participants’ability to recall detailed information about their interaction and resource usage after the fact. Inaddition, although survey questions asked participants to identify time spent interacting witheach peer in their network, few students gave such detailed descriptions. Lack of detailedresponses limited development of the peer interaction networks. For those participants whochose to provide only their names on surveys (presumably for the purposes of receiving extracredit), their responses were removed from data. FINDINGS AND DISCUSSIONSixty-six of 118 (56%) students enrolled in the course participated in all surveys. Participantdemographics are shown in Table 1. Participant demographics reflect the larger
shows that an engineering degree prepares students for a range of careers. However,engineering undergraduate training has often focused on equipping students with the knowledge,abilities, and attitudes that will make them successful as engineers in industry rather than the broadpossibilities that an engineering degree offers. Reflecting this focus, a common topic inengineering education literature discusses ways to bridge the gap between industry andundergraduate training [1]. However, the qualities students develop—such as critical thinking,problem solving, and teamwork—are also valued by employers in general. Additionally, researchstudies in engineering education on students’ post-graduation pathways often frame students whodo not enter
“multidisciplinary perspective” to systems thinking – one that equips students not only toaddress technical problems but to communicate the value of ethical, persuasive decision-makingin the workplace [1]. Yet, as the Boeing report suggests, “major opportunities for reform existbut have yet to be exploited” [1]. Among these curricular reforms yet to be exploited is the move“from the stage of dumping ‘expert-recommended’ communication strategies to the stage oftailoring communication strategies to achieve clarity of understanding with different audiences”[1]. This call for curricular reform is also reflected in the most recent update to the AccreditationBoard for Engineering and Technology, Inc. (ABET) outcomes for engineering programs, whichrequires that
to which I have no idea what I'm doing like 95% of the time.”—Amy (fifth year graduate student)Attitudes towards ExpectationsTo add insight to this data, we also characterized the interview excerpts that discussed expectationsunder one of four categories, deemed “expectation attitudes:” Correct and Positive, Correct andNegative, Incorrect and Positive, and Incorrect and Negative. From the interviews, we determinedwhether their expectations of graduate school were proven correct or incorrect. It is important tonote that these labels do not define what is “right” about the expectation (e.g., the expectation ofgraduate school being coursework heavy, for example, which is generally not reflective of doctoralengineering culture, was not
the semester when there is more time and TAs are still stressing theimportance of collaboration. However, there were discussions that this is a more expert skill,because TAs who are new may not have the time or capacity to keep track of who is doing welland also reflect on it at the end of class.Figure 4: Guidelines of what to say and how to interact during whole class interventions.Figure 5: Guidelines of how to structure the end of class wrap up to emphasize collaboration. During both workshops there were many discussions about how these guidelines shouldbe shared with other TAs. Both TAs are graduating and will no longer be teaching these courses.A final decision was made to provide new TAs with a cheat sheet of guidelines for
, can reflect their self-efficacy and may correlate to performance/competence with respect to their engineering identity.Attribution theory describes student perception of the cause of an outcome [4]. Attributions inacademia may include effort, knowledge, or ability and are strongly connected to emotions [4].Emotions generally influence daily choices. The way an individual reacts to the outcome of thesechoices may influence future behaviors. However, it is the student’s perception of attributionswhich emotionally influence motivation. Two students may attribute an outcome to the samecause, but view the characteristics of the cause very differently. We are particularly interested inhow these attributions may vary with strength of engineering
, formal instruction on teamwork may be limited.As part of a curriculum improvement process within the Mechanical Engineering department atRose-Hulman Institute of Technology, we are working to coordinate “threads” that cut acrosscourses in the curriculum, e.g., student teaming, technical communication, business acumen,ethics, and ill-structured problems. Each active thread is championed by a small facultycommittee, charged with prompting and analyzing department reflections, moderating anddocumenting departmental discussions of results, and collecting and sharing evidence-basedpractices relevant to the thread. Each thread is following coordinated change processes acrossdimensions presented by Borrego and Henderson [2] in order to have a greater
. Then for each factor participants will be asked how theyhave been influenced by their experiences in the ECE department. At this point, participants mayspeak on recent diversity and inclusion initiatives in the department, including the tip sheet anddiversity and inclusion design sessions put on by our larger NSF-funded study. Finally,participants will be asked how each factor could be improved for themselves or other ECEfaculty.It is possible that reflection during the interview itself will have some effect on participants’intention toward inclusive teaching. To observe this effect, participants will be asked to completean open-ended electronic survey question once before and once after the interview. Before theinterview, we will ask directly
. call uncertainty. More generally,ambiguity by the students over the desired outcome reflects the ill-structuredness of theproblems.However, students also perceived a number of contextual factors as contributing to ambiguity. Ageneral lack of knowledge on their part was seen as creating ambiguity as to how to solve theproblem. From this perspective, ambiguity would decrease with experience, as noted by Dave. Ifgeneral knowledge is an aspect of ambiguity, then the differences between novices and expertsnoted in the literature could be taken as indications of more or less ambiguity in the problem-solving process. Another contextual factor was group problem-solving. The dynamics of groupinteractions can lead to ambiguity, when group members are
here and you just like push it until it forms to the shape of whatever you're molding. Um so the like thicker ones would slip out of the seal. So they weren't like sealing fully, they weren't making this like cone shape. Um and then the fitter- thinner ones were ripping before it got there. Um, so the polypropylene was actually the only one that created the shape that I was looking for.”Category 2. Practical knowledge. Below we describe three aspects of the ways students gainedpractical knowledge about equipment and experimentation.Engineering experimentation. Coming into the project, the students were unfamiliar withdesigning their own experiments. For example, in his interview Noah reflected on the challengeof
]). TABLE I. LITERATURE DEFINITIONS OF MENTORING Definition Source “a collaborative process in which mentees and mentors take part in reciprocal and dynamic activities [7, p. 35] such as planning, acting, reflecting, questioning, and problem-solving” “a form of teaching where faculty members provide advice, guidance, and counsel in the areas of academic, career, and personal (psycho-social) development, which can occur either individually or [11, p. 48] in small groups” “a dyadic, hierarchical
. c American Society for Engineering Education, 2020 Work in Progress: Impacting Students from Economically Disadvantaged Groups in an Engineering Career PathwayAbstractThis work in progress describes the overall initiative in the program for engineering access,retention, and low-income-student success. It discusses the program structure, implementationof activities, outcomes for the first of five years of project, and reflections on our initial findings.IntroductionThe Program for Engineering Access, Retention, and LIATS Success (PEARLS) was establishedwith the objective of increasing success statistics of low-income, academically talented students(LIATS) in the College of Engineering (CoE) of the University of Puerto
education, including how to support engineering students in reflecting on experience, how to help engineering educators make effective teach- ing decisions, and the application of ideas from complexity science to the challenges of engineering education. ©American Society for Engineering Education, 2020 A Look Into the Lived Experiences of Incorporating Inclusive Teaching Practices in Engineering Education AbstractThis research paper contributes to the field's understanding on how to support educators increating a diverse and inclusive engineering education environment. Even with manyconversations around diversity and inclusion, recruitment
helpful in refining this specific OEMP assignment and developing generalguidelines for writing OEMPs on any topic. If multiple students are not making reasonable, well-justified assumptions, this suggests that the problem should be redesigned to provide morescaffolding that helps students make more realistic assumptions or more explicitly prompts themto write out their justifications. Second, having students metacognitively reflect on their ownassumptions is an important factor in their development of engineering judgment. Byunderstanding what assumptions students are making and the impact these have on design,instructors can highlight productive beginnings of engineering judgment and help studentsunderstand when they have made assumptions that
identitiesrelated to a specific subfield within their major (e.g. “I see myself as a mechatronics person, butnot a fluids person”) and therefore we expect to find differences in responses between coursecontexts for the same student.We measured motivation and attitudes towards learning in a cohort of students simultaneouslyenrolled in three upper-division mechanical engineering courses. We adapted portions of theMotivated Strategies for Learning Questionnaire (MSLQ) into two surveys: an online surveyasking students to reflect on all of their mechanical engineering courses (“cohort context”), and apaper survey delivered during class in each of the three courses (“course context”). Thecohort-context survey included questions related to intrinsic motivation
learning technology,students experience a tailored learning experience, specific to their learning path towards theirmastery of the given topic. Expanded research in the engineering education context can lead tomore closely aligning instructors’ teaching styles and students’ learning styles.IntroductionIt is well established that there is often conflict between the instructor’s teaching style andstudents’ learner styles in the engineering classroom [1]. The use of adaptive learning as ateaching style facilitates several learning styles, complementary to the traditional lecture style.Learning styles including sensory, intuitive, visual, auditory, inductive, deductive, active,reflective, sequential, and global [1], can all be incorporated into
emotions such as sympathy,empathy, and sensitivity, and views persons as relational and interdependent.The study of care has permeated other areas of knowledge, including education. Noddings [2]described the attributes of the teacher as a carer. In such a role, she proposes teachers should beattentive to the needs of students, responding always in such a way that the caring relation ismaintained. She emphasizes additional attributes of caring teachers: the ability to listen, theability to empathize with the student, and the ability to reflect upon the actions to be taken in caseof need. Finally, caring teachers should also promote a caring environment, encouraging theirstudents to read and respond to their peer’s feelings. Gholami and Tirri [3
engineering, who are particularlyvulnerable to dropping out of engineering careers.Career commitment reflects students’ intention to work in the field of engineering. Measures ofstudents’ self-reported commitment to career have primarily been used by others as outcomevariables [10], [11]. In our analysis, we model the possibility that commitment to an engineeringcareer may serve as a motivator to obtain the knowledge and credential often necessary forstudents to obtain their occupational goals. Because these are early career students, we expectthem to have relatively low commitment to the field of engineering in this baseline data, butmodeling their expressions of commitment throughout their undergraduate education may helpus better understand their
]. According to recent studies, the MM-GTresearch approach has become useful to develop and test theory in the fields of education[8], [9]. In this study, we plan to develop theoretical models of difficulty at a course level,following best practices of MM-GT application to provide insights for course curriculumdevelopment and teaching reflection in the field of engineering education.2. Research Design and Current Data CollectionIn this study, we plan to use an exploratory sequential design based on MM-GT to developand test theoretical models in four phases (see Figure 1). This paper presents the results ofthe first phase, which consisted of a grounded theory approach to identify the factorsassociated to what students perceive as easy courses and
, “Students’ agency beliefs involve how students see andthink about STEM as a way to better themselves and the world along with being a critic ofthemselves and science in general [20, p. 939]. The critical thinking perspective is intimately tiedto engineering agency beliefs, where students become “evaluator[s] of STEM as well as becomecritics of themselves and the world around them through self-reflection” [39, p. 13]. In essence,agency beliefs in this framework are based on a spectrum of how students view engineering as away to change their world or the world at large.Most agentic frameworks in engineering education used qualitative research methods. However,Godwin and colleagues [40] and Verdín and Godwin [41] used quantitative measures to
, Innovation, and Hands-on Learning", International Perspectives on Engineering Education, ed. S. Christensen et al.,Springer International Publishing Switzerland, 2015.[7] K. D. Strang, "Improving standardised university exam scores through problem-basedlearning, " International Journal of Management in Education, vol. 8, no. 3, p. 281, 2014.[8] A. G. Pereira, M. Woods, A. P. Olson, S. V. D. Hoogenhof, B. L. Duffy, and R. Englander,"Criterion-Based Assessment in a Norm-Based World, " Academic Medicine, vol. 93, no. 4, pp.560-564, 2018.[9] W. Ray and H. Cole, "EEG alpha activity reflects attentional demands, and beta activityreflects emotional and cognitive processes, " Science, vol. 228, no. 4700, pp. 750-752, Oct. 1985.[10] C. Demanuele, S. J. Broyd
collection of responses and not in terms ofeach individual perception of the system.Responses were grouped into usability and learnability subscales. Ninety-two responses for eachsubscale were coded and used to calculate the mean. Because the statements alternate betweenthe positive and negative, care is taken when calculating the mean [20] for each subscale. Thelatest research suggests that negatively worded items should not be used because they measuredifferent constructs [22]. To address this issue, items from even numbered statements werereverse scored before calculating the mean score. The first subscale reflects responses related tothe participants’ perception of the usability of the method. For this subscale, the mean score wascalculated from
reflect on their full rangeof projects. We conducted one focus group interview with three students from one team in energyengineering and six individual interviews with students from energy engineering, civil engineering,and computer engineering (Table 2). Since this is a work-in-progress, we reported our primaryfindings based on the group interview and six individual interviews. In our next step, for triangulationand enrich data with different aspects of students’ learning experiences in PBL, we planned to conductfocus group interviews firstly, and then invite same students from focus group interviews toparticipant in individual interviews.In the data analysis process, all interviews involved in this study were transcribed and reviewedcarefully
]. Engineering from a 21st century perspective, focuses onfinding solutions aligned with the needs and expectations of clients, while adhering to ethical andsocietal expectations of making the world a better place for others to live in [14]. There isevidence that the structure of some engineering programs may not be conducive to developing amindset aligned with 21st century engineering [15]. Given the potential for makerspace projectsto be aligned with a 21st century philosophy of engineering, there is justification for assessing ifstudents are developing a 21st century engineering mindset.Belongingness and InclusionThrough the use of makerspaces, students may gain a sense of how much they perceive theybelong and are included in situations reflective of
arrive at the answer. 85) Feedback should be aligned with goalsFeedback should be aligned with the purpose of the assignment and its evaluation criteria. Morespecifically, feedback should clarify what good performance is in terms of goals, criteria, andexpected standards.6) Feedback should encourage reflection, self-adjustment, and improvement “[Growth mindset] is about telling the truth about a student’s current achievement and then, together, doing something about it, helping him or her become smarter.”—Carol Dweck [25]Merely providing timely and specific feedback is insufficient: teachers must encourage self-assessment and expect the
Survey of UndergraduateResearch Experience (SURE), the Undergraduate Research Student Self-Assessment(URSSA), and instruments that measure engineering identity and sense of belonging. We alsodeveloped open-ended, qualitative questions that invited self-reflection. These questionscovered topics such as how students define “engineer,” circumstances in which they did anddid not feel like engineers, and how students with research experience would explain thevalue of that experience to potential employers or a graduate admissions committee. In spring2019, a survey of 64 questions was administered to all enrolled students in our engineeringschool, and 28% of students responded. Respondent demographics were representative of theschool’s student
reflect on your understanding of the NSF-funded Engineering ResearchCenter (ERC). Rate your present level of understanding, as well as your level of understandingprior to participating in the ERC for each of the items below.” No items in this section wereshown to be highly correlated with one another (see Appendix A).A two-factor structure emerged through EFA (Table 1): 1) present understanding, and 2) priorunderstanding. Both factors achieved good reliability levels; Cronbach’s alpha of 0.909 forpresent understanding and 0.907 for prior understanding.Table 1. Factor structure and factor loadings for understanding the ERC Item Present Prior
? – Multiple Choice: 0, 0.5, 1.0, or 1.5 letter gradesThe responses to both of these questions reflect very favorably on the PLA program (shown inFigure 3). There is a clear positive bias in the responses with the mean at 5.1, 76% >5, and 90%>4 on a 6-point Likert-like scale. The median response indicates that students, in general, feltPLAs contributed to their success, resulting in a perceived net improvement in studentperformance by at least half a letter grade (79% of responses.) To reiterate, the students indicatethey feel the peer assistants provide a positive impact on their learning. The authors did notattempt to obtain individually identifiable grades. Figure 3: Student responses to Q3 (left) and Q4 (right)An even
0.88, indicating high internal consistency between the items. The U.S faculty membersreported higher self-efficacy related to performing general research tasks than both U.S. graduatestudents and Indian faculty members did. They also reported higher self-efficacy related toperforming qualitative research tasks than Indian faculty members did. There were no differencesin self-efficacy related to performing quantitative research tasks among the three groups.Practically speaking, this instrument has the potential to be helpful for evaluating the efficacy oftrainings and workshops focused on increasing the EERSE of faculty and students. Engineeringeducation researchers can also use this instrument as a tool to self-reflect on their
often a necessity for professors toexplore the space and expose their students to the opportunity for projects that deviate fromstandard pencil to paper design projects that dominate engineering coursework by including thedevelopment of some physical final prototype.ParticipantA recipient of the makerspace grant, Dr. Cook is an assistant professor in the department of civilengineering. Her expertise is in structural engineering and her research interests are the designprocess and testing the behavior of largescale steel structures. Observations of her class reflect akeen interest in students’ growth, empathy for the student experience, and awareness surroundingthe potential pitfalls that accompany the many types of projects engineering