolled in the calculus-based sequence. We have previously reported on results from analyzing the performance of ourstudents relative to a national sample provided by the authors of DIRECT.26,27 In this work, wefurther analyze the statistics of the test by investigating to what extent the students’ level ofpreparation (algebra-based versus calculus-based) and gender (make versus female) affected theperformance of students in the second-semester laboratory.In Figure 1 on the following page, we show how Detroit Mercy students compare with thosefrom the national sample provided by the authors of reference 25. The graph
of metrology are now incorporated into EP Lab: (1) uncertainty in measurements(and its propagation); (2) use of metrology’s documented standard vocabulary and acceptedpractices; (3) using design of experiments (DOE) to analyze a process; (4) calibration of ameasurement instrument or process.The related learning objectives for EP Lab students are as follows (from the course syllabus): Student Learning Objectives 1. Learn and correctly use the professional vocabulary of metrology and measurement science associated with uncertainty & measurements; 2. Follow international standards in representation of uncertainty; 3. Assign uncertainty to a measurement by use of an uncertainty budget. This will
technological height, we are observing another wave oftechnological advances based on what some call the Second Quantum Revolution. [1] Thesequantum technologies are often referred to as a class of technologies that directly create,manipulate, and make use of the quantum properties of matter at the level of individual photons,atoms, electron spins, and exploit collective and entanglement quantum properties of matter.For the last 30 years or so, the research in these areas have moved from theoretical explorationsto experimental confirmations to realizations of prototype applications in diverse areas oftechnologies. These include, but are not limited to, quantum cryptography, quantum sensing andimaging, high-capacity communication, quantum computing
allowed more of a focus on the coding ofthe microprocessor. The majority of students have no coding experience prior to this course.Students do not seem to have an issue with coding the Arduinos. There were a few instanceswhere the Arduino proved to not be the best platform. Student example work and attitudes will bepresented. The effect on student assessment performance will be discussed.1 IntroductionThe ability to read, create, and interpret computer code is an important skill for an engineer or aphysics major to have. Many programs, including the program at the University of CentralArkansas (UCA), require students to complete one or more computer science courses for theirdegree. While these courses provide a solid introduction to
students rank-orderedapproximately 6 – 10 items that were important to them as they prepared to study for any course.Students were also asked to describe how they used each learning tool they identified. For thepurposes of this paper, Tables I and II illustrate the students’ number one item on their rank-ordered lists. Table I. Physics 100 (n = 43) Most Essential Learning Tool Needed to Study Number 1 Learning Tool Number of Responses % of Responses textbook, class notes 12 27.9 comfortable work place/quiet 11 25.6 computer 6
computational research and the research infrastructure atUCO. The process to acquire and deploy the Buddy Cluster will be detailed including the processto solicit proposals from and choose a cluster vendor. The process to get users working in thecluster environment - including internet browser-based access and use of certain cluster softwareand the use of more standard command line access and use - are discussed.IntroductionThere is a recognition of the national need for developing high performance computing (HPC)resources, which include human resources that will use this technology. In March 2007 theNational Science Foundation (NSF) released a report entitled “Cyberinfrastructure Vision for21st Century Discovery”1 that addresses how high
. For example, Richard Feynman was famousfor persuading scientists and mathematicians to explain complex ideas using only simpleterminology. This can also be extended to the connection between theory and experiment. RobertMillikan stated “The fact that science walks forward on two feet, namely theory andexperiment…….”[1] Although the understanding of physics may start from theoretical conceptsexplained in the lecture room, those ideas when complemented with laboratory experiments revealthe simple truths via observations and analysis. Physics is experiential.Indeed, experiments in the Advanced Physics Lab (APL) are different from those in theintroductory physics lab (IPL). APL experiments should be carefully designed to go beyond thesimple
, andhow we are able to locate an object using an antenna. Due to the complexity of designing atracking circuit, each group was given the circuit diagram of Figure 1. Then they were asked tobuild the circuit on the breadboard using standard laboratory components. It needs to be notedthat each team built their own inductor (coil) using magnet wire. As a result, each tracking circuitwas working at a different frequency. Antenna R2 C2 L1 U1 2.2kΩ 33pF