Courtney J. Wright, Sarah A. Wilson, Joseph H. Hammer, Lucy E. Hargis, Melanie E. Miller, Ellen L. Usher
Background
Engineering students encounter high levels of stress, which may negatively impact their mental health. Nevertheless, engineering students who experience mental health distress are less likely than their peers to seek professional help, even when controlling for gender and race/ethnicity.
Purpose
We examined beliefs that undergraduate engineering students have about barriers and facilitators to seeking professional help for their mental health. We also sought to identify cultural and systemic factors within and beyond engineering that might affect help-seeking. Together, these beliefs influence students' sense of personal agency around seeking mental health care.
Method
We implemented a pragmatic qualitative design that incorporated the integrated behavioral model to investigate engineering students' (N = 33) professional mental health help-seeking beliefs. We used thematic analysis to analyze help-seeking beliefs and perceived barriers and facilitators that students described during interviews.
Results
We identified four themes: Navigating the system impacts personal agency; sacrifices associated with help-seeking act as a barrier; engineering culture acts as a barrier to help-seeking; and student confidence in help-seeking varies significantly. These themes portray the effect of perceived barriers and facilitators on students' personal agency for accessing mental health care. Our findings have implications for engineering departments and university counseling centers that want to minimize barriers to help-seeking.
Conclusions
Engineering stakeholders must improve access to professional help for engineering students. Implementing changes to normalize help-seeking behaviors, enhance personal agency, and facilitate engagement with mental health resources will create better conditions for engineers. Further research is necessary to understand how other beliefs (e.g., attitudes, perceived norms) inform the relationships between student mental health, professional help seeking, and engineering culture.
{"title":"Mental health in undergraduate engineering students: Identifying facilitators and barriers to seeking help","authors":"Courtney J. Wright, Sarah A. Wilson, Joseph H. Hammer, Lucy E. Hargis, Melanie E. Miller, Ellen L. Usher","doi":"10.1002/jee.20551","DOIUrl":"https://doi.org/10.1002/jee.20551","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Engineering students encounter high levels of stress, which may negatively impact their mental health. Nevertheless, engineering students who experience mental health distress are less likely than their peers to seek professional help, even when controlling for gender and race/ethnicity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We examined beliefs that undergraduate engineering students have about barriers and facilitators to seeking professional help for their mental health. We also sought to identify cultural and systemic factors within and beyond engineering that might affect help-seeking. Together, these beliefs influence students' sense of personal agency around seeking mental health care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We implemented a pragmatic qualitative design that incorporated the integrated behavioral model to investigate engineering students' (<i>N</i> = 33) professional mental health help-seeking beliefs. We used thematic analysis to analyze help-seeking beliefs and perceived barriers and facilitators that students described during interviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified four themes: Navigating the system impacts personal agency; sacrifices associated with help-seeking act as a barrier; engineering culture acts as a barrier to help-seeking; and student confidence in help-seeking varies significantly. These themes portray the effect of perceived barriers and facilitators on students' personal agency for accessing mental health care. Our findings have implications for engineering departments and university counseling centers that want to minimize barriers to help-seeking.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Engineering stakeholders must improve access to professional help for engineering students. Implementing changes to normalize help-seeking behaviors, enhance personal agency, and facilitate engagement with mental health resources will create better conditions for engineers. Further research is necessary to understand how other beliefs (e.g., attitudes, perceived norms) inform the relationships between student mental health, professional help seeking, and engineering culture.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50123868","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}
For Inspiration and Recognition of Science and Technology (FIRST) robotics is an international, extra-curricular program that fosters young students' interpersonal skills and career choices in science, technology, engineering, and mathematics (STEM). FIRST teams are guided by mentors, about half of whom are also mentees.
Purpose
To describe and characterize FIRST mentors and their perceptions of their own interpersonal skills and STEM career choice and identify differences by mentor types and gender.
Method
The study participants included 261 FIRST mentors. A convergent mixed-methods approach was used. Data was collected quantitatively via questionnaires and qualitatively via interviews. The analysis was guided by the social cognitive career theory (SCCT).
Results
Fourteen categories were identified to describe and characterize the FIRST mentors. Nine were based on SCCT and five were new: influence of friends, interpersonal skill, personal contribution, challenges, and mentor-as-educator. Differences were found between three types of FIRST mentors: non-FIRST mentors, graduate mentors, and mentee mentors. The correlations between factors and categories indicated that the mentors' perceptions were consistent with those of the mentees. The FIRST program impacted the STEM career choice of men more than women, and external motivation influenced women more than men.
Conclusions
The FIRST program contributes to developing its graduates' interpersonal skills and affects their STEM career choice. The study provides insights into mentors' influence on mentees' career choices, from which both genders benefit. Making FIRST available in schools as a widespread enrichment program is expected to foster students' STEM career choices, thereby contributing to the human resource reservoir of the high-tech industry workforce.
{"title":"Interpersonal skills and STEM career choice of three types of FIRST mentors","authors":"Shahaf Rocker Yoel, Yehudit Judy Dori","doi":"10.1002/jee.20550","DOIUrl":"https://doi.org/10.1002/jee.20550","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>For Inspiration and Recognition of Science and Technology (FIRST) robotics is an international, extra-curricular program that fosters young students' interpersonal skills and career choices in science, technology, engineering, and mathematics (STEM). FIRST teams are guided by mentors, about half of whom are also mentees.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To describe and characterize FIRST mentors and their perceptions of their own interpersonal skills and STEM career choice and identify differences by mentor types and gender.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>The study participants included 261 FIRST mentors. A convergent mixed-methods approach was used. Data was collected quantitatively via questionnaires and qualitatively via interviews. The analysis was guided by the social cognitive career theory (SCCT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Fourteen categories were identified to describe and characterize the FIRST mentors. Nine were based on SCCT and five were new: influence of friends, interpersonal skill, personal contribution, challenges, and mentor-as-educator. Differences were found between three types of FIRST mentors: non-FIRST mentors, graduate mentors, and mentee mentors. The correlations between factors and categories indicated that the mentors' perceptions were consistent with those of the mentees. The FIRST program impacted the STEM career choice of men more than women, and external motivation influenced women more than men.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The FIRST program contributes to developing its graduates' interpersonal skills and affects their STEM career choice. The study provides insights into mentors' influence on mentees' career choices, from which both genders benefit. Making FIRST available in schools as a widespread enrichment program is expected to foster students' STEM career choices, thereby contributing to the human resource reservoir of the high-tech industry workforce.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50154935","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}
Engineers are socialized to value rational approaches to problem solving. A lack of awareness of how engineers use different decision-making approaches is problematic because it perpetuates the ongoing development of inequitable engineering designs and contributes to a lack of inclusion in the field. Although researchers have explored how engineering students are socialized, further work is needed to understand students' beliefs about different decision-making approaches.
Purpose/Hypothesis
We explored the espoused beliefs of undergraduate students about technical, empathic, experience-based, and guess-based approaches to engineering design decisions.
Design/Method
We conducted semistructured one-on-one interviews with 20 senior engineering students at the conclusion of their capstone design experience. We used a combination of deductive and inductive data condensation approaches to generate categories of beliefs.
Results
We identified a total of nine categories of beliefs, organized by approach. Although students' espoused beliefs did reflect the emphasis on technical approaches present in their socialization, they also described technical approaches as limited and overvalued.
Conclusion
The landscape of beliefs presented make explicit both the challenges and the opportunities that students' beliefs play as the backdrop for any efforts of engineering educators to develop engineers as effective and equitable engineering designers.
{"title":"Undergraduate students' espoused beliefs about different approaches to engineering design decisions","authors":"Giselle Guanes, Alexia Leonard, Emily Dringenberg","doi":"10.1002/jee.20544","DOIUrl":"https://doi.org/10.1002/jee.20544","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Engineers are socialized to value rational approaches to problem solving. A lack of awareness of how engineers use different decision-making approaches is problematic because it perpetuates the ongoing development of inequitable engineering designs and contributes to a lack of inclusion in the field. Although researchers have explored how engineering students are socialized, further work is needed to understand students' beliefs about different decision-making approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose/Hypothesis</h3>\u0000 \u0000 <p>We explored the espoused beliefs of undergraduate students about technical, empathic, experience-based, and guess-based approaches to engineering design decisions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We conducted semistructured one-on-one interviews with 20 senior engineering students at the conclusion of their capstone design experience. We used a combination of deductive and inductive data condensation approaches to generate categories of beliefs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified a total of nine categories of beliefs, organized by approach. Although students' espoused beliefs did reflect the emphasis on technical approaches present in their socialization, they also described technical approaches as limited and overvalued.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The landscape of beliefs presented make explicit both the challenges and the opportunities that students' beliefs play as the backdrop for any efforts of engineering educators to develop engineers as effective and equitable engineering designers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20544","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150383","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}
Black women students in engineering higher education are underrepresented and often face barriers at the nexus of race, gender, and engineering. When seeking to improve student outcomes, universities often prioritize academic success and neglect psychological, social, and emotional well-being. Little is known about why and how Black women in engineering engage in Black placemaking as they maneuver around barriers.
Purpose
The purpose of this study is to establish an empirical understanding of the experiences of Black women in engineering. Our work is guided by the following research questions: (1) Why do Black women in engineering seek out campus spaces specifically designated for them? What unique structural issues necessitate such spaces? (2) For what purposes do Black women in engineering use those distinct spaces? (3) How are Black women in engineering responding to the challenges and structural conditions?
Design/Method
We use Black placemaking theory to guide our qualitative in-depth interviews of 45 purposefully selected students. The data were analyzed via theory-informed themes.
Results
Major structural conditions necessitate specific Black spaces, including the absence of physical places, failure to understand intersectionality, failure to respond to safety, and cultural stereotypes about Black women in engineering. In response to such barriers, Black women in engineering engage in multiple placemaking activities, thereby seeking out safe and responsive spaces and transforming existing ones to serve multiple functions.
Conclusions
Whatever difficulties Black women face in academia, they are intensified in engineering. To confront such barriers, Black women engage with Black placemaking, creatively transforming existing places or co-creating distinct places for themselves. These actions can inform how engineering departments support spaces for minoritized student development.
{"title":"Black women's placemaking in undergraduate engineering","authors":"Atota B. Halkiyo, Meseret F. Hailu","doi":"10.1002/jee.20545","DOIUrl":"https://doi.org/10.1002/jee.20545","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Black women students in engineering higher education are underrepresented and often face barriers at the nexus of race, gender, and engineering. When seeking to improve student outcomes, universities often prioritize academic success and neglect psychological, social, and emotional well-being. Little is known about why and how Black women in engineering engage in Black placemaking as they maneuver around barriers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study is to establish an empirical understanding of the experiences of Black women in engineering. Our work is guided by the following research questions: (1) Why do Black women in engineering seek out campus spaces specifically designated for them? What unique structural issues necessitate such spaces? (2) For what purposes do Black women in engineering use those distinct spaces? (3) How are Black women in engineering responding to the challenges and structural conditions?</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design/Method</h3>\u0000 \u0000 <p>We use Black placemaking theory to guide our qualitative in-depth interviews of 45 purposefully selected students. The data were analyzed via theory-informed themes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Major structural conditions necessitate specific Black spaces, including the absence of physical places, failure to understand intersectionality, failure to respond to safety, and cultural stereotypes about Black women in engineering. In response to such barriers, Black women in engineering engage in multiple placemaking activities, thereby seeking out safe and responsive spaces and transforming existing ones to serve multiple functions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Whatever difficulties Black women face in academia, they are intensified in engineering. To confront such barriers, Black women engage with Black placemaking, creatively transforming existing places or co-creating distinct places for themselves. These actions can inform how engineering departments support spaces for minoritized student development.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50124898","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}
Shruti Misra, Neha Kardam, Jennifer VanAntwerp, Denise Wilson
Background
Belonging is a fundamental human motivation associated with a wide range of positive psychological, educational, social, and job outcomes. Frequent and predominantly conflict-free interactions within a stable, relational framework of caring are required to facilitate belonging.
Purpose
The goal of this study was to understand if and how emergency remote teaching (ERT) used during the COVID-19 pandemic changed the ways in which instructional support and interactions were linked to belonging among engineering students.
Methodology/Approach
This study used survey data from a cross-sectional dataset at a single large institution comprised of sophomore to senior-level students (n = 1485) enrolled in engineering courses between 2016 and 2021. Hierarchical linear modeling (HLM) was used to study relationships among instructional support, instructor interactions, and belonging.
Findings/Conclusions
HLM models of ERT and traditional learning differed dramatically. In traditional classroom learning, race, interactions with faculty and teaching assistants (TAs), and instructional support were important factors in belonging. In ERT, certain motivations to study engineering (altruism, desire to build things) had nuanced associations with belonging, while race and interactions with faculty and TAs became largely irrelevant. Most concerning, faculty interactions in traditional learning were negatively associated with belonging.
Implications
Rather than returning to pre-pandemic traditional learning, a hybrid model that offers a more level playing field for marginalized students to find belonging in the classroom is recommended. In developing such models, faculty must take special care to avoid having a potentially negative impact on student belonging.
{"title":"How did the landscape of student belonging shift during COVID-19?","authors":"Shruti Misra, Neha Kardam, Jennifer VanAntwerp, Denise Wilson","doi":"10.1002/jee.20542","DOIUrl":"https://doi.org/10.1002/jee.20542","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Belonging is a fundamental human motivation associated with a wide range of positive psychological, educational, social, and job outcomes. Frequent and predominantly conflict-free interactions within a stable, relational framework of caring are required to facilitate belonging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The goal of this study was to understand if and how emergency remote teaching (ERT) used during the COVID-19 pandemic changed the ways in which instructional support and interactions were linked to belonging among engineering students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methodology/Approach</h3>\u0000 \u0000 <p>This study used survey data from a cross-sectional dataset at a single large institution comprised of sophomore to senior-level students (<i>n</i> = 1485) enrolled in engineering courses between 2016 and 2021. Hierarchical linear modeling (HLM) was used to study relationships among instructional support, instructor interactions, and belonging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings/Conclusions</h3>\u0000 \u0000 <p>HLM models of ERT and traditional learning differed dramatically. In traditional classroom learning, race, interactions with faculty and teaching assistants (TAs), and instructional support were important factors in belonging. In ERT, certain motivations to study engineering (altruism, desire to build things) had nuanced associations with belonging, while race and interactions with faculty and TAs became largely irrelevant. Most concerning, faculty interactions in traditional learning were negatively associated with belonging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Rather than returning to pre-pandemic traditional learning, a hybrid model that offers a more level playing field for marginalized students to find belonging in the classroom is recommended. In developing such models, faculty must take special care to avoid having a potentially negative impact on student belonging.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50120246","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}
Walter C. Lee, Janice L. Hall, Malini Josiam, Crystal M. Pee
Background
It is well known that earning a bachelor's degree in engineering is a demanding task, but ripe with opportunity. For students from historically excluded demographic groups, this task is exacerbated by oppressive circumstances. Although considerable research has documented how student outcomes differ across demographic groups, much less is known about the dynamic processes that marginalize some students.
Purpose
The purpose of this article is to propose a conceptual model of student navigation in the context of undergraduate engineering programs. Our goal is to illustrate how localized, structural features unjustly shape the demands and opportunities encountered by students and influence how they respond.
Scope/Method
We developed our model using an iterative, four-stage process. This process included (1) clarifying the purpose of the development process; (2) identifying concepts and insights from prior research; (3) synthesizing the concepts and insights into propositions; and (4) visualizing the suspected relationships between the salient constructs in the propositions.
Results
Our model focuses on the dynamic interactions between the characteristics of students, the embedded contexts in which they are situated, and the support infrastructure of their learning environment.
Conclusion
The resulting model illustrates the influence of structural features on how students a) respond to demands and opportunities and b) navigate obstacles present in the learning environment. Although its focus is on marginalized students in undergraduate engineering programs, the model may be applicable to STEM higher education more broadly.
{"title":"(Un)equal demands and opportunities: Conceptualizing student navigation in undergraduate engineering programs","authors":"Walter C. Lee, Janice L. Hall, Malini Josiam, Crystal M. Pee","doi":"10.1002/jee.20543","DOIUrl":"https://doi.org/10.1002/jee.20543","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>It is well known that earning a bachelor's degree in engineering is a demanding task, but ripe with opportunity. For students from historically excluded demographic groups, this task is exacerbated by oppressive circumstances. Although considerable research has documented how student outcomes differ across demographic groups, much less is known about the dynamic processes that marginalize some students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this article is to propose a conceptual model of student navigation in the context of undergraduate engineering programs. Our goal is to illustrate how localized, structural features unjustly shape the demands and opportunities encountered by students and influence how they respond.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Scope/Method</h3>\u0000 \u0000 <p>We developed our model using an iterative, four-stage process. This process included (1) <i>clarifying</i> the purpose of the development process; (2) <i>identifying</i> concepts and insights from prior research; (3) <i>synthesizing</i> the concepts and insights into propositions; and (4) <i>visualizing</i> the suspected relationships between the salient constructs in the propositions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our model focuses on the dynamic interactions between the characteristics of students, the embedded contexts in which they are situated, and the support infrastructure of their learning environment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The resulting model illustrates the influence of structural features on how students a) respond to demands and opportunities and b) navigate obstacles present in the learning environment. Although its focus is on marginalized students in undergraduate engineering programs, the model may be applicable to STEM higher education more broadly.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jee.20543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50145697","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":"Honoring outstanding publications and reviewing","authors":"David B. Knight, Joyce B. Main","doi":"10.1002/jee.20540","DOIUrl":"https://doi.org/10.1002/jee.20540","url":null,"abstract":"","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119463","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}
ChatGPT has revolutionized conversations around writing since its release in November 2022. Faculty wonder how artificial intelligence (AI) such as ChatGPT will revolutionize higher education, where writing is a key competency and where our careers are built on our ability to productively publish. Perhaps you are intrigued, distressed, or horrified by AI; perhaps you are worried about how engineering writing should now be taught; or perhaps you want to arm yourself with messaging for your students when they ask why they even have to learn to write. We have no idea how AI will replace or modify the ecosystem of higher education and knowledge creation, or how ChatGPT will be embedded in the disciplinary norms of the future; some of those ideas are described in the guest editorials by Johri et al. and Menekse et al. in this issue. Many faculty may wonder whether the ability to self-generate text will go the way of the slide rule—becoming a quaint relic of the past. In this guest editorial, we conceptualize the “teaching of engineering writing” as the activities that happen both in undergraduate and graduate classrooms and informally in research relationships—wherever students learn to write authentically for disciplinary audiences. Historically, a reason for teaching engineering writing is to prepare our future engineering workforce to communicate their ideas with each other, to users, and to the public. Most faculty hope that our students would pursue meaningful and high-impact positions in industries that are at the forefront of technology. If our undergraduate and graduate students are to work in transformative areas, we need to arm them with the ability to communicate the value of novel ideas in the face of dominant narratives and pre-existing knowledge. Further, we find it difficult to believe that industries with high profit potential, technological advancement, or secure information will encourage the upload of queries or protected information into online AI tools. This guest editorial is framed around two propositions regarding why we still need to teach engineering writing: First, to teach students to write is to teach them to think; and second, AI is a tool and not a replacement for teaching writing.
{"title":"We still need to teach engineers to write in the era of ChatGPT","authors":"Catherine G. P. Berdanier, Michael Alley","doi":"10.1002/jee.20541","DOIUrl":"https://doi.org/10.1002/jee.20541","url":null,"abstract":"ChatGPT has revolutionized conversations around writing since its release in November 2022. Faculty wonder how artificial intelligence (AI) such as ChatGPT will revolutionize higher education, where writing is a key competency and where our careers are built on our ability to productively publish. Perhaps you are intrigued, distressed, or horrified by AI; perhaps you are worried about how engineering writing should now be taught; or perhaps you want to arm yourself with messaging for your students when they ask why they even have to learn to write. We have no idea how AI will replace or modify the ecosystem of higher education and knowledge creation, or how ChatGPT will be embedded in the disciplinary norms of the future; some of those ideas are described in the guest editorials by Johri et al. and Menekse et al. in this issue. Many faculty may wonder whether the ability to self-generate text will go the way of the slide rule—becoming a quaint relic of the past. In this guest editorial, we conceptualize the “teaching of engineering writing” as the activities that happen both in undergraduate and graduate classrooms and informally in research relationships—wherever students learn to write authentically for disciplinary audiences. Historically, a reason for teaching engineering writing is to prepare our future engineering workforce to communicate their ideas with each other, to users, and to the public. Most faculty hope that our students would pursue meaningful and high-impact positions in industries that are at the forefront of technology. If our undergraduate and graduate students are to work in transformative areas, we need to arm them with the ability to communicate the value of novel ideas in the face of dominant narratives and pre-existing knowledge. Further, we find it difficult to believe that industries with high profit potential, technological advancement, or secure information will encourage the upload of queries or protected information into online AI tools. This guest editorial is framed around two propositions regarding why we still need to teach engineering writing: First, to teach students to write is to teach them to think; and second, AI is a tool and not a replacement for teaching writing.","PeriodicalId":50206,"journal":{"name":"Journal of Engineering Education","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50154238","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}
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).
{"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":null,"pages":null},"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}
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.
Purpose
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.
Design/Method
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.
Results
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.
Conclusion
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.
{"title":"Compassion and engineering students' moral reasoning: The emotional experience of engineering ethics cases","authors":"Nihat Kotluk, 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":null,"pages":null},"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}
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