to comments are usually addressed at the beginning of the nextclass session. This paper investigates if a MP process can be effective in an online course setting.It also investigates and shares best practices that would be needed for a successfulimplementation.The modified Muddy Points methodology includes four steps: 1) collection of student reflectionsof unclear concepts; 2) assessment of student reflections in order to identify misconceptions thatcan keep students from achieving learning outcomes; 3) generating formative feedback, and 4)selecting and using delivery tool that quickly provides formative feedback to students. Thisprocess was implemented and studied in two fully online Materials Science courses. In onecourse, all of the steps
‘programminglanguage’ (and thus notional machine) the participants are being asked to learn. The study alsouses variations where the participants use cards which can be sequenced to plan the mouse’sprogram. In some exercise a predefined route is provide which participants must predict the endlocation of the mouse.Observed Misconceptions and ‘Failures’ Participant failures can be categorized into three general causes, misconceptions aboutthe domain, the language, or the participant’s logic breaking down due to cognitive load, asbroken down in Table 1. The most common mistakes related the domain related to spatialreasoning. Students often fail to compensate for prior moves by the mouse, insteadprogramming turns from the original orientation thus a ‘left
capabilities of the constituent parts.The mission engineering competency model establishes the proficiencies for practitioners toperform effective mission engineering based on interviews and open source literature. We alsodetail the relationships between mission engineering, systems engineering, and system ofsystems engineering.What is Mission Engineering?There is no single definition of mission engineering, also referred to in the published literature ascapability engineering. For example, the US Department of Defense (DoD) defines missionengineering as “the deliberate planning, analyzing, organizing, and integrating of current andemerging operational and system capabilities to achieve desired war fighting mission effects”[1]. A more general
- search focuses on mixed reality, head mounted displays and the impact on design. c American Society for Engineering Education, 2018 Mixed Reality and Automated MachineryWhen reality is augmented with digital information, it is called Mixed Reality (MR) [1][2].Machinery on factory floors is increasingly automated and system integrated [3]. The potential tosupport the design, sale and operation of automated machinery with a mixed reality device istremendous. This paper shares takeaways from a collaboration of industrial design students,mechanical engineers, field technicians and technical writers to investigate the innovationpotential of applying mixed reality in a manufacturing
Verilog HDLmodule development for FPGA synthesis. A floating-point binary numbering system is developedwith which all arithmetic operation modules are designed. Benefits of hardware implementedneural networks include the parallelization of computational processes that are not provided insoftware implementations of such networks. All modules included in the network are simulatedusing Altera’s ModelSim platform and synthesized on Altera’s DE2-115 Development Board.IntroductionNeural networks are a type of machine learning algorithm that were created with the intention tomimic the biological function of neurons in the brain [1]. In this biological sense, the primarypurpose of neurons is to process and communicate information with each neuron
speaking.IntroductionFreshmen arrive on campus ready to become a part of the excitement of biomedical engineering.They are eager to work in a lab, “tinker” in the design studio, and “learn by doing” rather thanjust sit in a lecture hall. The required fall semester freshmen course, “Modeling and Design”,focuses that freshmen enthusiasm into solving complex modeling and design problems throughfive modules [1]. The freshmen class of 115-140 students is divided into five-person teams.Students are challenged to develop, simulate, and test three physiological models; the arm, thecardiovascular system, and human efficiency. Students gain exposure to the design processthrough a foam core challenge. For their final project, students perform their own research,choose a project
. From these analyses, twovariables emerged as highly predictive of student performance: scores on peer evaluations andhomework submission timeliness. This relationship remains strong even when the measure ofstudent performance is adjusted so that student peer evaluations and late penalties on homeworkassignments do not directly factor into their adjusted overall score. We discuss potentialexplanations for and practical implications of this result.BackgroundBeginning in Spring 2013 we implemented a new freshman-level chemical engineeringlaboratory course [1, 2]. In this course, students work on open-ended product and process designprojects in teams of three to four. We use many different presentation techniques in order to caterto different
students access live solar energy data from theirlaptop or smart phone. A preliminary evaluation of the educational impact shows that studentsnot only gained an appreciation for solar energy, but they had confidence in their ability todevelop innovative ideas for improving solar panel performance.Energy TransformationEngineers should have technical expertise, but also the ability to work with new and “uncertain”information, collaborate, and solve open-ended problems [1]. In order to make it a reality, aninstitutional transformation of university teaching is essential [2] – [5]. That is the motivationbehind an energy transformation project underway in an undergraduate Engineering Technologyprogram. The goal is a new energy systems curriculum that
introduced anundergraduate research project to augment the Internship experience with relative success [1],[2] and engineering technology has introduced options for its seniors to work in internally fundedprojects as well [3]. This paper will compare and contrast these two techniques of providingstudents with Capstone project experience to highlight the pros and cons of each. With a mix ofboth industry experience and faculty guided work, the aim is to provide an optimal approach thatbenefits students, industry partners, and faculty involved in this very important element to four-year educational degree program.IntroductionThe University of New Hampshire at Manchester (UNH-M) offers degrees in both computingand engineering technologies in the Applied
andthe environment [1]. Reaching students in the middle school years or earlier is criticallyimportant because they are forming interests that will affect course selection in high school andin the long term may affect career choices [2-4]. To increase female enrollment in engineering,we need to promote engineering as a profession that contributes to the welfare of society [5].Others are showing that participation in robotics can be broadened through classes orcompetitions that are organized around a “Make Life Better” theme [6,7].Based on these findings, we developed an educational program at Michigan TechnologicalUniversity to promote engineering in pre-college STEM education. The program utilized two in-house affordable robotic platforms that
as other counties have been able to compete with the US with respect toconsumer-product development and manufacturing. This can be seen in countries that canproduce the same products at higher quality and lower cost. In order for the U.S. to remaincompetitive at a global scale, it is necessary to change how engineering education is organizedwith respect to the knowledge and skills in manufacturing technology and efficiency.Rural communities have borne the brunt of this with the US lagging in manufacturingcompetitiveness. Our model, we believe, will be of benefit to rural communities. Our program,“Making as Micro-Manufacture (M3)” proposes the following: 1) Give students the knowledge and familiarity to integrate electronic tools with
language typically involves acquisition of new vocabulary,punctuation, and grammatical structures to communicate with a computer. In other words,learning a programming language is like learning a human language. A recent study showed thatprogrammers use language regions of the brain when understanding source code and found littleactivation in other regions of the brain devoted to mathematical thinking. Even thoughprogramming code involved mathematical operations, conditionals, and loop iterations,researchers found that programming had less in common with mathematics and more in commonwith human language [1].In our study, we applied the well-developed cognitive framework used in second languageacquisition (SLA), into a Blended Learning (aBLe
of reliable and validinstruments provides the foundation for potential curriculum changes in the design and teachingof capstone courses to improve motivational growth and better prepare students for careers.IntroductionFor decades, the engineering profession has expressed concerns that US universities are notpreparing engineering graduates adequately to keep the nation competitive [1]. A recent nationalworkshop [2] of engineering employers identified 36 basic knowledge, skills, and abilities(KSAs) that are important in engineers entering the workforce. Among these, 15 KSAs wereidentified as most important and yet under-developed in graduates—one of which is self-driveand motivation. A subsequent workshop [3] of engineering students revealed
clients from diverse backgrounds [1]. Universities have respondedto the demands of industry to prepare engineers to work in groups and team problem solving thatrely upon metacognition and greater self-awareness [2]. In alignment with industry expectations for professionalism, ABET established standards thatspeak to critical thinking, communication, and demonstrate other professional skills. To achievethe ABET standards, some engineering schools require courses that arise from liberal artstraditions and thus, address issues of ethics, professionalization and the broader societal context.Those courses often provide a gateway for a student’s collegiate experience and affect everyincoming student’s sense of belongings in engineering. As many
, including backward design(Wiggins & McTighe, 2005), Webb’s depth of knowledge (2007), and Principles of Learning(Resnick, 1999). These frameworks represent some of the material faculty interact with duringthe Intensive and, as such, guide what aspects of the classroom observers attend to when usingthe ELCOT. The categories observers code include student organization, student talk, studentactivity, and instructor activity, each of which includes subcodes (see Appendix A). The studentactivity codes are grouped into levels according to Webb’s depth of knowledge (2007; seeAppendix C for operational definitions). Level 1 tasks require low cognitive engagement, withtasks such as taking notes, following procedures, or recalling information. Level 2
courses,particularly for underrepresented groups in STEM [1]. Despite evidence of effectiveness, STEMinstructors can be hesitant to adopt research-supported practices for student-centered learning.Hence, identifying effective methods to bridge the gap between STEM education research resultsand classroom practice is a topic of significant interest. While a variety of workshops and similarone-time interventions have been developed to help STEM instructors adopt research-basedteaching practices, research in professional development suggests that ongoing teachingdevelopment is much more effective than one-time efforts [2], [3], [4].Building on the research results of the K-12 education community, we created a network offaculty learning communities [5
Developing Teaming Robots for Engineering Design Education Using Cross PlatformsAbstractThe paper presents an engineering design approach to develop an instructional module forcollege students to learn Microprocessors and Robotics using multiple sensors, microprocessorsand software design tools. The module consists of research analysis, lesson content developmentand laboratory practice selection, which satisfies the ABET (Accreditation Board forEngineering & Technology) requirement for engineering education. The research analysis coversthe work reported by the scholars from MIT and other universities [1] [2], where the mainconcern is how to enhance students’ capability in developing engineering products using
influences the research team structure and progress; and Interactions on this multidisciplinary team have challenged them to overcome differences in knowledge background and skills to successfully address a common research goal.Project Description/Objectives:The ultimate outcome of the project is to develop a deep learning (DL) algorithm to automate theprocess of filtering and classifying images of damaged civil infrastructure collected after anearthquake event. The training images are gathered from existing databases of previous events,inspections conducted by professional engineers, or various formal/social media platforms(specific sources include NISEE PEER library [1]; EERI Learning from EarthquakesReconnaissance Archive [2
Education, Professional Development, and OutreachAbstractAn undergraduate Nanotechnology Fellows Program was established to addresses key problemsin implementing nanotechnology education: (1) science and engineering curricula are alreadyfull; (2) practical, hands-on experiences require extensive training on complex, expensiveequipment; and (3) necessary fundamental concepts and knowledge span multiple disciplines andare rarely taught at the undergraduate level. This work reports on the program evolution over thecourse of three years as well as the short- and long-term impacts on students’ academic andprofessional careers. The evaluation results from the first year indicated the most profoundimpact came from integrating the interdisciplinary
underrepresented and underemployed in the science,technology, engineering, and mathematics (STEM) workforce by a factor of <3.3. The combinedworking population of NHs, Pacific Islanders, and ‘Other Race’ (grouped by U.S. Census due tosmall sample size) represents 4.6% of the total U.S. workforce but only 1.4% of STEMoccupations [1]. This makes NHs and Pacific Islanders the most underrepresented ethnic groupin the nation in STEM employment (factor of 3.3), more so than Hispanic (2.3), AfricanAmerican (1.7), and American Indian and Alaskan Native (1.5) groups [1]. These statistics are ofconcern, especially in light of the U.S. Department of Commerce 2017 report that employment inSTEM occupations grew much faster than employment in non-STEM occupations
Model Canvas. The Design Canvas classifiesrepresentations by actionable questions on two axes—system development and design choices—which in turn are organized hierarchically by scale. Results of the project and examples ofrepresentations for the current iteration of the Design Canvas are presented along with theDesign Canvas development process.Product, Process and Representations in Capstone Design?Looking at the range of capstone design courses show that there is a wide variation ofapproaches in balancing the importance of design product vs. design process. A 2015 survey [1]indicates that while about three times as many courses emphasize process as do product, themajority of courses seek to balance the result of the capstone experience
communication [1]. Theconcept generation phase is the time to bring problem understanding, social factors andpractical knowledge together to develop possible solutions [2]. The quality and quantity ofconcepts generated in this phase affect and even determine the outcomes of the final designsolution [3], [4]. Prior research demonstrates that numerous concept generation techniquescan be used to facilitate engineers to increase creativity and generate more design alternatives[3], [5], [6]. Therefore, understanding concept generation and its techniques has importancefor engineering education and the design industry.There are two broad categories to classify concept generation techniques: unstructured designmethods and structured design methods [7], [8], [9
to the underside of the platform,connected to one or two motor neurons and then the motor neurons are connected to one or bothwheels. From there, other neurons can be attached which will send action potentials to thewheels, making them go forwards of backwards.In 2016 at the University of Wisconsin- Milwaukee, NeuroBytes were introduced to aphysiology class of 162 students [1]. The ideawas to use this new technology to see if itimproved student engagement and retention. Onegroup was given the patella tendon reflex labdesigned by NeuroTinker and the control groupwas given a lab manual based activity to learnabout the reflex. Through the usage of a pre andpost assessment questionnaire, data was collectedthat determined the students who used
Engineering, etc.The Center for Advanced Automotive Technology (CAAT) is one of 42 AdvancedTechnological Education Centers in the U.S. located at Macomb Community College(MCC, Warren, MI) and Wayne State University (Detroit, MI), who received grant fromthe National Science Foundation to develop few related courses in automotive materialsand lightweighting technologies [1]. MCC in turn approached few other universities andinstructors who have some knowledge in this subject area for help in developing few pilotcourses and to possibly deliver those to the community college students as series of corecourses. Thus, the course developed by the author “Design with Aluminum” under NSFGrant No. 1400593 is one of many other courses that other faculty developed
data suggests that participants’ most challengingexperiences clustered into two dominant groups: 1) self-directed learning, and 2) teamwork andcommunication.The results are intended to inform both capstone faculty and industry to identify areas of strengthand improvement. Our recommendations target current practices in capstone education includingcourse design and structure as well as industry onboarding practices.IntroductionEngineering education has seen numerous shifts over the past 30+ years designed to betterprepare students for contemporary practice. These shifts include the development of capstonedesign courses in the late 1980s, the shift towards outcomes-based accreditation with the adventof EC 2000, the inclusion of cornerstone
inclusive term forthis discipline. Today, core concepts of controlling a system with electronics and communicationtechnologies is fundamental to mechatronics systems. Mechanical, electrical and electroniccommunications have continued to evolve at an accelerating rate during recent decades andmanifest themselves in mechatronic systems.1-2Any robot also represents its own mechatronic system. The robot has a number of sensors thattakes in information (e.g., a signal from a clock, verbal command, etc), processes that inputsignal to an analog or digital input command. Thee commands are delivered to an analog ordigital controller that analyzes the situation based on expected values of these processed inputsignals. A different set of conditional signals
the propertiesof the same material in different processed states. Themotive here is to stimulate discovery and pose questions:why does this property change in that way when thematerial is processed? Why is this other property leftunchanged? Worked examples show what you can do Figure 1. The Process-Structure-with the package, and a set of “micro-projects” (with Properties-Performance tetrahedronspecimen answers for the Instructor’s use) promptstudents to explore for themselves. We have trialled the package, which is currently in Pre-release form, with a small number of students and are now looking for feedback, particularlyon the Interactive Phase Diagram Tool and the Active Learning “Micro Projects”. The paperwill
subject, types ofactivities, particular focus, and so on. These engineering notebooks are also known as Portfoliosor Journals in some other schools or disciplines. In one form or another, they are widely adoptedin engineering programs and courses. “Student portfolios” are recognized by ABET as anexample of data collection processes for the evaluation of Student Outcomes [1].In the “Introduction to Engineering Design” course, we had been using paper-based LabNotebooks (LN); students would employ a standard three-ring binder, and insert and organizehand-written notes, sketches, and other records of project-related work, including many pages ofprintouts of computer-generated contents. Noting the limitations of paper-based LN andpotential benefits of