young engineering students. Gage said that weas Appalachian people know we are smart, we are capable, we are good people. He said “If youbelieve what the people are telling you, it will crush you.” Keep fighting. Everyone has had apunch in the face. Don’t give up. Steve noted that in an ever changing world, it is important to bea lifelong learner and to always be ethical. Wayne noted that several of the people he grew upwith got engineering degrees, got their doctorate, and returned to Appalachia. These people serveas examples of what Appalachian people can achieve. Wayne echoed Gage’s sentiment thatAppalachian engineering students can’t go in half-heartedly. They must give 100%.Limitations and Future Work This study was conducted as a
academic work ethic [20], [21], [22], studentnetworks [23], and mentor guidance[24], [25], [26]. Using CCW is an avenue to conduct asset-based research, which highlights the strengths of students rather than weaknesses. This criticaland assets-based approach makes explicit the strengths and assets of communities, in this casewithin makerspaces. CCW can frame the experiences of students who might not always be seenthrough an explicit and purposeful focus on assets they bring into the space. This is apersonalized approach to understand the student staff’s experience as opposed to the neutraloutputs of their experience such as what they are creating or how many machines they are usingin the space.Research Question: What are the assets student staff
has not previously been discovered – qualitative researchers often havelittle or no concrete idea of what meanings, patterns, or relationships between themes will beidentified in a new data set. They are asking the research question because the meaning is unclearand requires new data and interpretation to provide meaning. At the same time, a qualitativehypothesis may still exist at a higher level: even that “X type of data about Y topic/phenomenonfrom P participants will generate meaningful answers to my research question.” Then, the DataCollection stage requires clear, ethical (e.g., IRB approved, if including human participants), andstructured data generation just as quantitative research; however, qualitative data comprises wordsor
form the basis of the study results, as discussed in the next section.6.4 Ethical and Trustworthiness ConsiderationsEthical considerations were addressed by obtaining informed consent from the participants andensuring that they were aware of the purpose of the study, their rights as participants, and thepotential risks and benefits involved. Confidentiality and anonymity were maintained byassigning pseudonyms to the participants and storing their data securely.Trustworthiness considerations were addressed by conducting an interrater reliability test. Toenhance the dependability and consistency of the analysis, the researcher enlisted a second coderto code a portion of the data independently. The interrater reliability score of 87% was
’ pedagogical beliefs, beliefs aboutthemselves, and beliefs about technology in integrating technology into the K-12 curriculum[25], [26]. According to Margot & Kettler [27], while PreK-12 teachers valued STEM education,they reported challenges on the structural and institutional level, pedagogy, assessment, andconcerns over students. Yet such challenges can be overcome. Research has shown that preservice teachersbenefit from improved STEM engagement, especially emotional engagement, after participatingin the robotics unit in a teacher preparation course [28]. Practice integrating technology-relevantactivities using robots boosted participants’ confidence and knowledge (of teaching practice,safety, and ethical issues) and their likelihood
, forgiveness, originality, future-mindedness, spirituality, high talent, and wisdom. At the group level, it is about the civic virtues and the institutions that move individuals toward better citizenship: responsibility, nurturance, altruism, civility, moderation, tolerance, and work ethic” (p. 5).With the conceptualization of this study through positive psychology, we attempt to be able toknow about the positive role the institutions can play to support an overall environment ofwellbeing and thriving for undergraduate engineering students. We argue that if institutions canprovide such an environment, the MHW of engineering undergraduates can be improved to suchan extent that the possibility of ending up with mental problems may be lessened.4
attitudes were accompanied familial encouragement.Supportive experiences prompted Anderson to pursue a career linked to his personal interestsand natural skill, developing an affinity for engineering, math, and science. His parentsmaintained a supportive stance on academic achievement and provided invaluableencouragement deemed critical to his success. STEM-related family trips and educational giftswere formative of engineering interest. Family career attitudes and promoted work ethic haveinformed Anderson’s approach to entry and success in engineering.Valuing family advancement encouraged Chris’s engineering selection. The family’s engineeringand medical pedigree provided early exposure to academic challenges and success. Supportingcollege degree
Associate Teaching Professor and the Vice-Chair for Undergraduate Education in the Computer Science and Engineering Department at UC San Diego. In addition to research related to Automata Theory and Computability education, she works on projects that support professionalization pathways for students, including industry internships, TA development, and ethics and communication. Her research and teaching have work has been supported by grants and awards from UC San Diego, NSF, and industry partners.Kristen Vaccaro, University of California San Diego Kristen Vaccaro is an Assistant Professor of Computer Science & Engineering at the University of Cali- fornia San Diego, where she is also a member of the Design Lab. Her
approach,instead it could be a solidification of already existing ideas alongside a change in how these ideasare communicated.Public standards within a knowledge generating community are established guiding principles,ideals, and goals which are used to evaluate knowledge, theories, and outcomes [13]. We expectstandards adopted by an EER team could be related to data quality/validation, disciplinary norms,research ethics, stakeholder requirements, or standards specifically applicable to that team. Whilewithin an idealized knowledge generating community, the standards would be shared among allmembers of the team, we anticipate that on EER teams there may be certain standards that are notshared across the team or present in different ways from one
and project management from industry and government settings.Dr. Jessica Koehler, Wake Forest University Dr. Jessica Koehler is the Senior Research Scholar for the Wake Forest University Program for Leadership and Character in the Professional schools. In her role she also supports with the development and assessment of character and ethics education in the engineering program.William N. Crowe, Wake Forest University ©American Society for Engineering Education, 2024 Enhancing Knowledge Surveys with an Intellectual Humility ScaleAbstractAs engineering education and related research evolve, it is also important for assessment toolsand research
the dataset and thedeveloping set of themes, refining and defining them. Themes were identified based on therecurrence, relevance, and significance of the codes in relation to the research question. Theserefined themes formed the basis for the results section.4.4 Ethical and Trustworthiness ConsiderationsEthical considerations were addressed by obtaining informed consent from the participants andensuring that they were aware of the purpose of the study, their rights as participants, and thepotential risks and benefits involved. Confidentiality and anonymity were maintained byremoving any person identifiers from the data, assigning pseudonyms to the participants, andstoring their data securely. The trustworthiness of the data analysis was
, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology Nursing Forum, 41(5), 545-547. https://doi.org/10.1188/14.ONF.545-547Case, J. M., & Light, G. (2011). Emerging research methodologies in engineering education research. Journal of Engineering Education, 100(1), 186-210. https://doi.org/10.1002/j.2168-9830.2011.tb00008.xDerry, S. J., Pea, R. D., Barron, B., Engle, R. A., Erickson, F., Goldman, R., ... & Sherin, B. L. (2010). Conducting video research in the learning sciences: Guidance on selection, analysis, technology, and ethics. Journal of the Learning Sciences, 19(1), 3-53. https://doi.org/10.1080/10508400903452884Ellis, A
International handbook of the learning sciences, F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, and P. Reimann, Eds., New York London: Routledge, Taylor & Francis Group, 2018, p. 44‒53.[44] R. Curry, “Insights from a cultural-historical HE library makerspace case study on the potential for academic libraries to lead on supporting ethical-making underpinned by ‘critical material literacy,’” J. Librariansh. Inf. Sci., vol. 55, no. 3, pp. 763–781, Sep. 2023, doi: 10.1177/09610006221104796.[45] J. Johannessen and B. Olsen, “Aspects of a cybernetic theory of tacit knowledge and innovation,” Kybernetes, vol. 40, no. 1/2, pp. 141–165, Mar. 2011, doi: 10.1108/03684921111117979.[46] National Academies of Sciences, Engineering
this week capturing at least five photos that you feel best capture yourexperiences and some of the challenges you may face participating in an REU for the first time.You can find some guiding questions below to help you: ● What are your experiences and some of the challenges you may face participating in an REU? ● How has your participation in this program changed your view on computer science/computing? ● How has your participation in this program affected your life?Each photo should have a unique title and caption. The caption should be no more than a few sentences.Keep in mind some of the technical and ethical considerations we discussed in the introduction.Step 2: Turn in pictures.Initial photo submissions are due by [due date
Consultants to assist engineering undergraduates with technical reports. She publishes and presents research in two fields: engineering ethics and writing, and literature.Dr. Hyesun You, The University of Iowa Hyesun You, Ph.D., is an assistant professor in the Department of Teaching and Learning. Before joining UI, Hyesun worked as an assistant professor at Arkansas Tech University. She also previously served as a post-doc fellow at New York University and Michigan State University, where she participated in NSF-funded grant projects. She earned her BS in Chemistry and MS in science education from Yonsei University. Her MEd in quantitative methods and Ph.D. in Science Education at the University of Texas at Austin
discipline and welcome individuals tocome as their whole selves without expecting them to acculturate to dominant ways of speaking.Diverse people bring unique strengths to the table, and their presence changes engineering forthe better. The language resources these Multicompetent Learners bring to the classrooms couldhelp us reimagine engineering learning environments where students stay true to themselves andtheir community values to create equitable and socially just technologies and solutions for all.References[1] D. Morales‐Doyle, “Discussant, empowering students in engineering: Ethical and transformative learning approaches for a socially conscious future”, 2024 NARST Conference, Denver, CO, United States, 2024, March 17-20.[2] E
, systems thinking [6],design thinking [7], and computational thinking [8] shape engineering identity. Computationalthinking, with its focus on algorithmic problem-solving, is a vital skill for engineers [9]. Integratingcomputational skills early and regularly in engineering curricula has been shown to improvestudent outcomes [10]. Similarly, we propose that incorporating data skills throughout thecurriculum can also strengthen engineering formation.Data skills refer to the ability to collect, organize, analyze, visualize, and communicate dataeffectively and ethically. Engineering students practice data skills in various assignments, such asconducting experiments, designing solutions, and evaluating results. These assignments mirror thereal-world
include religion, age, gender, etc. [8, 9].Although models using these predictors yield somewhat accurate results, they don’t consider thestudents’ work ethic or study habits. Therefore, we plan to factor in students’ efforts whenpredicting their course performance.One of the best ways to measure how much a student cares about their academic performance isto analyze their participation in the class [1, 10, 11]. A discussion forum is a platform that enablesstudents to seek help from their peers and instructors. Multiple studies have focused on producingand analyzing the statistical correlation between discussion forum data and student courseperformance [11, 12, 13]. While statistical correlations can benefit inference, student
assessment;artificial intelligence in educationIntroduction Research indicates that college and engineering students often lack essential skills requiredby employers, such as communication, decision-making, problem-solving, leadership, emotionalintelligence, and social ethics [1], [2]. This gap between college preparation and career demands isparticularly evident in the engineering field, where technical knowledge is prioritized over softskills like creativity, innovation, leadership, management, and teamwork [3]. Moreover, the shiftfrom traditional instruction to skill-based curricula has gained momentum in educational settingsto center the learner in education. This approach encourages students to engage in hands-onactivities, problem
techniques that accurately reflect the varied ways in whichstudents learn. Starting from this, new evaluation methods are being sought that better fit the wayof learning of each student, so our research will focus on finding a new form of evaluation basedon frequent unannounced evaluations to improve student learning. and contribute to academicintegrity. This new method was applied in civil engineering and architecture courses, along withactivities that develop student learning.Background/FrameworkAcademic integrity within the student environment is related to honesty, responsibility, andrespect, and implies that students must follow rules and regulations, demonstrating theircommitment to responsibility and ethics against frowned upon activities
Engineering at Georgia Tech, focuses on advancing written, visual, and verbal communication skills. Her research centers on affect theory and its application to technical communication, specifically information design. Jill studies how to enhance the effectiveness of pedagogical documents by incorporating principles from affect theory. Through her work, she aims to empower students, fostering an environment where they actively shape their communication interactions, including teamwork and ethical discussions. By integrating these principles, she goes beyond traditional methods, ensuring that students not only learn but also take an active role in shaping their communication experiences.Dr. Christie Stewart, Georgia
administration.The research protocol of using these institutional data received the approval of the university’sresearch ethics board.4.2 Data Analysis MethodsFor the purposes of the analysis, the variables in the linked data files were grouped into threecategories: (1) student experience; (2) learning outcomes; (3) demographics and background.The details about the variables are included in Appendix A. The missing values in the originaldata sets for those variables constituted a very small proportion, with 7% as the highest. Beforethe data analysis, we imputed variables in the categories of student experiences and learningoutcomes using the median values; and we did not apply any imputation to variables in thecategories of demographics and background.To
Engineering at the University of Toronto. She previously completed her Bachelors in Industrial Engineering also at the University of Toronto. She is passionate about supporting women in Engineering and STEM more broadly, both within and outside of her research. She has held fellowships in Ethics of AI and Technology & Society organizations.Dr. Alison Olechowski, University of Toronto Alison Olechowski is an Assistant Professor in the Department of Mechanical & Industrial Engineering and the Institute for Studies in Transdisciplinary Engineering Education and Practice. She completed her PhD at the Massachusetts Institute of Technology (MIT). ©American Society for Engineering Education