three themes related to advisor-advisee communication: Mutual Trust, ClearExpectations, and Delivery of Feedback.Mutual TrustWhen asked if they would share information about their neurodiversity-related experiences,strengths, and challenges with their advisor, most participants expressed some hesitation aboutdoing so, suggesting that students may not have the necessary trust in their advisor-adviseerelationship to facilitate these types of discussions. Wendy, who later on in her programdeveloped open communication with her advisor about neurodiversity, reflected on her earlyperception that she was not safe discussing her experiences with ADHD, saying: I think it would be something that might be helpful to share with my advisor
personal life. Additionally, the experiential nature of PBL allows students toencounter challenges, problems, or conflicts like those they may face in the corporate world, allwithin the secure environment of the classroom. This experiential learning model enables themto solidify knowledge through real-world problem-solving. This sentiment is reflected in thestatement from interviewee 1: “The student connects the content given with a real problem that can be encountered in everyday life, which helps in the construction and retention of knowledge.” [interviewee 1] Also, in the statement of interviewee 5, there is: “The student himself will identify
Paper ID #42380The Effect of Ego Network Structure on Self-efficacy in Engineering StudentsDavid Myers, Rowan UniversityMatthew Currey, Rowan UniversityLuciano Miles Miletta, Rowan UniversityDarby Rose Riley, Rowan University Darby Riley is a doctoral student of engineering education at Rowan University. She has a special interest in issues of diversity and inclusion, especially as they relate to disability and accessibility of education. Her current research is focused on the adoption of pedagogy innovations by instructors, specifically the use of reflections and application of the entrepreneurial mindset. Her previous
reinforced skills including experimental design, developing experimental protocols,analyzing data, optimizing a process, and making decisions based on data on a 5-point scale fromstrongly agree (4) to strongly disagree (0).Qualitative Data AnalysisTo better understand the impact of the experiential learning activities, several free responsequestions were included in the surveys. In the survey after each simulated industry experience,students were asked to briefly reflect on the activity by sharing things like what they learnedfrom the activity, how this activity challenged them to think like an engineer in industry, or whatcould be improved about the activity. In addition, students were asked to identify the mainchallenges in the biopharmaceutical
. It represents a behavioral aspect of well-being and has beenrecognized as a significant predictor of various learning behaviors and achievement outcomes[11, 12]. According to Renshaw and Bolognino (2016) [6], academic efficacy encompasseselements of both cognitive and behavioral well-being. However, their analysis suggests that itpredominantly reflects behavioral well-being rather than cognitive well-being. This implies thatacademic efficacy is more closely associated with the persistent pursuit of goals anddetermination rather than solely cognitive abilities or beliefs about one's capabilities.3. METHODOLOGY3.1 Methods Both quantitative and qualitative data were collected concurrently for the concurrentmixed-methods study as
, M.S. Takriff, S.R.S. Abdullah, “Comparative study between open ended laboratory and traditional laboratory”, IEEE Global Engineering Education Conference (EDUCON), 2011.[18] K. Issen, “Open-Ended Design Problems”, Reflection in Engineering Education Workshop at University of Washington, 2017.[19] K.S. Cheruvelil, A.D. Palma-Dow, and K.A. Smith, “Strategies to promote effective student research teams in undergraduate biology labs”, The American Biology Teacher, vol. 82, no 1, pp. 18-27. https://doi.org/10.1525/abt.2020.82.1.18, 2020.[20] A.R. Emke, A.C. Butler, and D.P. Larsen, “Effects of team-based learning on short-term and long-term retention of factual knowledge”, Medical Teacher, vol. 38, pp. 306-311, 2016.[21] R. Ubell
influenceparticipants' responses. Third, the study included a mix of closed-ended and open-endedquestions, allowing participants to express their thoughts and experiences in their own words.However, despite these efforts, the possibility of response bias cannot be entirely eliminated,and the results should be interpreted with this limitation in mind.Finally, the rapidly evolving nature of AI technology presents another challenge. The study'sfindings are reflective of the current state of AI and may not remain relevant as newadvancements and shifts in the industry emerge.6.2 Future WorkTo build upon the findings of this study and address its limitations, future research couldexpand the scope to include a more diverse range of participants from various
experiences.Dr. Jennifer L. Cole, Northwestern University Jennifer L. Cole is the Assistant Chair in Chemical and Biological Engineering in the Robert R. McCormick School of Engineering and the Director of the Northwestern Center for Engineering Education Research at Northwestern University.Dr. Kevin D. Dahm, Rowan University Kevin Dahm is a Professor and Undergraduate Program Chair of Chemical Engineering at Rowan University. He earned his BS from Worcester Polytechnic Institute (92) and his PhD from Massachusetts Institute of Technology (98). He has published two books, ”Fundamentals of Chemical Engineering Thermodynamics” with Donald Visco and ”Interpreting Diffuse Reflectance and Transmittance” with his father Donald
all of the course’s challengeproblems). The grades are indicative of the correctness of the calculated and inferred solution as well as thedescription of the process to reach the solution. Though the student grade is more of a representation of thecognitive domain, it is a good measure of the student engagement level and, when compared to grades inother assignments, reflects the impact of the gamified problem on their learning.In order to separate the assessment of the data (including coding of the reports) from the evaluation ofgrades, the authors split these responsibilities. MG, who was the instructor in the course, assessed all reportswith the rubric. RVG, who did not meet the students and therefore held no biases towards any of them
student-to-instructor interaction has a significantimpact on students’ learning and engagement [31]. Similarly, studies also show that student-to-instructor interactions help the student create a sense of belongingness in the online courses [32].Limitations, Implications, and Future WorkSimilar to other research studies, this study also comes with limitations. The sample recruited forthis study includes participants from one university at undergraduate level and is not representativeof the broader online engineering programs/community. Additionally, the undergraduate studentsrecruited were from only three engineering majors: information technology, software engineering,and graphic information technology, which does not reflect the experiences of
performers. These entities are abstractmission participants who can perform activities in the scenario. Fig. 4 focuses on motivation forthe articulated mission (shown in the diagram) from research lab directors. Given this mission orenterprise vision, drivers are used to define factors or rationale that drive the articulated mission.Each driver can then be mapped to one or more challenges which reflect issues that need to beresolved to address the driver. This dependency is expressed using the PresentedBy relationship.The challenges identified are used to motivate a set of opportunities expressed by theMotivatedBy dependency. These opportunities can be further traced to the capabilities of the SoIto achieve the proposed mission
other available courses listed under course sets that interest students provides theopportunity to further customize the degree plan.It is worth noting that changing a major can be a normal part of the college experience, as itmay reflect a student’s growth, self-discovery, and a deeper understanding of their academic andprofessional desires. To demonstrate the efficacy of our algorithm that works in this scenario,another example is considered for creating a transfer plan from the Associate of Arts program atPima Community College to the Biochemistry program at the University of Arizona. The structureof the degree requirement tree is provided in Figure 5, and the descriptions of the requirements arelisted in Table 4. The two-year to four-year
cold-water flow rate on hot-water outlet temperature.These results lead to a significant improvement (p-value = 0.034) for Q6R with a moderate effectsize (ES = 0.54). With improvement in all questions, overall, the DLM implementation wasbeneficial for the students as there is > 10% improvement with a medium effect size.4. Motivational OutcomeIn addition to pre- and post-test, we also conducted motivational survey. Participant consists of 75students from 3 different universities in the United States. The participant responses are shown inFig. 6 from a survey assessing the Shell & Tube Heat Exchanger DLM features listed in table 2.The plot reflects a predominantly positive evaluation of the modules' features. Notably, featuresfacilitating
Fig. 11. Additionally,the 6V to 4V transition was not smooth, unlike the test case with 20kHz PWM and 1kHz sampling frequency. Similar resultsare reflected in the Simulink simulation from Fig. 12. This phenomenon was anticipated from the duty cycle resolution issuethat 80kHz PWM frequency creates. OCR1A = 0 ∼ 99 1 ResolutionDutyCycle = = 1% (10) (99 − 0) + 1 Thus, the duty cycle cannot be expressed in a decimal form with 1% duty cycle
tasks, etc.). This349 is reflected in high ra ngs both pre- and post- Team Challenge for Criterion “C”. The most significant350 change between pre- and post- self-assessment was observed for Criterion “D” (pre- and post-challenge351 averages of 3.1 and 4, respec vely). Anecdotal observa ons and student feedback suggest that this352 learning approach is novel to the majority of students, and they feel most capable of addressing these353 challenges once they have been exposed to them and ac vely engaged in the process.354 Finally, before introducing the Team Challenges to students, significant me is devoted to introducing355 engineering problem-solving, which involves applying STEM concepts to prac cal applica ons. However,356
engaged experiences that involve guided practice,opportunities for collaboration, and reflection on applying course content through real-world application [13,14, 15]. Active learning allows for engaging with higher-orderthinking tasks, such as analyzing, synthesizing, and evaluating applied course content[15]. This practice of high-order thinking tasks occurs in formal and non-formal STEMeducation environments and can incorporate self-regulated learning, self-monitoring,and self-evaluation [13,14, 15, 16, 17]. Professional organizations value active learning experiences in engineeringdegree programs, as evidenced by ABET accreditation criteria and industryrepresentatives Prados, Peterson, and Luttuca, 2005 statements of there being a
. It is noted that students will also have background and foundational literature they will discuss in the intro that will not show up in the Matrix. c. Students will use the research question skills learned in class to identify metadata they will collect from the studied papers and compare across the matrix. d. Finally, students will write a reflection on the process that includes their search strategies, experiences, and next steps. e. Timeliness is important. After a week, you will have both half-credit for the assignment and less time to develop your paper, so please chat with me early if you’re falling behind on this.7. Lit Review: Paper a. Students must follow
who changemajors, and students who are veterans (e.g., [7], [27]). Other work has indicated the importanceof factors such as motivation and belongingness [5], [28]. While those factors are not connectedto a students’ academic record, they are an important reminder of what academic records can andcannot reflect about students. MIDFIELD leaders point to the value of qualitative research tofurther explore the quantitative findings [9]. Similarly, this paper represents the early quantitativestrand of a larger mixed-method project seeking to identify opportunities to support ECEstudents.The past few years have seen the engineering education research community grapple with thepotential contributions of educational data mining students’ academic
Mean St. Dev Mean St. Dev Non-Traditionally Underrepresented Students 3.510 0.426 29.30 3.797 Traditionally Underrepresented Students 3.236** 0.717 28.20 5.448 PMP-Eligible Students 3.161** 0.813 28.02 5.255 PMP Participants 3.343 0.546 28.46 5.782Significance reflects results of an independent samples t-test between non-TU students and TU studentsubpopulations. * p ≤ 0.05, ** p < .01, *** p < .005.Since RQ2 seeks to understand the relationship between participation in the PMP and studentacademic
exam problems. Reflecting on thebike frame project, the majority of students perceived the experience improved theirunderstanding of structural analysis and provided an opportunity to apply statics to real-worldscenarios. Taken together, these results suggest that the Wooden Bike Frame Challenge improvesstudent knowledge of advanced statics concepts, specifically structural analysis, and connectsthese concepts to real-world design scenarios.While it is not the first statics based PBL exercise, the Wooden Bike Frame Challenge is avaluable addition to the engineering education literature. Prior studies have presented PBLexercises that have students construct: (1) suspension systems modeling 2D and 3D particleequilibrium scenarios [1, 21]; (2
” or “when doing an experiment, I try to understand how the experimentalsetup works” are compared to expert responses. The data in [2] represent a wide range ofinstitutions and show that, instead of laboratories improving epistemic agreement betweenstudents and experts, a small decrease in agreement is observed over the course of anintroductory physics lab. This result is similar to another study which found that some laboratoryexperiences in basic electric circuits may deteriorate students’ epistemic views, in particular theirviews about coherence of mathematical models and the physical world [3].The literature from chemistry includes reflections on the purposes of educational laboratories.While chemistry programs in general devote more time
defined as a limit of Riemann sums. White down the limit form and then decide 𝑏on the units of ∫𝑎 𝑓(𝑥)𝑑𝑥 .Fancier version: assume g(s,t) is a function of two variables, where s is measured in v units and tis measured in w units and g is measured in o units (for output) .Write down the limit and difference quotient that is used to find ∂g/∂s.What does that make the units of ∂g/∂s ? 𝑏 𝑑What would be the units for the double integral ∫𝑎 ∫𝑐 𝑔(𝑠, 𝑡)𝑑𝑠 𝑑𝑡 ?Reflection: 1. Did you remember how to obtain units on derivatives and integrals? (Please elaborate) 2. Does this exercise refresh your understanding of calculating units from Calculus I or Linear Algebra
implementing practicalmeasures to support students are not separate initiatives, but two sides of the same coin. Thisinsight urges research studies to consider a panoramic view of the interconnectedness of identitydevelopment and academic performance, thereby presenting a cohesive tapestry of theseindividual threads [24]. The following research questions are offered by this work to foster morecomprehensive investigations in this field: • To what extent do interventions (academic, social, personal, professional, etc.) impact the academic performance and persistence of ET transfer students, and • how do these interventions interact with the shaping of their engineering identity during their first year of transfer?Reflecting back to the heart of
opportunities to develop them through hands-on tasks and mentorshipfrom their upperclassmen peers. We intentionally mix under and upper-class students in teams tofoster a collaborative learning environment.The advanced-level course builds upon the foundational concepts of the previous course andincludes additional learning objectives. As students progress to this level, we anticipate a moresignificant engagement level, reflected in the increased credit requirement from one to two.Students at this level also have the opportunity to take on a leadership role, either as a projectleader or team leader. Those who choose to do so must take the course for three credits.The instructors assign student leadership roles based on their interests, abilities, and
learning, 5. providing mentorship, not supervising, as students choose objectives, methods, and testing and assessment process of their project, 6. enabling students to reflect on what they learned from their projects and how these projects relate to the real world through survey and open discussions, 7. having consistent follow-up through scaffolded PBL assignments, as well as providing formative feedback for improvement, and 8. making project prepared and presented for external audience to motivate student accomplishment [16].Although PBL activities have been employed in courses to help students quickly learn newconcepts as well as prepare students with skills such as leadership, team building, ethical
criterion validity is whether academic motivation predicts GPA (whereacademic motivation FA results are compared to GPA). Content validity is a question of howrepresentative the individual items in an instrument reflect the whole construct of interest. Forexample, an instrument to assess motivation may include items regarding interest, success,and usefulness (i.e., motivation would likely increase if one is interested in, experiencessuccess with, and finds the learning helpful material). Suppose the instrument also containeditems unlikely to be associated with the construct of interest, such as including items inquiringabout socioeconomic status and intelligence on an instrument designed to assess motivation.In that case, we might find that those
States. In total, we will invite 500 studentsto complete the survey from various colleges and universities. By extending the invitation toparticipate across institutions of varying sizes, we are effectively strengthening the breadth anddepth of our findings.The 28-question survey seeks to understand the decision-making process that led students topursue the engineering technology program of study and their intended plans for the future uponcompletion of the degree. Questions also ask students to consider their degree of preparedness toenter the engineering technology program and their confidence that they will ultimately succeedin completing the degree. Additional questions ask students to reflect on how they handleacademic challenges, and to
defining the problem, while employing a systematicapproach with diverse tactics and heuristics to navigate complexities. Constantly monitoring andreflecting on their progress, they prioritize accuracy over speed, valuing the right solution over ahasty one. They excel in jotting down ideas and creating visual aids like charts and figures,ensuring an organized problem-solving journey. Flexibility is another hallmark, allowing them toconsider various perspectives and keep options open for innovative solutions. Their systematic,reflective, and adaptable approach makes them invaluable assets in any problem-solving endeavor[28]. Which means every engineer has or develops that skill. In fact, during their studies it isfundamental for the students
playing field for the production of texts in standard English. The power of GenAIas a writing tool is based on its large training data set; however, that apparent diversity belies theprimacy of language practices from younger, white, more affluent users in the training data(Bender et al. 2022). GenAI programs like ChatGPT utilize machine learning, organizinglanguage into tokens, representing units of meaning, often phrases, each assigned vectors tocharacterize relationships between tokens. Trained on vast text data, initially supervised byhumans, then refined through a reward model, these systems predict the likelihood of tokens in atext stream. Despite their capabilities, they predominantly reflect white mainstream AmericanEnglish, with limited
growth.The collaborations with the Engineering Wellness Coordinators reflect the value of saying yes tonon-traditional opportunities that arise. Liaison librarian work involves significant sustainedefforts to incrementally build in-roads with academic departments, with varying degrees ofsuccess dependent on the receptiveness and capacity of campus colleagues. When traditionalclassroom settings for Library instruction are not readily available, it can be very rewarding toexplore lateral pathways through alternative networks. It can be difficult to establish and build arelationship with instructors who may be reluctant towards integrating information literacy skillsinto their curriculum. In contrast, the campus colleagues working to support those