Collection

ASEE Mid-Atlantic Section Spring Conference

Authors

Fisseha Gebre, University of the District of Columbia; Devdas Shetty, University of the District of Columbia; JIAJUN XU P.E., University of the District of Columbia

Paper ID #44798Educating Undergraduate Students in Theory, Practice and Experience inAdditive ManufacturingDr. Fisseha Gebre, University of the District of Columbia Fisseha Gebre is a postdoctoral researcher in the School of Engineering & Applied Sciences at Center for Advanced Manufacturing in Space and Technology & Applied Research (CAM-STAR) lab in the University of the District of Columbia. He received his Ph.D. in mechanical engineering from Indian Institute of Technology, Bombay (IITB). His current research works include: (1) Parameter optimization and Characterization of powder-based 3D metal printing process

Collection

ASEE Mid-Atlantic Section Spring Conference

Authors

Roni A. Romero Melendez, University of the District of Columbia; Nian Zhang, University of the District of Columbia; J'niya Butler, University of the District of Columbia; Wagdy H Mahmoud P.E., University of the District of Columbia; Lara A Thompson, University of the District of Columbia

different heart conditions such as heart disease. Heart disease, orcardiovascular disease, is the leading cause of death for men, women, and people of most racialand ethnic groups in the United States, with heart disease responsible for 1 in every 5 deaths inthe United States in 2021 [1]. From 2018-19, the Unites States spent $239.9 billion in health careservices, medicines, and lost productivity due to death [2]. It is estimated that around 17.9million lives are lost worldwide due to heart disease/cardiovascular disease [3]. Understandingthe condition of the heart using ECG signals is not new, however, ECG produces a waveformwhich generates a large amount of data that is difficult to process. Deep learning can be used toexpedite this task and in

Collection

ASEE Mid-Atlantic Section Spring Conference

Authors

Maxine Fontaine, Stevens Institute of Technology (School of Engineering and Science); Frank T Fisher, Stevens Institute of Technology (School of Engineering and Science); Ashley Lytle

. Frank T Fisher, Stevens Institute of Technology (School of Engineering and Science)Dr. Ashley Lytle ©American Society for Engineering Education, 2024 Establishing Baseline Measurements of Adaptive Expertise in First-Year STEM StudentsAbstractAdaptive expertise is a construct developed to identify the cognitive skills involved inrecognizing when and how to apply knowledge to successfully solve complex problems. Theframework adopted for this study decomposes adaptive expertise into four distinct constructs: (1)multiple perspectives, (2) metacognition, (3) goals and beliefs, and (4) epistemology.The aim of the study is to establish baseline measurements along the four dimensions of

Collection

ASEE Mid-Atlantic Section Spring Conference

Authors

Gary P. Halada, Stony Brook University; Shyam Lal Sharma Sharma P.E.; Lori Scarlatos; Yi Zhang, Stony Brook University

combines modular“learnshops,” or learning workshops, with gamified learning. The project is invitational inapproach, inclusive of diverse knowledge systems, inquiry-based to engage a diverse body oflearners, and innovative in its application of culturally-informed pedagogies. In Phase 1 (Fall2023), we helped the first cohort of STEM educators explore and apply inclusive pedagogies forredesigning their existing courses, enhancing instruction and assessment, and engaging andmentoring students more effectively.Across higher education, and especially in STEM fields, educators are grappling with a systemiclack of diversity, equity, inclusion, and social justice. Universities have not only struggled todiversify their student populations but also to make

Collection

ASEE Mid-Atlantic Section Spring Conference

Authors

Devang Jayachandran, Pennsylvania State University, Harrisburg, The Capital College; Jeremy Joseph Blum, Pennsylvania State University, Harrisburg, The Capital College

, Code Generation Pipelines, Contest Programming.IntroductionProgramming contests are competitions in which participants attempt to write computerprograms that solve algorithmic puzzles. Past studies have identified a range of pedagogicalbenefits for student participation in these contests, including enhancing learning outcomes bydeepening conceptual comprehension and fostering team collaboration, along with equippingstudents for technical job interviews [1-2]. These benefits, notwithstanding, a number of hurdlesexist to expanding participation in these contests [3].Artificial Intelligence (AI) tools, like ChatGPT, have been found to lower barriers toparticipation in contest programming [4]. Generative AI tools can provide scaffolding