Journal of Engineering Education最新文献

{"title":"Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges","authors":"Muhsin Menekse","doi":"10.1002/jee.20539","DOIUrl":"https://doi.org/10.1002/jee.20539","url":null,"abstract":"Generative artificial intelligence (AI) technologies, such as large language models (LLMs) and diffusion model image and video generators, can transform learning and teaching experiences by providing students and instructors with access to a vast amount of information and create innovative learning and teaching materials in a very efficient way (e.g., U.S. Department of Education, 2023; Kasneci et al., 2023; Mollick & Mollick, 2023; Nikolic et al., 2023). For example, Google Bard and OpenAI ChatGPT are LLMs that can generate natural language texts for various purposes, such as summaries of research papers (e.g., OpenAI, 2023). At the same time, Midjourney and DeepBrain AI are diffusion models that can create diagrams (e.g., concept maps), images, and videos from textual or visual inputs. Engineering education, in particular, can benefit from integrating and utilizing generative AI technologies to improve instructional resources, develop new technology-enhanced learning environments, reduce instructors' workloads, and provide students with opportunities to design and develop their learning experiences. These technologies can help educators to create more personalized, effective, and engaging learning experiences for engineering students. Most engineering students struggle to acquire a deep understanding of complex engineering concepts because of the nature of the highly mathematical concepts, lack of prior knowledge, limitations of the large lectures, limited resources that prevent the use of commercially available lab equipment, and the lack of innovative teaching tools that could be utilized to enhance learning experiences (e.g., Menekse et al., 2018, 2022; Miller et al., 2011; Reeves & Crippen, 2021; Streveler & Menekse, 2017). These factors adversely affect retention and graduation rates and inhibit persistence in engineering majors (e.g., Estrada et al., 2016). Generative AI technologies and tools (e.g., CourseMIRROR) could support engineering educators to improve students' learning and engagement (e.g., Fan et al., 2015; Luo et al., 2015; Menekse, 2020).","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"578-582"},"PeriodicalIF":3.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compassion and engineering students' moral reasoning: The emotional experience of engineering ethics cases","authors":"Nihat Kotluk,&nbsp;Roland Tormey","doi":"10.1002/jee.20538","DOIUrl":"https://doi.org/10.1002/jee.20538","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>There has been an increase in interest in emotion in engineering and science ethics education. There is also evidence that emotional content in case studies may improve students' learning and enhance awareness, understanding, and motivation concerning ethical issues. Despite these potential benefits, however, emotions' relationship to moral reasoning remains controversial, with ongoing debate as to how much and in what way emotional content impacts on moral reasoning. Furthermore, only limited empirical research has explored how emotions affect students' moral reasoning in educational settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study was to determine whether mild to moderate compassion-induced engineering ethics case contents affected the moral reasoning schemas activated in students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We conducted experimental research using the Engineering and Science Issues Test (ESIT). First, we modified the six case studies of the ESIT, to increase the compassion associated with the cases' protagonists to a mild to moderate level. We tested this instrument with 207 participants to ensure the changes did affect compassion without impacting on other potential emotions. Then, in a second study with 305 participants, we investigated whether the changed compassion intensity of the protagonists in the case studies affected the moral reasoning schemas activated in participants.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The induction of mild to moderate compassion did not impact the moral reasoning schemas activated. Findings also show that we managed to affect compassion intensity in the case studies without changing other emotions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study reveals how to include a targeted emotion in engineering case studies in order to improve students' learning without affecting the moral reasoning schemas activated.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"719-740"},"PeriodicalIF":3.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Belonging as a gateway for learning: First-year engineering students' characterizations of factors that promote and detract from sense of belonging in a pandemic","authors":"J. B. Buckley,&nbsp;B. S. Robinson,&nbsp;T. R. Tretter,&nbsp;C. Biesecker,&nbsp;A. N. Hammond,&nbsp;A. K. Thompson","doi":"10.1002/jee.20529","DOIUrl":"https://doi.org/10.1002/jee.20529","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>A predictor of student success, sense of belonging (SB) is often inhibited for minoritized students in engineering environments and difficult to foster in online courses. A shift to remote learning formats necessitated by COVID-19, therefore, posed an additive threat to SB for engineering first-year students, especially those with minoritized identities. Research is needed to understand impacts of online learning to SB for engineering students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose Hypothesis(es)</h3>\u0000 \u0000 <p>The study examined factors that promoted or detracted from SB in engineering in remote courses and ways in which identity related to SB.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design Method</h3>\u0000 \u0000 <p>Part of a larger mixed-methods study, this article examines focus group data from 31 first-year engineering students in 2020 to characterize student experiences in engineering courses moved online during COVID-19.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In addition to the mutually reinforcing nature of SB and learning, findings reveal that the major factors of (a) peer interactions, (b) instructor behavior and course design, (c) environmental identity cues, and (d) personal and psychological factors influenced SB. Examples of factors that positively contributed to SB in remote-delivery courses included platforms for open communication with peers, “live” ability to ask complex questions, and a critical mass of peers of similar identity; example factors hindering SB included limited use of cameras in synchronous classes, elitist peer interactions, instructor focus on academic performance (vs. growth), and feelings of self-doubt.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Both identity and COVID-19 impacted SB for students, with results showing four pathways to support SB and learning for diverse students in engineering across course formats.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"816-839"},"PeriodicalIF":3.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50152707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Latiné engineering students resist White male engineering culture: A multi-institution analysis of academic engagement","authors":"Patton O. Garriott,&nbsp;Ayli Carrero Pinedo,&nbsp;Heather K. Hunt,&nbsp;Rachel L. Navarro,&nbsp;Lisa Y. Flores,&nbsp;Cerynn D. Desjarlais,&nbsp;David Diaz,&nbsp;Julio Brionez,&nbsp;Bo Hyun Lee,&nbsp;Evelyn Ayala,&nbsp;Leticia D. Martinez,&nbsp;Xiaotian Hu,&nbsp;Megan K. Smith,&nbsp;Han Na Suh,&nbsp;Gloria G. McGillen","doi":"10.1002/jee.20536","DOIUrl":"https://doi.org/10.1002/jee.20536","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although participation rates vary by field, Latiné and women engineers continue to be underrepresented across most segments of the engineering workforce. Research has examined engagement and persistence of Latiné and White women in engineering; however, few studies have investigated how race, ethnicity, gender, and institutional setting interact to produce inequities in the field.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To address these limitations, we examined how Latina, Latino, and White women and men students' engagement in engineering was informed by their intersecting identities and within their institutional setting over the course of a year.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We interviewed 32 Latina, Latino, and White women and men undergraduate engineering students attending 11 different predominantly White and Hispanic Serving Institutions. Thematic analysis was used to interpret themes from the data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our findings illustrate how Latinas, Latinos, and White women developed a strong engineering identity, which was critical to their engagement in engineering. Students' engineering identity was grounded in their perceived fit within engineering culture, sense of purpose for pursuing their degree, and resistance to the dominance of White male culture in engineering. Latinas described unique forms of gendered, racialized marginalization in engineering, whereas Latinas and Latinos highlighted prosocial motivations for completing their degree.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Findings suggest that institutional cultures, norms, and missions are critical to broadening participation of Latinas, Latinos, and White women in engineering. Disrupting White male culture, leveraging Latiné students' cultural wealth, and counter-framing traditional recruitment pitches for engineering appear to be key in these efforts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"695-718"},"PeriodicalIF":3.4,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative artificial intelligence and engineering education","authors":"Aditya Johri,&nbsp;Andrew S. Katz,&nbsp;Junaid Qadir,&nbsp;Ashish Hingle","doi":"10.1002/jee.20537","DOIUrl":"https://doi.org/10.1002/jee.20537","url":null,"abstract":"The recent popularity of generative AI (GAI) applications such as ChatGPT portend a new era of research, teaching, and learning across domains, including in engineering (Bubeck et al., 2023; Kasneci et al., 2023; Lo, 2023; Qadir, 2023). In this guest editorial, we discuss the potential impact of GAI for engineering education as researchers and teachers. We see this editorial as the start of a serious dialogue within the community around how GAI can and will change our practices, and what we can do to respond to these shifts. GAI is built on foundational models (FMs) that can be adapted to various other tasks, such as large language models (LLMs), and they operate by learning from many examples and becoming very good at predicting the subsequent probable output or output sequence. Given the abundance of digitized data, they can quickly learn a wide range of topics and respond to user queries almost instantly. Whether engineering a new software application, writing a code snippet to analyze data, designing a product, or composing a cover letter for a job application, GAI users can leverage the power of LLMs to generate outputs that meet their specific needs (UNESCO, 2023). The ability to learn a skill and adapt it to new contexts is a capability that humans have excelled at for a long time. Some would even argue that the competence to learn original things in new environments to tackle novel problems, and teach it to others, is one of the most unique characteristics of our species (Tomasello, 2009). To assist us in this process, we also have the capability to continually create tools and techniques, another distinct trait of humans and central to the engineering profession (Johri, 2022). What, though, is the potential and limit of developing tools and technologies that can mimic and even go beyond what we have conceived of as human intelligence? What potential consequences do technology that can generate novel outputs have for society, especially education in terms of both benefits and harms (Bommasani et al., 2021; Farrokhnia et al., 2023)? What implications does this have for engineering educators (Johri, 2020)? While we discuss how GAI shapes research and teaching practices within engineering education, we recognize that there are additional implications for the use of GAI for self-motivated and sustained learning initiated by learners on their own. That topic is beyond the scope of this editorial and discussed in some detail in the Menekse et al.0s guest editorial in this issue.","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"572-577"},"PeriodicalIF":3.4,"publicationDate":"2023-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50149531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-explanation activities in statics: A knowledge-building activity to promote conceptual change","authors":"Jose Luis De La Hoz,&nbsp;Camilo Vieira,&nbsp;Carlos Arteta","doi":"10.1002/jee.20531","DOIUrl":"https://doi.org/10.1002/jee.20531","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The complexity and diversity of problems and concepts in different engineering subjects represent a great challenge for students. Traditional approaches to teaching statics are ineffective in helping some students overcome the learning barriers that underlie learning statics and developing problem-solving skills.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This article explores how self-explanation activities may support student learning in statics. Specifically, this study examines the characteristics of student self-explanations of worked examples and their relationship with students′ conceptual change.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>The study population included 147 undergraduate engineering students enrolled in a statics course. The students wrote their self-explanations at each step of an incomplete or incorrect worked example in the context of static equilibrium. Students′ self-explanations were qualitatively analyzed using content analysis to identify the approaches used. We used descriptive and inferential statistics to identify differences in students′ conceptual understanding of statics, based on their approach to self-explanation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified four self-explaining approaches: restricted explanations, elemental explanations, inferential explanations, and strategic explanations. After completing the activity, students who self-explained incomplete worked examples showed better results in the quality of their explanations and conceptual change than students in the incorrect worked example condition.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The findings suggest a relationship between the type of worked example, students' approaches to self-explaining, and their conceptual change and problem-solving skills in statics. To increase the quality of the students' explanations and to improve their conceptual understanding, additional prompts or initial training in self-explaining may be required within the worked-examples context.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"741-768"},"PeriodicalIF":3.4,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50124194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Person-centered analyses in quantitative studies about broadening participation for Black engineering and computer science students","authors":"David Reeping,&nbsp;Walter Lee,&nbsp;Jeremi London","doi":"10.1002/jee.20530","DOIUrl":"https://doi.org/10.1002/jee.20530","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>There have been calls to shift how engineering education researchers investigate the experiences of engineering students from racially minoritized groups. These conversations have primarily involved qualitative researchers, but an echo of equal magnitude from quantitative inquiry has been largely absent.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This paper examines the data analysis practices used in quantitative engineering education research related to broadening participation. We highlight practical issues and promising practices focused on “racial difference” during analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Scope/Method</h3>\u0000 \u0000 <p>We conducted a systematic literature review of methods employed by quantitative studies related to Black students participating in engineering and computer science at the undergraduate level. Person-centered analyses and variable-centered analyses, coined by Jack Block, were used as our categorization framework, backdropped with the principles of QuantCrit.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Forty-nine studies qualified for review. Although each article involved some variable-centered analysis, we found strategies authors used that aligned and did not align with person-centered analyses, including forming groups based on participant attitudes and using race as a variable, respectively. We highlight person-centered approaches as a tangible step for authors to engage meaningfully with QuantCrit in their data analysis decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our findings highlight four areas of consideration for advancing quantitative data analysis in engineering education: operationalizing race and racism, sample sizes and data binning, claims with race as a variable, and promoting descriptive studies. We contend that engaging in deeper thought with these four areas in quantitative inquiry can help researchers engage with the difficult choices inherent to quantitative analyses.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"769-795"},"PeriodicalIF":3.4,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50153361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the tension between persistence and well-being in engineering doctoral programs","authors":"Kanembe Shanachilubwa,&nbsp;Gabriella Sallai,&nbsp;Catherine G. P. Berdanier","doi":"10.1002/jee.20526","DOIUrl":"https://doi.org/10.1002/jee.20526","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>While studies examining graduate engineering student attrition have grown more prevalent, there is an incomplete understanding of the plight faced by persisting students. As mental health and well-being crises emerge in graduate student populations, it is important to understand how students conceptualize their well-being in relation to their decisions to persist or depart from their program.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose/Hypothesis</h3>\u0000 \u0000 <p>The purpose of this article is to characterize the well-being of students who endured overwhelming difficulties in their doctoral engineering programs. The PERMA-V framework of well-being theory proposes that well-being is a multifaceted construct comprised of <i>p</i>ositive emotion, <i>e</i>ngagement, <i>r</i>elationships, <i>m</i>eaning, <i>a</i>ccomplishment, and <i>v</i>itality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>Data were collected in a mixed-methods research design through two rounds of qualitative semistructured interviews and a survey-based PERMA-V profiling instrument. Interview data were analyzed thematically using the PERMA-V framework as an a priori coding schema and narrative configuration and analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The narratives demonstrated the interconnectedness between the different facets of well-being and how they were influenced by various experiences the participants encountered. The participants in this study faced prolonged and extreme adversity. By understanding how the multiple dimensions of well-being theory manifested in their narratives, we better understood and interpreted how these participants chose to persist.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"587-612"},"PeriodicalIF":3.4,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50116350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Persistence at what cost? How graduate engineering students consider the costs of persistence within attrition considerations","authors":"Gabriella M. Sallai,&nbsp;Matthew Bahnson,&nbsp;Kanembe Shanachilubwa,&nbsp;Catherine G. P. Berdanier","doi":"10.1002/jee.20528","DOIUrl":"https://doi.org/10.1002/jee.20528","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;While previous work in higher education documents the impact of high tuition costs of attending graduate school as a key motivator in attrition decisions, in engineering, most graduate students are fully funded on research fellowships, indicating there are different issues causing individuals to consider departure. There has been little work characterizing nonfinancial costs for students in engineering graduate programs and the impact these costs may have on persistence or attrition.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose/Hypothesis&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Framed through the lens of cost as a component of the expectancy–value theory framework and the graduate attrition decisions (GrAD) model conceptual framework specific to engineering attrition, the purpose of this article is to characterize the costs engineering graduate students associate with attending graduate school and document how costs affect students' decisions to persist or depart.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design/Method&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Data were collected through semistructured interviews with 42 engineering graduate students from R1 engineering doctoral programs across the United States who have considered, are currently considering, or have chosen to depart from their engineering PhD programs with a master's degree.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In addition to time and money, which are costs previously captured in research, participants identified costs to life balance, costs to well-being, and identify-informed opportunity costs framed in terms of what “could have been” if they had chosen to not go to graduate school. As these costs relate to persistence, students primarily identified their expended effort and already-incurred costs as the primary motivator for persistence, rather than any expected benefits of a graduate degree.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The findings of this work expand the cost component of the GrAD model conceptual framework, providing a deeper understanding of the costs that graduate students relate to their persistence in engineering graduate programs. It evidences that motivation to persist may not be due to particularly strong goals but may result from costs already incurred. Through this research, the scholarly community, students, advisors, and university policymakers can better understand the needs of engineering graduate students as they navigate graduate study.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 ","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"613-633"},"PeriodicalIF":3.4,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50134812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward developing a valid and reliable assessment of adults' nature of engineering views","authors":"Erdogan Kaya,&nbsp;Hasan Deniz,&nbsp;Ezgi Yesilyurt","doi":"10.1002/jee.20524","DOIUrl":"https://doi.org/10.1002/jee.20524","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Pre-college engineering education reform documents aim to help students develop engineering literacy. Helping students develop sophisticated epistemological views about engineering makes a significant contribution toward developing engineering-literate citizenry. Despite these widely recognized benefits, research on students' and teachers' epistemological views about engineering, commonly termed nature of engineering (NOE) views, has remained stagnant due to the absence of an open-ended questionnaire whose collected evidence of validity indicates its appropriateness for assessing students' and teachers' NOE understanding.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study is threefold: (a) describing the development of a new open-ended instrument, the Views of Nature of Engineering Questionnaire version B (VNOE-B), (b) providing evidence in support of the VNOE-B validity and reliability, and (c) discussing the implications of the newly developed instrument for future research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We aimed to provide evidence for the content and face validity of the VNOE-B by seeking input from a panel of science/engineering educators. Evidence for the construct validity was determined by analyzing the open-ended questionnaire and semistructured interview responses provided by pre-service and in-service teachers (considered novices in this field) and experts (engineers and engineering educators).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our findings indicate that the VNOE-B is consistently successful in differentiating between experts' and novices' NOE views. All engineering experts held sophisticated NOE views across all NOE views under study, whereas most novices held partially informed or uninformed NOE views.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Evidence of validity supports the appropriateness of the VNOE-B as an open-ended instrument for assessing adults' NOE views. Its application may guide teacher educators in determining the efficacy of teacher professional development in meeting the aim of improving in-service teachers' NOE understanding. Additionally, VNOE-B may potentially inform the success of pre-service teacher training.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":"112 3","pages":"634-673"},"PeriodicalIF":3.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50147276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}