, what they do know, andhow they know it. A corollary of this is that once students realize they don’t know something, Page 23.1328.4they are more receptive to instruction and are “primed” to learn. This motivation is difficult tomeasure, but appears repeatedly in student attitude surveys we have collected. These twoexamples of student comments reflect some of these ideas: • “It's good to be able to formulate an answer right away to see if you understand it. Without it, when a professor is teaching something new, you think you know how to do it until you try to work on it later and realize there was an aspect you needed clarification
required to build full sized antique artifacts.The knowledge gained from this project, whether it utilized scale models or full sized replicas,was for all intents and purposes the same. The major difference was cost. For universities withlimited budgets, the research and development of scale models makes perfect sense. Thisdiscovery, by itself, made the project worthwhile for both the students and faculty involved.Conclusions, Reflections, and the FutureThe use of scale models recreating ancient technologies has been added to the Technology inWorld Civilization course. The use of scale models has impacted the students learning process inthree ways. First, students take an active part in the construction process requiring trial and errorattempts to
of software versions, missing software or hardware components, access rights to drivers, etc. would cause many problems and shall be tested prior to the lab sessions. 2) It is important to clearly state deadlines and consequences of late submission. A lack of hard deadlines and late-submission consequences was also assumed by many students. Despite repeated reminders, a lot of students forgot to submit the model files they used in the lab. The solution was to grade late submissions much more harshly; it is fine if a student needs more time to complete a report, but the quality of the submission must reflect this extra time spent. 3) It is important to clearly specify expectations in a grading rubric
physical world of the production to reflect the inner struggles of the characters. Big Daddy’s house is not an easyplace to live in for any of the characters in this play. By raking the stage the director and designer have made the psychic and emotional challenges of the story a tangible part of the environment for the audience.”At this point, the students recognized the rationale for building a raked stage despite moderntheaters having their seats stacked vertically. To place the students in the position of being futureartistic directors themselves, we then transitioned to the first part of the lab activity, whichsimulated the construction of a raked stage at different angles of incline. The students were
?”. European Journal of Special Needs Education. Vol.32, No. 1, pp. 71-88. 2017.[12] Brownson C, Drum DJ, Swanbrow Becker MA, Saathoff A, Hentschel E. “Distress andsuicidality in higher education: Implications for population-oriented prevention paradigms.”Journal of College Student Psychotherapy. Vol. 30, No. 2, pp. 98-113. 2016.[13] Condra M, Dineen M, Gills H, Jack-Davies A, Condra E. “Academic accommodations forpostsecondary students with mental health disabilities in Ontario, Canada: A review of theliterature and reflections on emerging issues”. Journal of Postsecondary Education andDisability. Vol. 28, No. 3, pp. 277-91. 2015[14] Peterson J, Digman MF. “A Comparison of Learning Outcomes and Learner Satisfaction ina CADD Course with Flexible and
among educators. Some faculty have completely banned AI use in theircourses, while others embrace AI as a new learning tool [6].The emergence of AI is prompting civil engineering faculty to reflect on whether we shouldadjust our educational approaches. For civil engineering faculty at accredited institutions, wemust look to ABET for guidance [9]. While the current guidelines do not address specific toolsand methodologies, it seems reasonable to expect that AI may become part of standard practiceused to solve engineering problems (addressing ABET Student Outcome 1). The application ofAI may also be useful in developing engineering designs that are holistic and meet a variety ofpublic needs (addressing ABET Student Outcome 2). Communication
own equation sheets inthe work cited in [30] and [31]. Advantages and disadvantages of student-produced versusinstructor-provided equation sheets are not well studied, although in [32] a student specificallymentioned that preparation of the sheet assisted in studying. However, in [33], the possibility of astudent forgetting to write down an equation needed on their equation sheet and thus not having iton the test was raised.Some research has addressed deeper questions of equation sheets, their advantages, and theirpossible down side, with [34] indicated that students trying to transfer knowledge from calculusto physics and later to engineering relying heavily, perhaps too heavily, on equation sheets. In[35], a student reflection mentioned
who are studying engineeringis needed. Future work might include comparative assessments of the perceptions, experiences,and outcomes of CSt who are studying engineering with those in other areas of study, whichwould lay a foundation for developing interventions needed to support CSt in engineering.AcknowledgementsThis work is supported by the National Science Foundation under award #2119930. Anyopinions, findings, and conclusions or recommendations expressed in this material are those ofthe author and do not necessarily reflect the views of the National Science Foundation.References [1] Khan, K. S.; Kunz, R.; Kleijnen, J.; Antes, G. Five steps to conducting a systematic review. Journal of the Royal Society of Medicine 2003, 96, 118–121
general, a broader research base on SBPs is likely to be useful inmeeting program goals.AcknowledgementsThis work is supported by the National Science Foundation under award #2119930. Anyopinions, findings, and conclusions or recommendations expressed in this material are those ofthe author and do not necessarily reflect the views of the National Science Foundation.References [1] What Works Clearinghouse Summer Bridge Programs. 2016; https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=WWCIRPS661, Publisher: National Center for Education Statistics. [2] Yoder, B. L. Going the distance: Best practices and strategies for retaining engineering, engineering technology and computing students. American Society for Engineering Education. 2012
problems to solve, defining the problem space, making design decisions toprioritize certain technical features over others, etc. - are always shaped by cultural norms. Theengineers' social and political beliefs are always reflected in their practices and their work [16],[17]. Engineering as a heterogeneous practice should be aware of its entangled social justiceissues and work with the communities when creating designs [11], [18], [19].Engineering education is moving towards perceiving engineering as a sociotechnical field notonly because of the shifting ideology described above but also because the movement can betterengage students’ identities, hence broadening participation in engineering [8], [20]. Becauseengineering has been heavily
dropin post-course evaluation scores (Tab. 1; ‘21). Post-course evaluations revealed significant issues withthe design of the new MBL approach. Reflecting on the student feedback led to the establishment of aset of best practices that could improve the development and delivery of future MBL assessments.Redesigning the MBL assessment following these principles resulted improved post-course evaluationsduring the 2022 and 2023 offerings of MD1 (Tab. 1; ’22-‘23).The best practices used to improve the MBL approach for MD1 are briefly summarized below:Best Practice 1: Each mastery skill should only evaluate one well-defined skillIt is recommended that skills requiring complex multistep solutions are broken into separate skills. Forexample, a vaguely
large mid-Atlantic institution. As described at 2023 ASEE Annual Meeting, we spentthe past few years increasing the response rate to the end-of-course survey and aim in the longerterm to increase the proportion of students who self-report their attainment of the course learningobjectives as either “Good” or “Excellent.” After reflecting upon these results and learning lessonsfrom previous course offerings, we are now introducing strategies to increase student engagementfurther and attain department metrics for the course. Starting in Fall 2023, 13 activities wereintroduced to improve the course. The activities can be grouped into three categories: (A)Demonstrating the worth of the course to the students; (B) Making class fun; and (C
paper is meant to provide adetailed account of the perceptions of five students in one course.Another limitation of this work is that terms such as “sense of community” and “trust” were notdefined for students, so their responses to the focus group questions reflect their ownunderstanding of what these terms mean. In future iterations of this work, we could ask studentsquestions such as: In your view, what does it look like to have a classroom community? Is havinga sense of community important to you in your graduate courses? Why or why not?It could also be helpful to explain to the students the purpose of the focus group beforehand.Students seemed to be expecting to give feedback in a manner similar to a course evaluation andanswer questions
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
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