Chi’s (2009) active/interactive framework (for example, the “active learning” category was changed to “classroomgroup work” and lecture and guided practice were added to our list). The final list included eightinstructional strategies (see Table 1). Table 1 Categories of instructional approaches Instructional Strategies Descriptions Used to Build Survey 1 Classroom Group Work Working in pairs or groups to address questions about the material, and working in pairs or groups to answer problems or challenges that have been posed by the instructor. 2 Artifact Dissection Students work together to disassemble a common product (e.g., sewing
educational exercises. Theelectrical operation is controlled by an Arduino, which can be reprogrammed by connecting it toa computer with a USB cable. Thus, the same device can be used to introduce sensor interfacingand data collection, or to study feedback control of thermal systems. Those applications are notdiscussed here.Motivation: A conceptual exercise for an undergraduate heat transfer courseWe introduce the convection experiment to students with a thought experiment. For additionalcourse materials, including lecture slides and laboratory worksheets, visit http://web.cecs.pdx.edu/˜gerry/expt/convection/.Consider a lightbulb exposed to a moving air stream as depicted in Figure 1. Suppose that the airvelocity, u∞ , and the temperature of oncoming
was defined toensure that students would be able to model and analyze elements subjected to axial, shear,bending, and combined loads. Each week the students were required to:1) Prepare for each ANSYS recitation session by completing an assigned ANSYS tutorial before attending the corresponding ANSYS recitation session. No grade was assigned for completing the assigned ANSYS tutorials.2) Work during the recitation session either on completing the assigned ANSYS tutorial due to errors or other issues occurring during their outside-of-class prep work or on completing the assigned ANSYS homework problems.3) Complete the assigned ANSYS homework before the start of the next recitation session.Each self-paced ANSYS tutorial focused on a
, many students still donot understand the full breadth of problems engineers solve. Studies continue to highlightcommon misconceptions about engineering work including gender stereotypes about engineeringand erroneous concepts about the nature of the engineering profession [1][2][3]. Unfortunately,these misconceptions are driving the U.S. towards a large talent gap such that the number ofengineering jobs that need to be filled in the future will outpace the number of engineeringdegrees awarded [4].For those students who eventually decide to pursue engineering, studies have indicated that whenhigh school students, especially first-generation students, choose engineering, their reasons rangefrom having a curiosity and interest in the subject
operationalized them for theengineering design context. While not intended to be exhaustive, these categories, listed below,served as a framework for guiding both the intervention and analysis. Their primary aim was toencourage students to examine several ways in which unconscious processes influence and biasdecision-making. 1. The Views of the People Around Us: How people may be influenced by their social environment and how it may be difficult to not conform to majority views. 2. Human Error and the Limits of Our Perception: The impact of “blind spots” in our perception due to limitations around what we are allowed or able to observe. 3. Internal Beliefs and Biases: This includes heuristics, quick assumptions our mind makes
instructional designs in the context of evidence-based learning principles [1-4].The Importance of STEM EducationSTEM (Science, Technology, Engineering, and Mathematics) education is regarded as animportant factor in building and maintaining a nation's workforce, economy, competitiveness,and security in the modern world. [5], [6]. However, in the United States, there are more STEM-related jobs in the government and private sector than trained individuals to fill those jobs [7],[8]. One way to help fill this gap may be through STEM education at the high school levelbecause STEM education has been shown to promote interest in future STEM college degreesand careers [9], [10]. For instance, many students who enter into a major in STEM-related fieldsat the
a detailed description of the two-way exchange program and summarize resultsfrom a systematic analysis of five reflective learning prompts that were administered to thestudent participants throughout the program (i.e., 1 pre-program, 3 mid-program, and 1 post-program). As further background for these efforts, we summarize relevant prior literaturediscussing strategies for scaffolding and assessing learning outcomes, both in general andspecifically in the context of global engineering programs. Based on our preliminary results, wealso discuss both benefits and challenges associated with this innovative programmaticimplementation. Furthermore, we propose directions for improvement, with an emphasis onstudent recruitment, faculty involvement
pursue graduateeducation. Overall, this paper introduces a replicable methodology for analyzing curricula anddemonstrates its application through a case study of one institution’s computing programs.1 IntroductionThe rapid evolution of the job market, driven by artificial intelligence (AI) and automation, along-side shifting economic demands, underscores the need for an adaptable education system. Al-though educational institutions strive to equip students with the necessary knowledge for success-ful careers, many graduates struggle to land jobs that match their qualifications, even with the highdemand for tech talent. A 2024 study conducted by Hanson et al. [21] found that approximately37% of students in fields such as computer science (CS
inthe data that it is presented to and uses that knowledge to make predictions on data that it has neverseen before. With machine learning, computers do not need to be explicitly programmed to solvea problem. Machine learning utilizes data and algorithms and statistical models to solve a problemusing inference instead of instructions. A simplified machine learning flow is shown in Figure 1.Figure 1: Machine learning flow. Algorithm is trained on training and makes predictions on test data. 2Machine Learning Types Machine learning has three main categories, supervised learning, unsupervised learningand reinforcement learning (Figure 2). Supervised machine learning is a type of machine
CADsoftware in a group setting. While these are preliminary findings, they highlight the potentialvalue of engaging first year engineering students with a CAD software in a group setting withinformal classroom environment.IntroductionDesign has become an integral part of how engineering colleges prepare their students forprofessional practice. Prior work suggests that it is important to help students become “informeddesigners.” This is the designer’s mindset with a level of design expertise that falls between anovice designer and an expert designer (p.779)1. Strategies for facilitating this process have beenpreviously reported in the literature with undergraduate students (e.g., Alien Centered designprojects2), and with high school students3 (e.g
. student in Mechanical Engineering at the University of Delaware c American Society for Engineering Education, 2016 A Revised Undergraduate Controls Lab Featuring Exposure-based Experiences1 IntroductionMost ABET accredited undergraduate mechanical engineering programs have some sort ofcontrols course and accompanying laboratory experience [1]. The goal of most of theselaboratory courses is to give the students hands-on experience working with hardware andimplementing control algorithms while learning the theory in an accompanying lecture course.As early as 1981, Balchen et al. [2] asserted that the criteria for a good experiment is that itshould (1) demonstrate important
will also be discussed.This will go a long way in motivating technology students to take this important, professionalexam, eliminating their fear, improving their understanding, and reinforcing the best practices forlife-long learningIntroductionThe Fundamentals of Engineering (FE) exam is typically the first step in the process leading tothe P.E. license. It is designed for recent graduates and students who are close to finishing anundergraduate engineering degree.The National Institute for Certification of Engineering Technologies (NICET, a division ofNational Society of Professional Engineers (NSPE)) defines technologists 1 as follows:“Engineering technologists are members of the engineering team who work closely withengineers, scientists
students are high need Generation 1.5 students—U.S. educated Englishlearners. At SJSU, they struggle to complete their English and writing requirements, requirementsmeant for their native English peers. Often, these struggles impact their retention and graduationrates from SJSU. The challenges presented by this complicated skill set in Generation 1.5 studentscan be seen most clearly in English writing, a critical competency for academic success at SJSUwhich encompasses retention and graduation. According to Singhal [1], high needs Generation1.5 students have unique needs in the areas of academic writing; in particular, these students needto develop their mastery of academic literacy. Literacy is not only the ability to read and write butit also
compared to9.8%).1 Furthermore, employers claim that there shortages of qualified workers in STEM areas.2National Science Board identifies that the students will be required to develop their STEMcapabilities at higher level as compared to the levels in the past, even for low skilled jobs.3 Tomeet the demand for the STEM work force, there is a dire need to expand the STEM pipeline byincreasing the number of STEM graduates. To stay competitive in the global market in STEMareas, research shows that we need to make sure that US students have needed STEM skillsevery step of the way from K to 8, high school to college which is supported by high qualitySTEM education.4 Interventions needed to fill in the gaps are meant to boost K-12 STEM teacherquality
academicperformance in determining their long-term persistence in STEM subject matter5.This project studies the impact of these factors and their interactions that occur in a system withsociocultural effects occurring at four levels. These levels include: 1) Student enculturation andacademic support systems; 2) Classroom effects, both course design and pedagogy; 3)Departmental Culture; and 4) Interdepartmental Coordination and Interaction (See Figure 1). Thecurrent project is utilizing assessment at each level to both determine key areas in need ofreform, and to feed back results of innovations to stakeholders at each level. It is hypothesizedthat effects at each level act as key drivers of student motivation, achievement and persistence. Inthis paper, we
curricula.Ms. Jane Nicholson Moorhead, Mississippi State University c American Society for Engineering Education, 2015 Hybrid Engineering Matriculation Model to Promote Informed Engineering Major Selection Decisions1. IntroductionStudents who chose an engineering major because they identify with the engineering-relatedactivities of that field are more likely to be retained. The limited knowledge of engineering thatmost students posses when they choose an engineering major negatively effects theircommitment to their selected major 1. Introduction to engineering courses are one way topromote informed engineering major decisions among engineering students 2,3.However, one of the most prominent
seek to bring about change – helps us understand the different ways in which peoplesolve problems individually and as part of a team. When team members’ cognitive styles arediverse, creating an effect known as cognitive gap, the team may experience the advantages ofapproaching problems in diverse ways, but the likelihood of conflicts and misunderstandingsincreases6.This study investigated the relationship between cognitive style and the perceptions of studentsworking in teams about their own ideation. Through the analysis of reflection surveys from 202pre-engineering, engineering, and design students participating in an ideation study, we exploredthe following questions: (1) how does working in teams impact students' perceptions of theirown
the mind map to see if there is evidence of learning, and in this work, we combine ideas from two of the most successful of these metrics by creating a new tool that checks if small sub-graphs exist in both a student and the criterion map (an experts mind map). By analyzing the results of these matches, we create a global metric that we then compare to our previous metrics and find that this new metric has similar behavior. This is positive since this metric provides a means for more interesting feedback to students.1 IntroductionIn this paper, we evaluate a new mind map analysis metric that compares an experts mind map(called the criterion map) to a students map to evaluate how similar the two maps are. In
discusses a major group project using model rockets in atwo-hour per week laboratory that is a part of a two-credit course in exploration of engineeringand technology at the Old Dominion University in Norfolk, Virginia.Introduction:A model rocket is a combined miniature version of real launch and space vehicles. Once amodel rocket leaves the launcher, it is a free body in air. Model rockets have been used asprojects before. Boyer et al. [1] report a similar project for sophomore aerospace engineeringstudents. Figure 1 shows a cross section of a ready to launch model rocket with a B6-4 solidengine. Page 26.1643.2Figure 1. Single stage model rocket with
Effects Grades: Sizeness and the Exploration of the Multiple‐Institution Database for Investigating Engineering Longitudinal Development through Hierarchal Linear Models Page 26.280.2Introduction In a recent study, an effect entitled sectionality was probed to determine the effect ofdifferent course sections at various schools had on students’ grades.[1] A caveat of that studybrought up numerous times in lectures and via private correspondence – one left out of theoriginal paper – was the effect of class size (or sizeness) for the same introductory courses.While anecdotally, faculty from all over the country had discussed with the researchers in thepast few years that
American Society for Engineering Education, 2015 Comparative Dimensions of Disciplinary CultureIntroductionDespite calls to promote creativity as “an indispensable quality for engineering” [1], the U.S.engineering educational system has been slow to develop pedagogies that successfully promoteinnovative behaviors. Engineers need more creativity and interdisciplinary fluency, butengineering instructors often struggle to provide such skills without sacrificing discipline-specificproblem-solving skills. At the same time, engineering programs continue to struggle withattracting and retaining members of underrepresented populations—populations whose diversitycould greatly contribute to innovation. Interestingly, the lack of diversity
advances in science,specifically in communication and information technologies, are resulting in a renewed interestin hands-on (physical and virtual) learning. While laboratories in engineering education provideopportunities for hands-on learning, researchers have found that student learning in labs has notachieved the expected benefits [1, 2]. There are numerous shortcomings in traditional labs thatinclude, for example, short time constraints and high student expectations [3]. When we treat ourstudents as novices receiving existing knowledge (in a lecture and in a highly structured lab),they do not have the opportunity to construct knowledge. Constructionism, as defined by Papert [4], is a pedagogical approach that encourageslearning
relationships that become difficult to correct. Using DBL, thecorrect relationships are clearly identified through the student’s decisions. While DBL shares manycharacteristics with existing methods, it is presented here as a new pedagogy that has not beenstudied prior to this paper.DBL has similarities to existing active learning methods [8-13], but differs in several importantways. First, a general to specific decision set provides the structure for solving novel problems.Second, students receive help with their understanding when they have trouble making thosedecisions. The goal of this method is to build expertise and to increase the chance that a studentcan solve novel and complex problems by: 1) Improving student understanding through the
(SOC) devices(BeagleBone Black1 and Raspberry PI2) that were essentially capable of performing all the dutiesof a computer on a single chip. The need to go beyond the basics of providing an introductorycourse in the microprocessor or microcontroller in Engineering and Engineering Technologytype curriculums has long been overdue. The subject matter covered in System Design hasmatured to the extent that it has been the subject of curriculum content in the form of two ormore courses in most of the universities. The subject course which is the subject of this paper is a400 level course in the Electrical and Computer Engineering Technology Department. This ispreceded by two courses: 1) a C or C++, programming course, that covers the C or C
which require a two-course sequence inthermodynamics. With the arrival of computerized thermodynamic functions, laboriousinterpolation from thermodynamic tables can be reduced or eliminated, allowing more advancedexercises to be formulated. Computerized thermodynamic properties have been introduced forclassroom instruction and for homework assignments at many points over the last decade. Oneof the first to do this was McClain [1] who developed ideal gas thermodynamic properties usingMathCad for dealing with gas turbine problems and other cases where ideal gasses are used. Thiswork was expanded by McClain [2] in establishing exercises for students using the MathCadthermodynamic property functions. This work was continued by Maixner et al [3
), influenced our efforts to develop the teaching standards used for this project. In addition, a framework that articulates what informed design thinking entails – students using design strategies effectively; making knowledge-‐driven decisions; conducting sustained technological investigations; working creatively; and reflecting upon their actions and thinking – was another foundation upon which this work was built (Crismond & Adams, 2012). The final set of the design teaching standards (see Table 1 for details) created for this project is organized around three dimensions: Dimension I – STEM Concepts – Teachers’ understanding of science, technology
substantially finished the first two years earning no more that three grades of D or Fwhile earning better than a grade of C in five courses. Once certified, students can begin thejunior year with its emphasis on Mechanical Engineering courses. One thread of the junior year,is a two-semester sequence, taught once a year, on the topics of intermediate mechanics ofmaterials (fall semester) and machine component design (spring semester). The text used is acustom printing of the Shigley and Mischke 5th edition Mechanical Engineering Design[1] (manystudents find the original online). The first semester covers analytical mechanics while thesecond semester covers applied mechanics. A result of teaching a junior level 2-semestersequence once a year, is that it
asking them togenerate a high-level description of learning activities that met standards for both disciplines.Four humans rated the LLM output – using an aggregate rating approach – in terms of (1) whetherit met the CS learning standard, (2) whether it met the language arts learning standard, (3)whether it was equitable, and (4) its overall quality.Results: For Claude AI, 52% of the activities met language arts standards, 64% met CS standards,and the average quality rating was middling. For ChatGPT, 75% of the activities met languagearts standards, 63% met CS standards, and the average quality rating was low. Virtually allactivities from both LLMs were rated as neither actively promoting nor inhibiting equitableinstruction.Discussion: Our
work has realized the impact of industry-sponsored projects on the students' self-efficacy,in which students on industry-sponsored teams showed larger increases in self-efficacy comparedto university-sponsored [1]. This work aims to closely examine students' self-efficacy by utilizingthe EDSE survey to understand trends amongst cohorts, and understand influencing factors forsuccess.1.1. Capstone Design Capstone Design is a course that students commonly take during their final year ofundergraduate studies in engineering disciplines. This course is typically structured to bridge theworld of education and real-world application [2]. Overall, this course serves as the culminatingexperience for students at the end of their college career
for Industrial Applications. Previously, he had Job experience in the field of Sourcing & Procurement, Production, and Supply chain in different companies in Bangladesh for more than seven years. His GitHub account is https://github.com/Atik1219. and email is atik.kuet.09@gmail.com.Selim Molla, University of Texas at El Paso ©American Society for Engineering Education, 2025Development of Digital Laboratory Modules Using Computer Simulation for Enhanced Learning Experience in Manufacturing Education 1 AbstractThe complexity of modern manufacturing