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.
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.
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.
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.
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.
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.
The study examined factors that promoted or detracted from SB in engineering in remote courses and ways in which identity related to SB.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 positive emotion, engagement, relationships, meaning, accomplishment, and vitality.
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.
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.
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