educational and professional opportunities should reflect and support these values. Futureresearch on this topic could include power sharing structures and opportunities within SUCCs, thequantitative impact of an intervention program like DeSIRE on student academic outcomes or teacherretention rates, or community and parent perceptions of SUCCs (especially in conjunction with theservice mission of public universities).ConclusionThe purpose of this study was to understand how a school-university-community collaboration coulduse existing community assets to support the reduction of rural flight, or “brain drain,” by influencingstudent and teacher perceptions of local workforce opportunities. Through meaningful relationshipbuilding between various
6 5 1 0 0 engineering design and STEM Have students participate in hands- 7 5 0 0 0 on activities Engage and empower students in 8 4 0 0 0 enquiry-based learning Students work collaboratively on 7 5 0 0 0 group projects Engage students in open-ended problem solving with student peer 6 6 0 0 0 collaboration. Reflect on my teaching 5 4 3 0 0
leveraging institutional data to support reflective teaching practices. She has degrees in Electrical Engineering (B.S., M.Eng.) from the Ateneo de Davao University in Davao City, Philippines, where she previously held appointments as Assistant Professor and Department Chair for Electrical Engineering. She also previously served as Director for Communications and International Engagement at the Department of Engineering Education at Virginia Tech, Lecturer at the Department of Engineering Education at The Ohio State University, and Assistant Professor at the Department of Integrated Engineering at Minnesota State University, Mankato. She holds a Ph.D. in Engineering Education from Virginia Tech.Carol Geary, Virginia
are struggling tofind a research advisor conceptualize this struggle as a direct reflection on their competence and worth.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant 2130169. Anyopinions, findings, and conclusions or recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of the National Science Foundation.References[1] Council of Graduate Schools, “Ph.D. completion and attrition: Analysis of baseline program data from the Ph.D. completion project,” 2008.[2] R. Sowell, J. Allum, and H. Okahana, “Doctoral Initiative on Minority Attrition and Completion,” Washington, DC, 2015. doi: 10.1145/1401890.1402023.[3] R
, connecting new information to prior knowledge, and refining problem-solving strategies.Additionally, Jones et al. [40] note that teachers often learn alongside their students, especially incollaborative and technology-driven contexts, where roles can shift, allowing students to becomethe teachers and teachers to become the learners. Hence, we decided to adopt this framework toexplore students’ perceptions of their faculty mentors' roles in an undergraduate researchenvironment, to determine if it reflects an engaged learning experience. For this study, we thusfocused on what the students said about their mentors and juxtaposed it against the indicators inthe framework.Table 1: Faculty Role for Engaged Learning (adopted from [40
according to somearticles in the literature, there have been changes in the definition of engineering over the yearsto reflect a simple fact that defining engineering is not as simple as it may look and sound [7].Recently, there are numerous calls to further modify the definition of engineering to be in linewith its continuing and systematic advancements as well as ever-changing societal norms andvalues. For instance, John Anderson in the Bridge: a National Academy of Engineers platformsuggested creating definitions with more “operational” key terms [8]. There are calls to includeother aspects in the definition of engineering like culture and ethnicity amongst other factors asstated in the 2020 virtual ASEE annual Conference [9].Steib records that
than 0.4 were considered poor agreement. These ranges are consistent with current conventions for assessing interrater reliability [31]. Cohen’s Kappa was calculated for each theme in the data, using 2X2 contingency tables that evaluated how well a particular theme identified by the domain expert agreed with the theme assigned by top NLP modelling techniques classification models.ResultsIn our study sample, initial topic modeling revealed the emergence of four topics (also referred toas codes). Table 2 displays the most frequently appearing words linked with each of these fourtopics. Topic 1 reflected students’ desire for greater practice with solving problems associatedwith engineering content including but not
specificknowledge on the project's topic, reflected in increasingly technical descriptions in each of thepresentations. We have taken the metric of the number of articles as an indicator of students' pursuitof new knowledge. In describing the solutions, students included diagrams, concepts, methods,and results in their presentations, which demonstrates their engagement with the articles.Defined RequirementsOne of the most important findings of this study was the analysis of requirements. Only one groupmaintained the number of requirements, indicating that iterative design is necessary to developbetter solutions to problems. In the first iteration, three groups provided more detailedrequirements, either by adding or dividing those initially proposed in the
% DT 0.6017 -14.7% LR 0.5930 -16% NB 0.5709 -19.1%Final ModelThis study employed an artificial neural network with a specific structure to analyze andmodel a dataset (see Fig. 2). The network featured a hidden layer comprising two neurons, achoice-balancing model complexity, and efficiency. The network's target variable was“Dropout,” and all other available dataset variables were used as inputs to predict this target.This configuration allowed for an in-depth exploration of the relationships between” Dropout”and other variables. A key feature of the network was its focus on classification, reflected inits nonlinear
points, and he or she only had an error in the manipulation of the equation priorto finding those points. In graph 9 (Figure 5), all of the points are wrong and the slope is incorrect. However, if wecompare the line in the graph and the correct line for the equation, they are reflections of eachother across the x-axis. Therefore, it may be that this graph resulted from a sign error, slope andintercept are positive when they should be negative.In the case of Graph F (Figure 2), Sam hypothesized about this student’s reasoning in creatingthe graph when he was grading. Sam gave the student 7 points. The other graders only gave thepoints for the correct slope – a feature of the appearance of the graph. Daniel said, “I will onlylook at what the
and within a major [10, 11]. One study showed that acombination of student-advisor interaction, student-faculty interaction, participation inextracurricular activities, and utilizing the library correlated with a higher first to second yearretention rate (fall to fall) among students [12]. While each method is helpful, institutions wouldbe well served in designing academic support opportunities that include the variety ofstakeholders in their students’ social networks at college.In addition to feeling connected to the college community, educational researchers havedemonstrated the efficacy of a positive academic self-concept. Students perceive their academicabilities through self-reflection and comparison to others. This perception, their
researchers to guide their curriculum analysis and redesignefforts. BackgroundWe have referred to the idea of “curricular complexity” loosely so far, but we can be moreprecise by using a framework that is growing in popularity when describing curricular designpatterns. The formal analysis of curricular design patterns can be accomplished using aframework called Curricular Analytics [10]. The adoption of Curricular Analytics reflects aparadigm shift toward a data-driven approach to analyzing curricula and degree requirements.This method quantitatively assesses the "complexity" inherent in a plan of study; at its core,Curricular Analytics captures and models the intricate web of pre- and corequisite
methods, strategies, and their outcomes, allowing institutions to gaugethe overall performance of educators and identify areas for improvement. This process allowseducators to reflect on their teaching practices, adapt to evolving pedagogical trends, andenhance their students' learning experiences. In the existing literature much is known about howteaching evaluations are conducted and their value in helping educators become better at theircraft. However, there remains a gap in our understanding of the theoretical underpinnings of howsupervisors and peer evaluators make decisions about how to rate teaching beyond their ownperceptions of teaching.In this paper, we introduce the theory of rating (ToR) by Robert Wherry as a candidatetheoretical
might be reflected in the underrepresentation of students with disabilities in thescholar community. Between 11% and 15% of U.S. college students identify themselves asstudents with disabilities [7] [8] and about only 4% of these students with disabilities haveenrolled in engineering majors [8]. As of 2015, while the 33% of the U.S. population held atleast a bachelor’s degree, only 14% of the population with disabilities had reached this level ofhigher education [9]. Furthermore, just 1% of students with disabilities have received a PhDdegree in 2017 [10]. These statistics provide a glance of the disadvantaged position that studentswith disabilities hold, as compared to the general population in the U.S. Given the historicallyexclusionary
, University of Oklahoma Haley Taffe is an Accelerated Masters student in Biomedical Engineering at The University of Oklahoma. She focuses on first year students and self-reflection opportunities within the classroom to enhance learning. ©American Society for Engineering Education, 2024 Examining the Engineering Self-Efficacy, Design Self-Efficacy, Intentions to Persist, and Sense of Belonging of First-Year Engineering Students through Community-Partnered ProjectsAbstractCommunity-partnered projects (CPP) have been used in education from the 1990’s and have beenshown to demonstrate effective learning by working on real-time problems which are diverse andcultural, social, and environmental
influence thatcontribute to the funds of knowledge. Thus, combining them can provide a better understandingof how underrepresented groups (e.g., MSFW students) convert or exchange their funds ofknowledge and social capital to navigate STEM spaces.As stated above, FofK concentrates on students’ families, lived experiences, and communityresources, all of which are impacted by social capital [22], [23], [24]. For example, Stanton-Salazarand Dornbusch [25] mentioned that social capital and FofK are essential for minoritized students’academic success. Their findings showed significant results on how the accumulation andacquisition of social capital are tied to social class, which is also reflected in students’ performanceand access or lack of funds of
disciplines.Section six describes the course assessment methods, including the post-course survey and ananalysis of students' responses from a pilot implementation, focusing on their comprehension ofengineering disciplines, readiness for academic challenges, and confidence. Section sevendiscusses how students’ feedback has been used to enhance the course and the nextimplementation. Finally, the last section concludes by reflecting on the effectiveness of thecourse, arguing the potential impact of this course on students' academic and career decisions.2. Literature ReviewA student choosing an engineering major is influenced by factors ranging from personal interestsand abilities to external influences like family, educators, and societal perceptions [3
. Specific skills developed include computerprogramming in Python, basics of electrical circuits, integrating computer hardware andsoftware, computer networking, and cyber security. Campers were introduced to computingcareers and majors through presentations and guest speakers during the Lunch and Learn time.At the end of the week, teams of campers applied these skills to an Internet of Things-themedCapstone project, which they presented to their peers and parents.Pre- and post-surveys, daily reflections, and structured interviews were collected to establishcontinuous improvements for the program and to further our understanding of how to betterprepare high school students to choose disciplines of study. Triangulation of the multiple sourcessupports
as close as possible to those reported by the in-person group. 4. The students in the remote group perform at least as well as the in-person group in terms of understanding of the concepts related to databases as reflected by grades for the ISBL assignments.Statistical Comparisons and ResultsTable 2 provides the mean, median, and standard deviation of the outcomes measured in thisexperiment. The outcomes include average ISBL assignment grades, score for each motivationconstruct and the overall motivation, scores for experiential learning constructs environment andutility, self-assessment scores for each of the four database concepts and the averageself-assessment score over all concepts, and the SUS score. To compare the two
diverse levels ofcompetence learn from one another and their instructors. In a WisCom, learners collaborativelyfollow an inquiry cycle of learning challenges, exploration of possibilities and resources,continuous reflection, negotiation among fellow participants, and preservation of their new-found knowledge.We are applying this framework to generate a learning community among ECE students andinstructors [10]. Research shows that individuals in a shared academic community often interactthrough social media beyond their courses and become colleagues as they build their careers. Toremediate the lack of belonging that our Latinx ECE students feel, sociocultural learning theorieshave been proposed which frame the design, development, implementation
that students’ scores on the first project were significantlyhigher in the HyFlex modality. HyFlex's median ranks were significantly higher in all other grade measures(Project 2, 3, and final semester grades), whereas means were similar for the rest. Between in-person andone-or-more-times-remote students, t-tests and the Mann-Whitney U test indicated similar grades for Project 1.The median ranks were higher for in-person students, whereas the means in both modalities were similar in allother measures.Study 6: Deep Learning (unpublished work, currently in progress)While grades are a traditional measure of academic success and commonly used to determine universityprogression, they may be reflective of effort and or performance (Banta et al
, data arecentered on tracking students' advancement and pinpointing areas where instructional methods,curriculum design, and classroom management can be enhanced. Instructors and educationalprofessionals employ this information to fine-tune their teaching strategies and aid students facingchallenges.On the other hand, capstone projects reflect a conclusion of students' academic experience andemphasize the practical knowledge and skills they acquired for their future professionaldevelopment. In addition, capstone projects require engaging students in the constraints of the realworld to understand what it takes to achieve social value for the proposed solution and, at the sametime, attain the promised performance and innovation aspects. The data
), provided by CourseNetworking, LLC is a key component of the projectby which students can communicate and collaborate via the online academic networking platform.CN facilitates intercampus activities to lead to cultivation of the scholars’ STEM identity. TheePortfolio feature of CN allows scholars to showcase their accomplishments, academic work andmicro-certification badges that verify their project participation, knowledge, behaviors, and skillsets. Student self-reflection and student-student and student-faculty interactions are improved byCN posting and reflection tools.‘Seeds’ and ‘badges,’ are features of the CN that are used as incentives for scholars to engage inproject activities. They help incentivize, monitor, reward, and celebrate
, and Recognition (R3).The Center’s mission is to recruit diverse and talented students, provide evidence-basedprogramming for retention, and celebrate students’ success by recognizing their hard work andaccomplishments. This mission is tied to the state’s mandate to increase enrollment in highereducation [7] and to boost STEM education to meet the growing STEM workforce needs [8].Tennessee Tech University’s strategic plan to increase freshmen-to-sophomore retention rates to82% and to reach a 50% 4-year graduation rate by 2025 [9] is also reflected in the Center’smission.The CoE SSC activities and outcomes have been through substantial growth in the past threeyears, the staff have been intensifying recruitment efforts, developing and
often regret that I chose chemical engineering. 3. Overall, chemical engineers are considered good by others. 4. Overall, being a chemical engineer has very little to do with how I feel about myself. 5. I feel I don't have much to offer chemical engineering. 13Does endorsement of masculine ideals predict sense of belonging and identity over performance and peer interactions? 6. In general, I'm glad to be a chemical engineer. 7. Most people consider chemical engineers, on the average, to be more ineffective than other groups. 8. Being a chemical engineer is an important reflection of
into an Excel sheet. The responses to the last question were copied and pasted intoChatGPT with the prompt: I asked students what they found most confusing or interesting about an assigned reading. Their responses are below. Summarize them according to what was interesting and what was confusing.Thankfully, the responses did not need to be formatted or edited for ChatGPT to distill rows oftext into a short, concise list. The first few times this method was employed, the efficacy ofChatGPT’s summary was verified with the author’s own review of the student responses. It wasfound to be both an exhaustive and accurate reflection of what the students said. An example ofone of ChatGPT’s summaries can be found in the
noteffective yet. For example, the Department of Energy (DOE), in its Public Access Plan [17]released in June 2023 prompts researchers to write a Data Management and Sharing Plan wherethey will describe, among other things, how data sharing will be maximized, and data repositoryselection. The DOE does not endorse any particular repository and recommends usingrepositories that are appropriate for the data type and discipline, that reflect relevant standardsand community best practices for data and metadata, and that align with the DesirableCharacteristics document. The National Science Foundation (NSF) published in February 2023an updated version of their NSF Public Access plan [18], which is being reviewed after a requestfor information period. In the
reflects the student experiences from one medium sized university in West Texas, thesefindings may not be representative of student experiences of a larger sample from other areas ofthe country. Further, as many of the participants were early in their academic careers, theirexperiences may not reflect those who are farther along in their STEM studies. Due to the cross-sectional nature of this study, retention rates of participants within STEM majors were notmeasured. One of the individuals who participated in the focus groups started college as a STEMmajor but changed their major to history. This student provided feedback about theirexperiences after changing majors saying: "I've definitely felt more supported in the historydepartment. Maybe it's
clear and logical algorithms is crucial, demanding proficiency incomputer programming languages commonly used in engineering, such as Python, Java,MATLAB, or others relevant to the discipline. Additionally, CT serves as a foundational skill fordata analysis and modeling across various engineering disciplines. Its widespread adoption inSTEM education institutions, as evidenced by the incorporation of Next Generation ScienceStandards (NGSS), reflects a positive trajectory in developing CT abilities and meeting thedemands for skilled technical workers [12]. The implementation of CT in engineering education necessitates a shift towards student-centered learning strategies to mirror the complexities of real-world problem-solving
spontaneous questions toexplore, deepen understanding, and clarify answers to earlier questions [15]. Interviews wereconducted by the third author during the latter half of the fall semester and were audio recordedbefore being transcribed by Otter.ai (Otter.ai Inc, 2023) and edited for clarity by the second author.Interview questions were derived from theory and prompted participants to reflect on theirexperiences with mastery-based learning, features of the program, individual and communityefficacy as educators, as well as their perceptions of the student’s failure mindset, attitudestoward assessment, performance/ competence, metacognition (thinking about learning process),agency (ownership of learning), and engineering identity (Table 1). The semi