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for in-person students, whereas the means in both modalities were similar in allother measures.Study 6: Deep Learning (unpublished work, currently in progress)While grades are a traditional measure of academic success and commonly used to determine universityprogression, they may be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
illustrated. Learners are alsoactively participating in the activity. Finally, the instructor asks the same sets of questions toassess how well students comprehend the experiment. ECP Module Instructional Design Template Module Information Synoptic/Purpose of Instructional Instructor Module Process Reflection a. Developers/Instructors a. Essential Questions a. Materials needed/Expected Reflection Institution for use. b. Module Objectives b. Mobile Title/Topic b. Procedures c. Placement within
disciplinary and everyday language in students’ responses. This can help us make thetool a more inclusive generative AI tool that understands the various language students may useto explain their thinking. In turn, instructors and researchers will be more aware of the diverselanguage and thought patterns students use to wrestle with challenging concepts in the discipline.AcknowledgmentsWe acknowledge the support from the National Science Foundation (NSF) through grant EEC2226553. Any opinions, findings, conclusions, or recommendations expressed are those of theauthors and do not necessarily reflect the views of the NSF.References[1] H. Auby, N. Shivagunde, A. Rumshisky, and M. Koretsky, “WIP: Using machine learning to automate coding of student
persistence among low socioeconomic status students," Journal of Experiemntal Social Psychology, vol. 72, pp. 45-52, 2017.[22] R. M. Stwalley III, "Assessing improvement and professional career skill in senior capstone design through course data," International Journal of Engineering Pedagogy 7, no. 3, pp. 130-146, 2017.[23] R. M. Stwalley III, "Professional career skills in senior capstone design," in ASEE Capstone Conference - Columbus, Washington, DC, 2016.[24] J. McCarthy, "Reflective writing, higher education, and professional practice," Journal for Education in the Built Environment, vol. 6, no. 1, pp. 29-43, 2011.[25] G. Bolton, "Narrative writing: reflective enquiry into professional practice," Educational Action
Exercise DescriptionThe robotic platforms were used in an operating systems and systems programming course at PennState Behrend as a part of a lab exercise to demonstrate concepts related to task design, timing,synchronization, and mutual exclusion mechanisms. The exercise was divided into sections:Introduction to the robotic platform operation, task design using timing and synchronizationmechanisms, and feedback and reflection on the lesson learned.The tudentts were first introduced to the basic operation of the robotic arm using manual controland Application Programming Interfaces (API) control through a Python control program. Thechallenges of moving the arm in space using different coordinates and keeping track of the arm’sposition were
on this positive interest from students, a committee of faculty who taught in math andsciences was convened to develop the program. Because of the institution’s historical strengths inthe sciences, the committee recommended that the institution offer a B. S. in EngineeringScience, which was subject to the same ABET criteria as B.S. programs in Engineering andEngineering Physics.[7] It was also believed that the program named Engineering Sciencewould be better accepted at a liberal arts institution where a degree such as engineering might beviewed by some as a strictly vocational major. The intent of the degree to equip students with abroad and general engineering background also reflected key principles of the liberal artsapproach.The
with the specific focus of each survey section, we aimed toensure the relevance and coherence of our assessment tools. This alignment provides a clearerframework for understanding the survey results and reflects the complexity and interconnectednessof sustainability in engineering education.Research Questions: 1. Impact of Active Learning Approaches: How are active learning strategies and hands- on curricular implementations in engineering classrooms related to changes observed in undergraduate engineering students' responses in a six-section pre-post sustainability survey and their open-ended feedback? 2. Comparative Analysis Across Disciplines: How do the pre-post sustainability survey results differ among students
frustrated when it happened.Discussion This study investigated how students perceive generative AI (GAI) for designing mood boards ina computer-aided design (CAD) course regarding design creativity. Specifically, we introduced a workshopand a homework assignment that incorporated the GAI tool Midjourney into the students' final CADprojects, aiming to teach 20 students how to use GAI in conceptual design. Through surveys and interviews,we examined students' creativity in the mood board design process and the final products, comparing themto those created without GAI. Our findings revealed that most students (17 out of 20) believed GAI boostedtheir creativity, although expert evaluations of their works did not reflect this. Additionally, we
in Spring 2023Overall, compared to previous years [18],[19] the gender and racial diversity of the eligibleapplicants and ACCESS scholars decreased despite the wide range of outreach efforts, some ofwhich specifically targeted underrepresented groups of students. The decline in diversity,especially compared to Cohort 1, may partially be due to the fact that many current WestVirginia University students from underrepresented groups, who were eligible for the ACCESSscholarship, applied and were selected in the earlier years of the ACCESS project. In addition,decreased diversity may be reflecting the broader trends in college enrollment, broader genderand racial disparities in Computer Science and
al.’s researcher identity scales, which aim to measure the sameconstructs as in the current research, originally contained 26 total items, but were reduced 16total items following the factor analyses of these scales and those of the related identities(scientist and engineering). One unique advantage of Perkin et al.’s approach is that many of theitems provided a more detailed reflection on the specific context of doctoral education. Forexample, the dissertation advisor is proposed as a critical external source of recognition and thusthe following item was added: “My advisor(s) see me as a RESEARCHER.”2 Similarly, thecompetence scale in Perkins et al. work focuses more on specific competencies associated withresearch, such as delivering
68% 84% 0.0327Class SurveysA weekly reflection and survey were conducted with Likert scale multiple-choice questions. Forthis study, only the results from the beginning of the class (pre) and end of the class (post) wereanalyzed. The complete wording of the Likert questions and answer choices are shown inAppendix I. The survey results analyzed by gender are shown in Table 4. The table shows thesum of the top 2 Likert responses, such as Effective and Very effective to indicate the percentageof students with a positive assessment in each topics area. To show the effect of training moreclearly both the pre- and post- questions are shown when the same question were present in bothsurveys. In Table 4 the pre- and post- questions
variable student experiences thatmay not be represented within this work. Another limitation in the study can be found within thesurvey design. Initially, the project took a deficit framing and developed the survey instrument tocontain questions related to barriers rather than student experiences. In doing this, results may beskewed more towards sharing frustrations or negatively framed experiences in replacement ofauthentic positive experiences that may not have been elicited provided the question framing.Lastly, the students were asked to reflect on experiences at the end of the course, in which theexperience reflected in a student’s response may not be representative of their authentic as timeand other experiences may have skewed memory of
. Aaron W. Johnson, University of Michigan Aaron W. Johnson (he/him) is an Assistant Professor in the Aerospace Engineering Department and a Core Faculty member of the Engineering Education Research Program at the University of Michigan. His lab’s design-based research focuses on how to re-contextualize engineering science engineering courses to better reflect and prepare students for the reality of ill-defined, sociotechnical engineering practice. Their current projects include studying and designing classroom interventions around macroethical issues in aerospace engineering and the productive beginnings of engineering judgment as students create and use mathematical models. Aaron holds a B.S. in Aerospace Engineering
examination. Following each coding session, reflections, emotions, impressions, andinterpretations were recorded in a memo document to note emerging trends. After thepreliminary coding, a second-pass axial coding was conducted on the Excel sheet to identifycommon themes related to the control/treatment group and the decision to stay/leave. Theseemergent codes were discussed with the second author to refine the claims made from the dataand for coding consensus.The authors of this paper have varied experiences with engineering and as members of thegroups we interviewed. The research team of faculty, postdoctoral scholars, graduate students,and undergraduate students included researchers from higher education and engineeringeducation. Three of the
experience. Eighty-seven percent of Seniors (20+ years) reported reasonswhy standards are important. 11The idea is further reinforced by the shifting analytical categories reported by increasing levels(i.e., more years on the job). First, the trend for reasons of Importance seen in the overall data islargely apparent and is reflected in the analytical category Expectations of the Profession whenanalyzed based on Level. As engineers gain experience, the types of technical challenges theyface change, as does the number of challenges they face and their respective knowledge aboutthem. The free-response data suggests this is due to the changing awareness
engineeringpractitioners. Intuition is a skill used by experts in the decision-making process when problemsolving, and believed to develop alongside expertise largely through experience. Previous worksupports that at least six years of experience is necessary for expertise development. Wesubsequently define early-career as up to six years of post-baccalaureate experience and expectthat this population will not yet have expertise and therefore not use intuition. However,research has shown that early-career practitioners who graduated from a primarily undergraduateinstitution (PUI) prior to the onset of COVID-19 both claim expertise and report using intuitionin their decision-making. This unexpected result may be reflective of the PUI’s emphasis onhigh-impact
, reflection, teamwork, and communication skills [3]. And finally, from [6] “We knowfrom research that the more students engage with other students in the class, as well as withprofessors, the more likely they are going to stay and get their baccalaureate degrees.” Boud [3]also suggests that peer learning suits some students better than learning individually, particularlywomen and students from some cultural backgrounds.The approach here is to use CATE to enhance learning in a peer-learning environment. This isintended to provide the many benefits of peer learning without an increased time commitment forthe instructor.Figure 2. A randomly generated circuit and associated step-by-step analysis. The CATE systemincludes an algorithm to select component
. Students can ask any remaining questions they may have 14 Wrap-Up and Reflection about the program and reflect on what they learned about the nature of engineering practice over the semester.Example Lecture: Week 3 – Differentiating STEM Fields Since the first year of most engineering programs consists of mainly science and mathematicscourses, it was pertinent to explicitly describe how engineering is different from these fields and howtechnology interacts with them. The lecture extended these topics to also cover STEAM, where the ‘A’stands for art. The notion of combining art into these fields that are usually viewed as inartistic hasdiscovered a resurgence in the importance of
, “Possible astronomical alignments at Tsiping, New Mexico, a lateAnasazi site.” Bulletin of the Astronomical Society, 12, 886, Sep. 1980.[8] D. Thomas, “Reflections on Inclusive Language and Indexing.” Key Words, 28(4), 14–18,Win. 2020.[9] D. Thomas, “Another Look in the Mirror: Correction to Reflections on Inclusive Languageand Indexing.” Key Words, 29(2), 26, Sum. 2021.[10] C. A. Metoyer, and S. Littletree, “Knowledge Organization from an Indigenous Perspective:The Mashantucket Pequot Thesaurus of American Indian Terminology Project.” Cataloging &Classification Quarterly, 53(5/6), 640–657, Jul./Sep. 2015, doi:10.1080/01639374.2015.1010113[11] M. Ewing, “Representing Historically Marginalized Communities in Archives: MovingBeyond LCSH to Create