Collection

2025 ASEE -GSW Annual Conference

Authors

Haiying Huang, The University of Texas at Arlington; Monica Franco, The University of Texas at Arlington

Tagged Topics

Diversity

lectures & those learnt from previous courses, especially from math courses. 3. Visualize solutions, Discuss the solution steps, alternative approaches, expected results, results & evaluation plan and evaluation plan. Using flow diagram, sketches, etc. are strongly encouraged. Reflect on the plan before proceed (break point #1). 4. Solve the problem Follow the planned solution steps. Do not skip steps! 5. Evaluate the results Follow the evaluation plan. Discuss the results with others (breakpoint #2). Re-visit the goal and constraints if necessary 6. Report methods & Write down detailed step-by-step solution following the

Collection

2025 ASEE -GSW Annual Conference

Authors

Nandika D'Souza, University of Texas at Dallas; Hector R. Siller, University of North Texas; Hyun Kyoung Kyoung Ro, University of North Texas; Debbie Huffman, North Central Texas College; Mary J Combs, Quality Measures

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Diversity

University of Texas at Arlington, Arlington, TX Copyright  2025, American Society for Engineering Education 2school graduates over the next 15 years. This growth will reflect a more diverse student population,with 75% of graduates being non-White. To address workforce needs, Texas has a few initiativesunder the Closing the Gaps exas's accountability system 2. The system utilizes the followingparameters1. Academic Achievement • Reading/Language Arts (RLA): Measures student performance on standardized tests in reading and language arts. • Mathematics: Assesses student performance on standardized math tests.2. Graduation Rates • Four-Year Graduation Rate: Tracks

Collection

2025 ASEE -GSW Annual Conference

Authors

Araceli Martinez Ortiz, The University of Texas at San Antonio; Gabriela Gomez, The University of Texas at San Antonio; Patricia Rodriguez Ann Rodriguez, The University of Texas at San Antonio

Tagged Topics

Diversity

their level of agreement with STEM-related attitudinal statements usinga 5-point Likert scale: 1 = Strongly Disagree, 2 = Somewhat Disagree, 3 = Neither Agree norDisagree, 4 = Somewhat Agree, 5 = Strongly Agree.The items for each survey were organized into five key constructs: (1) Interest in STEM, (2) Self-Efficacy, (3) Collaboration, (4) Academic Engagement, and (5) Sense of Belonging. Each constructencompassed a set of targeted items designed to assess specific aspects of students’ experiences andattitudes. A content analysis was conducted to identify recurring themes within the survey questions.Items were then grouped into one of the five key construct categories that best reflected thesethemes, ensuring each question aligned with a

Collection

2025 ASEE -GSW Annual Conference

Authors

Sandipon Chowdhury, West Texas A&M University; Swastika Bithi, West Texas A&M University

Tagged Topics

Diversity

Total Students= 36 6-7 Grade Little bit knowledge Figure 4: Water-related knowledge assessment for 6–7 grade students in STEM educationThe STEM-based water education program was assessed among 23 grades 8–12 students, focusingon groundwater sustainability, the water cycle, aquifer filtration, and engaging activities.Knowledge levels were categorized as vast knowledge and little knowledge to evaluate learningoutcomes. The results showed that 91% of students had vast knowledge about groundwatersustainability, while 9% had limited knowledge. 96% demonstrated vast knowledge of the watercycle, and 4% had limited knowledge. In groundwater quality and filtration, 100% of studentsdisplayed vast knowledge, reflecting strong comprehension

Collection

2025 ASEE -GSW Annual Conference

Authors

Mohammad Waqar Mohiuddin, Texas A&M University, College Station, Texas; Jonathan Weaver-Rosen, Texas A&M University; Carlos R. Corleto P.E., Texas A&M University; Joanna Tsenn, Texas A&M University; Shadi Balawi, Texas A&M University

Tagged Topics

Diversity

essential teamwork skills critical for theiracademic and professional success. IntroductionTeamwork is essential for success in undergraduate engineering education and professional practice.Engineering projects often involve collaboration among individuals with diverse disciplines andexpertise, requiring students to effectively contribute, communicate, delegate tasks, and resolveconflicts1. Team members bring unique perspectives and ideas, fostering creativity and innovation,essential for tackling complex problems2. Furthermore, the ability to thrive in team settings is highlyvalued by employers, as it reflects adaptability, collaboration, and leadership qualities3.Developing teamwork skills during

Collection

2025 ASEE -GSW Annual Conference

Authors

Mengqi Monica Zhan, University of Texas at Arlington; Grace Ellen Brannon, The University of Texas at Arlington; Liwei Zhang, The University of Texas at Arlington; Frank K. Lu, The University of Texas at Arlington

Tagged Topics

Diversity

belonging and resilience, ultimately enhancing retention in aerospace engineering and otherSTEM fields. The study protocol is under review with the Institutional Review Board, with approvalanticipated in January 2025.Specifically, we will ask the following questions: • Can you walk me through your experience participating in the self-led research project? What tasks or activities were you involved in, and what was the overall process like for you? • Reflecting on that experience, how has your participation in the research project influenced your understanding of your own skills? Have you discovered any new abilities, or have you become more or less confident in certain areas? How has participation in this program grow

Collection

2025 ASEE -GSW Annual Conference

Authors

Alexander Hernandez, West Texas A&M University; Sanjoy Bhattacharia, West Texas A&M University; Sarah Petters, University of California, Riverside; Markus Petters, University of California, Riverside

Tagged Topics

Diversity

criteria 3, 5, and 6 were successfully addressed through the evaluation ofLO2. For the assessment of the seven environmental science graduate students, all participantsscored 100%, far exceeding the threshold of >80% for LO2. Further analysis of the lab reportsdemonstrated that each group successfully collected reasonably accurate experimental data,processed the data to generate statistical figures, and conducted analyses to determine the phasetransition temperatures of the samples. These outcomes reflect the students’ ability to applyexperimental methods and data analysis techniques effectively.As part of an outreach effort targeting early undergraduate students, we invited students from alocal community college Amarillo College (AC) to

Collection

2025 ASEE -GSW Annual Conference

Authors

Chinedu Okonkwo, The University of Texas at San Antonio; Roy Uzoma Lan; Ibukun Gabriel Awolusi, The University of Texas at San Antonio; Jiannan Cai

Tagged Topics

Diversity

“learning from practice.” Students were made to practiceusing the provided code templates and make adjustments to see the impact of different AI models onprediction accuracy. Pre- and post-implementation surveys, together with hands-on laboratoryassignments, were administered to evaluate students’ perception of improvement in AI knowledge,confidence, and relevance to their career. The findings of the study indicate the effectiveness of thelearning module incorporated into the course with the students' perception of AI knowledge,learning confidence, and relevance to career increasing by 39%, 22%, and 6%, respectively. Theseresults reflect the students' understanding and appreciation for the importance of data and theexploration of historical