objectives. Ultimately, at the close of the Capstone course, students arerequired to present a design solution to their client that meets expectations.Literature suggests that project success could depend on many factors which also contribute toteam members’ overall satisfaction. These factors include balancing team members’ projectinterests, their desire to work with specific peers with varied personalities, and withconsideration to institutional project priority [1-3]. Balancing all these factors during teamformation is time-consuming for course instructors, but doing so is crucial for teams’ success incompleting projects. Team formation in the Capstone course is a key activity undertaken by allcourse instructors in cooperation, as it plays a
among students raises questions about their accuracy andpotential to enhance learning outcomes. For instance, studies have demonstrated that while LLMsexcel at automating repetitive tasks and providing structured outputs, they often exhibit limitationsin handling complex and context-dependent tasks such as CPM and PERT calculations. Accordingto Nenni et al. (2024), AI's ability to analyze large datasets and assess risks significantly enhancesproject management, yet challenges remain in its adaptability to nuanced scenarios [1]. Similarly,Taboada et al. (2023) highlighted application of AI on PMBOK’s eight performance domains,including planning and delivery, but emphasized the need for educators to ensure these tools areused to complement, not
Engineering Management Academic Leaders (CEMAL) and Program Chair and Chair of the Engineering Management Division (EMD) of ASEE. Dr. Asgarpoor is currently serving as President of the American Society for Engineering Management (ASEM). ©American Society for Engineering Education, 2025 A comparative analysis of student performance outcomes in online and in-person classesAbstractThe COVID-19 pandemic energized a wave for online education that had started a couple ofdecades earlier [1] which has persisted beyond the pandemic. Seventy one percent of studentssurveyed in 2021 reported they would continue at least some form of online learning even post-pandemic [2]. The popularity of
training must be varied to help determine system performanceaccurately. This is important because system performance results dictate future course of actionin engineering management or DoD decision-making. Such results inform acquisition decisionssuch as further funding and development, program canceling, and fielding decisions.KeywordsTest scenario variation, pretest sensitization, video game, nested factorial design.1. IntroductionAs part of the U.S. Department of Defense acquisition process, a program office develops aproduct per the needs/requirements defined by a service, such as the Army. Within the Army, theArmy Test and Evaluation Command tests and evaluates the product to determine if it fills thecapability gap(s), providing critical
demand for professionals equippedwith unique skill sets that complement AI systems is surging [1], [2]. To maintain a competitiveedge in this evolving environment, educational institutions must prepare students not only withtechnical knowledge but also with professional skills such as critical thinking, adaptability,creativity, collaboration, and ethical decision-making [3], [4]. These competencies are essentialfor thriving in AI-enhanced workplaces, where traditional roles are being redefined, andinterdisciplinary approaches are becoming the norm. In light of these challenges, the role ofeducators is pivotal in reshaping curricula and teaching strategies to address the gaps betweentraditional education and the demands of AI-driven industries [5
education to help overcome the common misconception that onlinelearning is a solitary, self-paced, non-instructor led activity [1], and it retains the social andparticipation aspects that are a key factor in the success of online learning [2]. The 2010 meta-analysis performed by the U.S. Department of Education [3] found that, on average, students inonline learning conditions performed modestly better than those receiving face-to-faceinstruction. Moreover, using the live interaction instruction possible with Zoom and BlackboardCollaborate could help address the Sloan-C quality elements including learning and costeffectiveness and institutional commitment, access, faculty and student satisfaction [4].IntroductionAlthough the successes and student
Artificial intelligence (AI) continues to be felt both in academia and industry, yetits full potential is yet to be exploited for the common good. While AI technologies areincreasingly being implemented, questions linger over their long-term impact on education andthe workforce. Recent research efforts have focused on promoting the ethical and responsible useof AI. As a result, explainable AI, which focuses on helping users understand how AI systemsmake decisions, has received a lot of interest [1].Even though some skepticism about AI decisions still lingers, it is already transformingworkplaces by enhancing efficiency, automating repetitive tasks, and enabling data-drivendecision-making. AI technologies such as natural language processing help to
years.KeywordsEngineering Summer Camp, Program Streamlining, Lean Systems, Student Engagement,Operational Efficiency, Continuous Improvement.1. IntroductionEngineering summer camps have long served as a dynamic gateway for cultivating students’early interest in STEM fields, particularly engineering. Through immersive, hands-on activitiesand personalized interactions with faculty, these programs give young learners a chance toexplore various disciplines—including mechanical, electrical, civil, and computer engineering—and gain insight into potential career paths. Recent studies underscore the power of well-designed summer camps to spark curiosity, strengthen self-efficacy, and shape academicaspirations, especially among students who might otherwise lack exposure to
another part of the study, we compared the studentperformance in all sections with the one section where discussion board participation was notrequired.Literature ReviewIn 2014, University of Marylan Global Campus [1] decided to use open educational resources(OER) in statistics and college algebra courses, and pilot tested Pearson MyLab in a few sectionsof a course and compared the outcomes. They have reported positive outcomes since usingMyLab, including a substantial increase in student success. The success rate changed from 60%to 80% in statistics and 50% to 80% in algebra. Faculty evaluations also improved since theywere spending more time on student-teacher relationships rather than grading the assignments.A study from 2008 [2] compared the
struggling andmake adjustments to the curriculum and program structure to better prepare graduates for success.Additionally, the analysis of student performance can provide valuable feedback to faculty andadministrators, helping them to continually improve and adapt the program to meet the changing needs ofthe industry. Overall, a thorough analysis of student performance in the first year of an engineeringgraduate program is an essential component of ensuring that graduates are well-prepared to succeed intheir careers and make meaningful contributions to the field of engineering [1].The analysis of student performance in their first engineering graduate program has been a key aspect ofevaluating the effectiveness of such programs for many years. In
explores the influence of implementing the EFQM model on customerperformance, emphasizing its structured qualitative and quantitative approach to monitoring andenhancing strategic planning. Lessons learned from the EFQM model’s organizationaldeployment offer actionable strategies to enhance strategic planning, leadership, and processoptimization. This is useful for engineering education as a quality management tool.Keywords: EFQM Excellence Model, Quality Management, quantitative method, hypothesistesting, qualitative method, RADAR. 1. Introduction The European Foundation for Quality Management (EFQM) Excellence Model serves asa comprehensive framework for achieving organizational development and strategic alignmentby emphasizing
are typically ledby project managers (PM) and the focus is on completing the project within the triple constraintsof scope, time, and cost. Program management is typically led by the program managers (PGM)and focuses on strategic initiatives such as alignment of multiple projects, finance, resourceallocation, coordination of cross function activities, and to collaborate with other business units.The PGM manages in a collaborative fashion incorporating program, project, and systemsengineering into an integrated systems approach. This approach has been applied in manyindustries for complex efforts such as New Product Development (NPD) in the aerospace,automotive, healthcare, and information systems. Figure 1 is an illustration of a typical
ofdiverse professional backgrounds and to function effectively on a team [1]. Researchers [2]further expanded the purposefully general criteria provided by ABET “into six main groups:problem-solving and Critical thinking, Communication, Team Work, Ethical Perspective,Emotional Intelligence, and Creative Thinking.” While these researchers provided an analysis ofthe literature, they also identified that many engineering students are ultimately deficient in theirability to communicate effectively. This sentiment was also recognized by Riemer [3], whoreferenced studies by [4] and [5].The ProblemMany engineering programs have limited ability to add courses, as university general educationrequirements and accreditation requirements challenge programs to
leadership development and institutionaleffectiveness. The findings contribute to academic discourse and provide practicalrecommendations for fostering leadership excellence in higher education, ensuring sustainablegovernance and positive societal impact.Keywords: Higher Education Institutions, leadership styles, managers’ development,governance, organizational models, institutional effectiveness.INTRODUCTIONThe organizational structures of Higher Education Institutions (HEIs) can be understood throughthree primary models: mechanistic, organic, and anthropological (Figure 1) [1], [2].Misalignment in the application of these models often results in governance deficiencies. Forinstance, the mechanistic model, which operates as a technical system
Ye is a professor of the Institute of China’s Science Technology and Education Policy, Zhejiang University. His research interests include Engineering Education, Science Technology and Education Policy. ©American Society for Engineering Education, 2025 How to Cultivate Digital Engineering Management Talents: A Case on the “Digital Intelligence Innovation and Management” Engineering Doctoral Program1 IntroductionIn the current context of the world’s comprehensive promotion of digitaltransformation, improving the digital literacy and skills of talents is the top priority ofquality education in higher education[1]. In 2022, China released the world’s firsthigher education
testament to the transformative potential of education—notonly in shaping individual careers, but also in advancing industries and influencing society atlarge.References 1. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. This article discusses how businesses implement AI successfully and emphasizes managerial decision-making, similar to the focus of the course. 2. Raj, P., & Swami, S. N. (2020). Artificial intelligence and machine learning for business: A no-nonsense guide to data-driven technologies. Springer. This book provides a managerial perspective on AI technologies, making it relevant to your course’s emphasis on non-technical AI