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
the first mechatronics course in Mechanical Engineering Technology(MET). The lab modules provided students with practical experience in using IoT technologiessuch as MQTT, ThingSpeak, and Simulink to design and control mechatronic systems. Themodules covered a range of topics, including motor control, feedback control, and systemmodeling and simulation. The course provided students with a strong foundation in thetheoretical concepts of mechatronics, which were then reinforced through the hands-on labmodules. The success of the course is reflected in the positive feedback from students, whoappreciated the practical skills gained through the lab modules. Moving forward, the course willevolve to meet the changing needs of students and industry
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
, the simplicity of the project naturally yields the project to be used in awide variety of learning environments and student learners. When implementation does occur, the generatedresults would need to be studied and further modifications would be made to the teaching approach.Eventually, the module and learning materials along with the project will be made highly accessible toeducators through a centralized soft robotic teaching website being developed at Rowan University.AcknowledgementsThis material is based upon work partially supported by the National Science Foundation under Grant No.2235647. Any opinions, findings, conclusions, and recommendations expressed in this material are thoseof the authors(s) and do not necessarily reflect the
could be’, 2019, doi: 10.1007/s11186-019-09345-5.[26] S. Hunziker and M. Blankenagel, ‘Single Case Research Design’, Research Design in Business and Management, pp. 141–170, 2021, doi: 10.1007/978-3-658-34357-6_8.[27] R. H. Horner and J. Ferron, ‘Advancing the Application and Use of Single-Case Research Designs: Reflections on Articles from the Special Issue’, Perspectives on Behavior Science , vol. 45, pp. 5–12, 2021, doi: 10.1007/s40614-021-00322-x.[28] V. S. Athota and A. Malik, ‘Within-Case Qualitative Analysis’, Managing Employee Well-being and Resilience for Innovation, pp. 95–174, 2019, doi: 10.1007/978-3-030- 06188-3_5.[29] I. Halevi Hochwald, G. Green, Y. Sela, Z. Radomyslsky, R. Nissanholtz-Gannot, and O
lecture series program Q7. How did the [component] Mean 3.875 3.333 affect your sense of belonging in the research group? Std. dev. 0.696 0.471PALS surveyThe Patterns of Adaptive Learning Scales (PALS) survey is demonstrated in the literature toaccurately predict the motivation and persistence among students that engage in researchexperiences [15 ,11][19 ,18]. This instrument can assess the perceptions of student’s goals,which include orientations that are classified as mastery (or task), performance-approach, andperformance-avoidance. The revised scales were used in this study to reflect the adaptation of thePALS survey to measure goal
student engagement, critical thinkingskills, and overall learning outcomes. The current study contributed to the discourse on thetransformative potential of hands-on learning in the context of biology education.Massachusetts Institute of Technology (MIT) Digital Learning Lab, in one of their articles [26],conceptualized hands-on learning as a cyclical process that encompasses concrete experience,reflective observation, abstract conceptualization, and active experimentation. A few studieshave shown how hands-on learning improves student outcomes, including motivation andengagement, conceptual knowledge, critical thinking, and problem-solving development. Tofurther substantial the ongoing discussions, some studies [27], [28] have found that hands
activities of the course studied?” Our datasuggest that students’ learning of the literacies of HCD is reflected through the different stages oftheir capstone project. Moreover, they used the literacies as tools for honoring the voices andexperiences of the community where they implemented their project.Our study offers implications for engineering education. Foremost, although not directly theobject of this paper, it is impossible to understand learning without considering teaching. In aphenomenographic study, Zoltowski et al.[46] argue that students’ ways of understanding andexperiencing HCD have different degrees of comprehensiveness. Our data show that focalstudents seem to present a comprehensive perspective of HCD: The main issue with the
supported by the National Science Foundation under GrantNumbers 2346868 and 2144698. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation. We would like to express gratitude to Team Y for participatingin this study and for their willingness to open their meetings to us and provide feedback on theinitial drafts of this paper. We would also like to thank Dr. Nicola Sochacka for her insightfulfeedback and discussions as we analyzed our initial data. Finally, we would like to thank themembers of the ENLITE research team who gave feedback to the drafts of this paper.References[1] M. Borrego and L. K. Newswander
judgements), the appreciation of the idea (appreciatingfeedback) and managing the emotions associated with the idea (managing affect). Thus, anappropriate framework for idea acceptance would comprise of the same three areas, justworded to reflect their association to any idea as opposed to feedback literacy. This modelcan be seen in Figure 3. Apprecia�ng the Topic Evalua�ng the Idea Managing Affect Idea AcceptanceFigure 3: The Proposed Idea Acceptance Model. The model contains three dimensions: Appreciating the Topic, Evaluatingthe Idea and Managing Affect. All three dimensions are required to achieve Idea Acceptance.This model is also inspired by the
Communication 161 Total 962Also not reflected in these numbers is the use of our materials by our industrial stakeholders.After working with us as consultants, two of our industrial consultants requested access to thevideos for use in onboarding new employees. We gave them access to our videos, but we werenot able to give them access to our learning management system and the ability to earn badges,since Brightspace usage is restricted to Purdue affiliated users.Table 2 and Figures 1-3 contain selected comprehensive results of the feedback surveys fromstudents in the pilot courses. We chose to present comprehensive results (rather than results byclass, gender, etc.) since our aim for the pilot
NationalScience Foundation research. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author and do not necessarily reflect the views of theOffice of Naval Research or the National Science Foundation.References[1] B. K. Townsend and K. Wilson, “A hand to hold for a little bit: Factors facilitating thesuccess of community college transfer students to a large research university,” Journal ofCollege Student Development, vol. 47, no. 4, pp. 439-456, 2006. [Online]. Available:https://doi.org/10.1353/csd.2006.0052.[2] D. D. Buie, “Beyond a deficit view: Understanding the experiences of first-generationstudents who participate in college access and success community-based organizations,” Ed.D.dissertation
overview ofcurrent knowledge, theories, methods, and gaps in the existing research [12]. Topical aspects ofthe research question frame the literature review and provide an understanding of the challengesfacing technical education today. The literature review reflects on and researches the subject andhow the issues contribute to the literature [13]. This literature review begins with knowledgeareas that support improving clean energy educational opportunities for current and futuretechnicians in clean energy industries.Existing literature was reviewed to identify key skills development approaches and strategieswithin the context of the fast-moving and technology-intensive clean energy industry, using athematic approach to consider the following
projects. The end of both design projects reserved one day to focus on EMand asked them to reflect on questions they had about engineering, to create a concept map as agroup about EM, and to identify the value they had created for stakeholders in their projects. In2021-2022, this was the first introduction to EM for both projects and was designed as areflection. In 2022-2023, it was the first introduction to EM for the robot project, but theresearch-based project had heavily focused on EM and value creation throughout the semester.Student workload across the design projects was reduced in 2022-2023 compared to the yearprior. For example, students were given additional time to brainstorm their designs and createthem, reducing the overall number of
andthe R.O.S.E Research Group at the University of Cincinnati. Without your support and guidanceduring the writing process, this document would not be what it is. We are honored to be a part ofthese outstanding groups of scholars.This work is based on research supported by the National Science Foundation Grant Awardunder Grant No. 2212690. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the views of theNational Science Foundation. References[1] K. J. Jensen and K. J. Cross, “Engineering stress culture: Relationships among mental health, engineering identity, and sense of inclusion,” J. Eng. Educ., vol
integration of Generative AI in engineering education has proven to be a trans-formative force, enhancing traditional learning methodologies and empowering students toachieve greater creativity, depth, and innovation in their academic work. Through the practicalimplementation of AI-driven tools in courses such as Circuit Analysis, Dynamics, ElectricalPower, and Industrial Power, students have experienced significant improvements in project qual-ity, critical thinking, and collaboration skills. These advancements reflect the potential of Genera-tive AI to revolutionize PBL and to support personalized learning experiences, enabling studentsto excel in the rapidly evolving field of engineering. However, alongside these benefits, importantethical
has been known to significantly increase success, retention, and graduationrates. We noticed the differences in the level of preparedness and its influence on the student’sperception of their journey. We also explored the influence of soft skills, outlook, scholarlyattributes, and support on the perception of the journey through the program. Although ourparticipants have reported that they did not perceive any overt sexism or racism, we present thefindings correlated with gender and race/ethnicity.Our future work will include fine-tuning the protocol to explore intersectionality and reflect uponthe situations where the students might feel minoritized. Additionally, the students in the futurestudy will be purposefully selected to examine
items passed the .32 criteria, and together, the model explained a totalof 46.26% variance. Therefore, we proceeded with the more parsimonious one-factor solution.The one-factor CFA model fitted poorly to the data. Therefore, we explored the modificationindices. By allowing error covariances of similarly worded items (i.e., between items 16 and 18,19 and 21, 17 and 23, 19 and 22, 19 and 20, and 20 and 21), we reached an acceptable model fitfor the one-factor solution of the CFA sample (χ2 = 137.52, df = 16, p < 0.001, RMSEA = 0.1095% CI [0.085, 0.116], CFI = 0.96, TFI = 0.93). All items loaded above .50 onto the mindsetfactor. These modifications reflected the covariance among items that focused on intelligenceand among items that focused on
CS.Next, the theme of collaboration was also found to be beneficial for students’ formation of bondsin CS. This result is reflected in prior work whose results suggest that the long-term impacts ofproject-based learning in STEM transcend traditional learning outcomes to also includeprofessional advancement and friendships [60]. Further, authors demonstrate that students’exposure to collaborative assignments are a significant, positive predictor of their persistence inCS [26]. Interestingly, however, the more recent work of Lehman et al. [32] found that students’exposure to collaborative pedagogy in introductory CS courses was a significant, negativepredictor for persistence. In their discussion, they suggest that the surprising result may
betelling of how students approach learning with the affective domain [14]. Also, returning to theidea that the domains are connected is reflected in the fact that many of studies found focus on twodomains at a time instead of only one domain at a time [4-7], [14-19]. Several studies exist thatresearch the domains, but they focus on testing a specific class within engineering or non-engineering majors [4-6], [9], [14-16], [18], [20]. Similarly, the studies that focus on math orchemistry classes may not have tested solely engineering students, which could still distort or skewresults towards conclusions that may not apply to engineering students overall [4-5], [21]. Theproblem with these studies is that their findings cannot be generalized for all
analyses at subsequent time points. For instance, if X students drop out orgraduate by the end of a semester, they will be removed from subsequent analyses, ensuring thatthe remaining students constitute the entire study cohort for subsequent persistence analyses.The study will acknowledge the dynamic nature of student enrollment, and robust measures willbe employed to handle attrition. The removal of students who exit the program will ensure thatanalyses reflect the evolving composition of the sample, contributing to the accuracy andrelevance of the findings.ConclusionIn conclusion, this study undertakes comprehensive exploration of the factors influencingengineering student persistence, with a particular focus on the impact of Calculus I. By
avariety of digital tools. Their choices reflect their degree of awareness and understanding ofavailable tools, showcasing whether they are acquainted with a diverse range of technologiesrelevant to the construction industry. On the other hand, assessing students' comfort levels inusing a specific digital tool provides insights into their confidence and self-perceivedcompetence. This subjective measure complements the objective evaluation of their toolselection, offering a holistic view of their digital skill awareness, confidence, and readiness toapply their knowledge.These scenarios were crafted to assess participants' knowledge of digital technologies and theirreadiness to apply them in practical construction scenarios. By presenting authentic