hasbrought new concerns for younger generations. Students are able to find quick answers throughonline videos, blogs and similar websites but they do it without any deep analysis and sometimeswithout questioning the source [1]. It means that they have quick access to half-deliveredinformation to finish full projects in easy steps without understanding the underlying theory.Without the motivation of learning, the student-engagement with the program, its academic workand retention can be affected [1-4]. There is evidence that academic disengagement increasessteadily over an undergraduate engineering experience [5] and that students have low level ofresilence and discipline due to lack of motivation [6]. These are some of the reasons why newermodels
option could be provided to expandthe filter size indefinitely or to have multiple filters16-20. The prototype is shown in Figure 1. (a) (b) Figure 1. Prototype Design with (a) Front view and (b) Side view The prototype device consists of one inlet and two outlets (one exhaust and one leading toa water tank). The inlet and outlets are all equipped with solenoid valves. Near the inlet and exhaustare carbon dioxide sensors that are constantly monitoring the concentration of CO2. In betweenthe inlet and outlets is a vacuum pump air compressor. The prototype design is a two-stage system:Adsorption and Regeneration. During the Adsorption stage, the inlet and exhaust solenoid
engineering by providing interesting design projects and fun competitions; • improving the oral and pictorial communication skills of students.Literature ReviewABET criteria for accrediting engineering program requires “a culminating major engineeringdesign experience that 1) incorporates appropriate engineering standards and multiple constraints,and 2) is based on the knowledge and skills acquired in earlier course work.” ABET studentoutcomes place an emphasis on teamwork and effective communication [1]. An earlyintroduction to these requirements is achieved through this course, as it is a designed-basedcourse for all engineering students in which they work in a team. Project-based curriculum andactive learning techniques have been
object; rather the model demonstrates the hydrostatic pressuredistribution exerted by a liquid on a flat surface. The motivation for the model was the desire tohelp students avoid common errors when calculating the resultant force produced by thehydrostatic pressure distribution on a submerged flat surface.Underlying fluid mechanics conceptHydrostatic pressure in a liquid increases linearly with distance below the liquid surface. This isoften represented by a triangular pressure distribution drawn on a vertical section through the flatplane on which the pressure is acting. The plane can be either vertical or inclined, so the sectionrepresents the plane by either a vertical or inclined line as shown in Figure 1. Figure 1
, labactivities sometimes become too focused on equipment rather than learning. Lee and Ceylan [1]note how student learning becomes passive rather than active when students follow cookbookapproaches with large pieces of equipment and no prior operating experience. From anadministrative standpoint, what is the point in purchasing and maintaining costly and largeexperimental equipment that students will only interact with for a few hours during their entireundergraduate education? The ASME Vision 2030 [2] suggests that Mechanical Engineeringcurricula must encourage and provide opportunities for active discovery-based learning in orderto meet the demands of the profession into the future.Each successive generation is more comfortable with technology than
multiple energy resources.This paper introduces a smart grid implementation using multiple DG sources that include wind,solar photovoltaic (PV), and hydrogen fuel cells in a junior-level electrical power system classoffered in a B.S. in Electronics and Computer Engineering Technology program. The DGsources include a 1 kW hydrogen fuel cell unit, a 0.5 kW wind turbine, and a 0.5 kW solar PVpanel array. The DG units are connected to a DC bus bar in which a state-of-the art dataacquisition and control interface (DACI) developed by FESTO smart grid technologiesconstitutes a smart grid implementation supported by a low-voltage data acquisition and control(LVDAC) software for monitoring and recording overall power system operation variables andfinal
hours per semester.1. IntroductionChina’s undergraduate computing education, started in the late 1950s, has gone throughtremendous changes in recent decades, both in numbers and in contents, in response to thechanging needs of the economy, research, and social development. By 1960, about 15universities in China offered computing related programs2 [1]. According to [2], there are 2,603computing related programs with 901,000 students in these programs in 2015, making up forabout 16 percent of the entire engineering student body in China. The graduates coming out ofthe pipelines are playing increasingly more important role in science, engineering, andeconomics of the country in general. The graduates are also becoming very competitive atvarious
the learning environment created by the instructor [1-2]. A properlydesigned learning environment ought to minimize the influence of the teacher on learning whilemaximizing the learning habits of the student. One potential method for doing this is to create a“specifications-based” learning environment wherein the assessment of student performance isstructured around requiring students to engage course materials in ways that are consistent witheffective learning. This paper examines the application of a specifications-based environment tothe design and delivery of a Statics/Dynamics course.The specifications-based approach outlined in this paper is derived from two disparate fields.The first is that of Statistical Process Control as practiced
this paper describes a Linear Systems laboratory project that involves designing a simplifiedspeech recognition system to recognize the 5 long vowel sounds for a team of 3 or 4 students. Thisproject is assigned soon after the student has been introduced to the Fourier Transform in theassociated Linear Systems lecture course. This paper describes the Laboratory project byillustrating the solution with a specific example drawn from real data for a single student team.This laboratory project has the primary goals: 1. Understand the importance of the Fourier Spectrum for developing useful signal analysis algorithms and systems. 2. Develop a speaker-independent vowel classification system to distinguish the 5 long vowel sounds for a
Computer Science (STEM)in the 21st century and ensuring the competitiveness of the United States in the global economy[1]. To date, there have been a number of emerging efforts to integrate computational thinkingwith STEM education [2], and there are many opportunities for students to learn aboutcomputers, computer programming and computational thinking in K-12. For example, Carnegie-Mellon University, Purdue University, the Computer Science Teachers’ Association (CSTA), theInternational Society for Technology in Education (ISTE) and others are leading the way to bringcomputer science and computational thinking to K-12 through programs like CS4HS [3,4]. Also,in elementary and middle schools, a student can take enrichment programs in Scratch [5
. Introduction Spatial reasoning is an important predictor of student success in STEM fields [1], [2]. Sorby reports that spatial cognition has been a focus of research for nearly a century. One important part of spatial cognition is "spatial visualization, which is defined as the process of 'apprehending, encoding, and mentally manipulating three dimensional spatial forms'" [3]. Given this importance of spatial visualization, an important question is if and how students' spatial visualization skills can be developed. Sorby studied the effect of students taking a 1-credit spatial skills course, and found multiple benefits: improved performance in introductory
within the InformationSciences and Technology (IST) Department at George Mason University (GMU). Thiscomparison effort will help us confirm the most effective algorithm to identify students’ who areat risk of failing a class so that academic advisors/instructors can offer better academic guidanceand support.Keywords: Classification algorithms, navigational behavior, performance prediction, LearningManagement System.1. IntroductionOne of the major goals in any higher educational institution is to improve students’ performance.A Learning Management System (LMS) can be used as a platform to assess students’performance. Several universities have been using LMS for the past few years which is asoftware application that helps to administer, track and
coordinatesystem used [1],[2]. The idea and use of FBDs to aid in the development and solution ofmechanics problems is not new and is a standard tool in first year level mechanics course. Aliterature review into the effect of using FBDs reveals that students who drew correct FBDs weremore likely to solve problems correctly [3]-[5] and found that drawing inaccurate FBDs led tomore incorrect solutions [5] and/or led to failing the course [3]. Alternatively, other researchersnoted the opposite result; that the quality [6] or correctness [7] of the FBDs did not impactstudent performance significantly. Furthermore, similar studies noted that students who createdhigh quality FBDs were likely to produce low quality equations [8]. A potential explanation forsome
below was given to six undergraduate courses containing over three hundred studentstotal. These courses ranged from freshman level to senior capstone design classes. Question 1 Have you ever been in the Endeavor laboratory building? Yes No 2 Have you had structured lab classes in the Endeavor? Yes No 3 Have you had major specific lab classes in other buildings? Yes No 4 Does your program/ department have its’ own lab building? Yes No 5 If yes, do you feel included in your program/ department lab building? Yes No 6 In general, do you feel isolated from other students in you Major? Yes No 7 In general, do you feel
steps of constantcomparative analysis.This review suggests the existence of at least 31 factors that can potentially impact the successfulimplementation of RBIS in the classroom. Hence, they could be barriers or drivers toinstructional change in higher education. These 31 factors were classified and organized into sixcategories: 1) culture, 2) change management, 3) institutional support, 4) pedagogical knowledgeand skills, 5) students´ experience, and 6) faculty motivation.BackgroundSeveral reports on engineering education make the call to change pedagogical approaches inengineering by increasingly embedding research on learning into teaching practices [1-3]. Thistype of change, that involves a transformation in instructional practices and
, diversity, and inclusion in Additive Manufacturing.IntroductionAdditive manufacturing (AM) is a set of processes by which physical objects are made from digitalfiles generated by computer-aided design software. The term encompasses seven differenttechnologies, as per ASTM nomenclature [1], powder bed fusion, material jetting, directed energydeposition, binder jetting, vat photo polymerization, material extrusion and sheet lamination.These technologies use a variety of feedstock materials such as polymers, metals, ceramics, andconcrete by systematically depositing layer upon layer to create a near net shape of the final partrequired. As opposed to traditional machining techniques like CNC, milling, machining, in AM,material is added instead of
. Gainen and Willemsen [1] assert that calculus provides thefoundation for future engineering courses. Without a good foundation in calculus, engineeringmajors will have difficulty in applying the knowledge in their junior or senior level courses.Many aspects of engineering require an application of calculus such as: design of storm drainand open channel systems; calculation of forces in complex configurations of structuralelements; analysis of beams (i.e., shear forces, bending moment, deflection, stress distribution);analysis of structure relating to seismic design; design of a pump based on flow rate and head;calculations of bearing capacity, lateral earth pressure, and shear strength of soil; computation ofearthquake induced slope
study is not to necessarily recommend one tool, butto bring important information into one place to make it easier for instructors to compare andselect the tool that will work for them, their students, and their course.Background Assessment and feedback are important parts of the learning process. However,providing individualized feedback to students can be very time consuming for faculty andteaching assistants. Therefore it is important to provide authentic assessments and feedback thatsupport learning [1] while balancing the time required by course staff. New computer-basedtools have been developed to assist instructors with grading and feedback beyond the traditionalmultiple-choice Scantron based test. Learning management
in the industry. c American Society for Engineering Education, 2019TEAM MENTAL MODELS IN ENGINEERING DESIGN CONTEXTS 1 A systematized literature review of the characteristics of team mental models in engineering design contexts AbstractDesign tasks are characterized by high levels of complexity and uncertainty. Accordingly, inengineering design practices, engineers communicate, share, and integrate their differentviewpoints and orientations to develop a deeper understanding of the problem space and tobroaden the solution space. In this context, engineering design is usually taught
experiences of these womenin the engineering workplace. This systematized literature review synthesizes research on theexperiences of women within the non-academic, engineering workplace. This review examinesfemale engineers from an international perspective and is not limited to female engineers in theUnited States. Using scholarly articles, this review seeks to answer the following questions: 1)What types of experiences do women in the engineering workforce encounter in the workplace?2) How do these experiences influence women to leave or persist in the engineering workforce?In addition to answering the following questions, this review also seeks to identify any areaswhere further research is warranted. Using qualitative methods and analysis, three
engineeringinstructors often form teams in the classrooms. However, many factors can affect theeffectiveness of teamwork. One factor that could affect the result of teaming is the diversityin teams. Although team diversity could increase creativity and innovation in teams, if notmanaged well, it could also have negative consequences for teams. Of the various forms ofdiversity, race and gender have received the most attention in the literature, likely becausethey provide visual cues to teammates. In this study, we conducted a systematized literaturereview related to the race and gender in teamwork. To do this systematized literature reviewwe followed the procedure suggested by Borrego, Foster, and Froyd [1]. We searched fourdifferent databases including
lessons learned and the potential foruptake in other courses and institutions.I. IntroductionWhile the Accreditation Board for Engineering and Technology (ABET) has identified effectivecommunication as a critical competency and writing skills are widely recognized as beingimportant for practicing engineers and scientists [1], strategies for developing thosecommunication skills in engineering students have been rather limited. Engineering facultytypically feel more certain of their ability to convey technical material than to teach (or respondto) student writing. At the level of an individual course, one common model is for technicalfaculty to collaborate on assignment design and response with co-teachers who specialize inwriting or communication
challenge and is common to nearly all Unit OperationsLaboratory courses – in a recent survey, 69 out of 70 programs reported that their Unit Opsstudents work in teams [1]. This means that Unit Ops courses must have a strategy for placingstudents into teams, which by itself is a difficult problem and an active area of research.Instructors have several options for assigning teams. One is team self-selection (allowing studentsto choose their own groups), which requires minimal effort on the part of the instructor. However,there are several drawbacks associated with self-selection, including bad student experiences,team homogeneity, clique behavior, and negative effects on students’ perception of many aspectsof the course. These are well-summarized by
-based learning featuring prominently. It is common for Olinstudents to be enrolled in at least one course every semester in which they are expected orrequired to make something – from mechanical toys and autonomous robots to circuits andsoftware and the machine shop and the library strive to make tools available for students to usewith as low a barrier to entry as possible while still preserving a culture of professionalism andrespect for tools.Previous Guidelines at Olin CollegeOlin College students practice engineering design early through a variety of project-basedexperiences, and all students are enrolled in an “introductory experience”. This helps preparestudents for these design experiences, as all 1st year students (approximately 100
pipeline and graduationrates. The process has also deepened our understanding of the needs of students in terms ofhow to better align student career aspirations with industry workforce needs. Theeffectiveness of the collaborative model could be replicated among other institutionsinterested in promoting engineering degrees among Hispanic and low income students.INTRODUCTIONPowerful indicators suggest that there may be more than 1 million new jobs in STEM fieldsby the year 2026, and, as a group, they will grow twice as fast as the average for alloccupations in the economy, according to recent projections by the Department of Labor,Bureau of Labor Statistics [1]. Equally powerful indicators suggest that Hispanics are one ofthe fastest growing
unique contributions and novel approaches to solving today's complex challengesand those of the future. Common areas of concern have been the ability to modernize mid-sizedfactories that lack funds to advance aging technology. The digital age has provided cost effectivealternatives to increase productivity and allow customization of products6-8. All companies needto be better positioned to integrate these new technologies into their manufacturing and businesspractices in order to remain competitive in the global economy. In particular, enabling technologiesand research advances in future manufacturing will be discussed. Figure 1. New horizons for next generation manufacturing workshop flyer
-strand research program focused on (1) authentic assessment, often aided by interactive technology, and (2) design learning, in which she studies engineers designing devices, scientists designing investigations, teachers designing learning experiences and students designing to learn.Dr. Jamie Gomez, University of New Mexico Jamie Gomez, Ph.D., is a Senior Lecturer III in the department of Chemical & Biological Engineering (CBE) at the University of New Mexico. She is a co- principal investigator for the following National Science Foundation (NSF) funded projects: Professional Formation of Engineers: Research Initiation in Engineering Formation (PFE: RIEF) - Using Digital Badging and Design Challenge Modules to
are presented first. Next a history ofABET’s accreditation policies and practices, and a limited account of present-day accreditationprocedures as practiced is presented to provide vital evidence related to ABET’s evolvinggovernance model. Preliminary conclusions from the data set are then presented, with openquestions suggested by the analysis to date.Theory and MethodThe interpretive findings of this paper draw from the larger project, which was organized as anexploratory qualitative study of engineering education governance built around the use ofgrounded theory methods [1-3]. The study is built on semi-structured interviews with a multi-site, multi-scale design. The interview protocol was derived from the project’s seven coreresearch
school is a small Historically Black College and University (HBCU) which offersengineering programs in civil engineering, electrical engineering, industrial engineering andtransportation systems. We serve a very diverse student population of about 1,300 undergraduatestudents in the School of Engineering (SOE) along with 135 graduate students pursuing master’sand doctoral programs. The engineering programs are supported by 35 full time faculty andabout 30 adjunct instructors. All four engineering programs are currently preparing for ABETreaccreditation in Fall 2019. Three programs are to be reviewed under the EngineeringAccreditation Commission (EAC); Civil, Electrical and Industrial Engineering [1] and oneprogram under the Applied and Natural
to explain whydeterministic systems are able to change in unpredictable ways, while complex systems theory seeks toexplain how the often numerous actors within a system interact with one another to engender change(Wolf-Branigin, 2013). According to Wolf-Branigin (2013) and Heylighen (2008), complex systems shareat least three characteristics: 1. The system is self-organizing: Through interacting with one another, the actors within a system spontaneously (i.e., without direction from a centralized authority) arrange themselves to create a global system structure. In terms of organizational change, this means that change within an organization cannot be generated by a central authority, but rather must be championed and