learning, enabling students to comprehend, reflect, and apply their learning toward solving new problems. Al- though critical thinking could be used toward solving challenging problems, it is sometimes considered as a similar concept of “challenging level” among students and instructors. This study aims to investigate this similarity issue by evaluating students’ opinions based on critical thinking, and challenging level of course as- signments in computer and software engineering courses. Students are asked to rank each assignment based on how much each assignment stimulated their critical think- ing, and how much it challenged them. Moreover, instructors provide their opinions about critical components of each course assignment for
were also encouraged to conduct a class debriefingsession related to the questionnaire content as either an orientation or reflection, at the beginningor end of the course, respectively. Because it was conducted as a class activity, it was permittedthat all students would complete the items; however, student assent and parent consent wereneeded for student data to be included in our analysis.Student ParticipantsExamining the construct validity of the questionnaire was conducted in two stages, first for EFA,then for CFA. The data for each stage were drawn from consenting student responses to the itemsat 6 high schools in consecutive years. In the first year, nearly 500 students were enrolled in theclasses, but the number of fully consenting
enhancingteamwork skills among STEM students, underscoring the importance of behavioral theory ineducational strategy development.IntroductionTeamwork in STEM education holds paramount significance as it mirrors the collaborativenature of modern professional workplaces. STEM field involves solving complex problems thatrequire multidisciplinary approaches with effective teamwork [1]. This necessity is reflected inthe curriculum of STEM education, which frequently incorporates project work and groupassignments to simulate real-world challenges. These educational strategies are not just aboutteaching technical skills; they are also about fostering an environment where students learn tocollaborate effectively, share ideas, negotiate solutions, and manage group
understanding of how the design problem-solving behaviors ofundergraduate engineering participants differ based on their levels of spatial ability while, whysuch differences exist and how they might affect their learning outcomes is yet to be known. Futureresearch provide us some insight into it.ACKNOWLEDGMENTSThis work was made possible by a grant from the National Science Foundation (NSF #2020785).Any opinions, findings, and conclusions, or recommendations expressed in this material arethose of the authors and do not necessarily reflect the views of the National Science Foundation. 11REFERENCES 1. R. Gorska and S. Sorby, "Testing instruments for the
interactions. Again, this section reflects the NSF emphasis on working cohesively acrossdifferent institutions, disciplines, and areas of expertise to solve large, complex problems.Section 3. Culture of Inclusion Items: Respondents are presented with 11 items, based on theliterature, that measure feelings of inclusion within a group. When we present the visual forcommunicating about the survey below, we will discuss the evidence in support of using it. In the2022 survey, these items were presented to each respondent randomly. The reason for this was todetermine if these 11 items still fell into two factors as they did in 2021, even when not presentedtogether as sets of items.Section 4: Recruiting and Mentoring Activities: In previous iterations of the
cingulate cortex regions of the brain, has been linkedto inhibition control [26-27]. A section of the literature highlights the N400, a prominent negativecomponent peaking around 400 milliseconds, as pertinent to interference control in Stroop tasks[28-29]. The N400 reflects the higher cognitive demand involved in managing the interferencebetween conflicting sources of information, such as ink color and word name in incongruentconditions. Additionally, alongside the N200 and N400, studies have reported a late negativity infrontal regions or a late positivity in centro-parietal regions, typically occurring around 600milliseconds [29-30]. These late components are indicative of processes like executiveengagement, conflict resolution, response
students’ navigational capital, and researchers’ schema development through the peer review process. Dr. Benson is an American Society for Engineering Education (ASEE) Fellow, and a member of the European Society for Engineering Education (SEFI), American Educational Research Association (AERA) and Tau Beta Pi. She earned a B.S. in Bioengineering (1978) from the University of Vermont, and M.S. (1986) and Ph.D. (2002) in Bioengineering from Clemson University. ©American Society for Engineering Education, 2024 Work In Progress: An Exploratory Study of Appalachian Students’ Quest for Success in Undergraduate Engineering ProgramsAbstract This work in progress paper reflects
throughthe EDIL Survey, reflecting a comprehensive understanding of inclusion within academiccommunities. The components from SI suggest that inclusion has a multi-faceted understandingthat goes beyond just being present in a group, to include how one is perceived and valued by theinstitution and its smaller sub-communities. SI-1 also adopted the survey instrument, but theyonly used part of the survey, which focused solely on the engineering department. The reductionin the number of items compared to SI could imply a more streamlined approach to measuringthe sense of inclusion that focuses on specific aspects of inclusion.Psychometric Integrity The study utilized a variety of instruments with different dimensions to measureconnectedness and
Search TermsFor the search, we carefully selected a set of specific keywords and search terms to ensure athorough search, capturing a wide range of relevant papers. Core themes searched were digitalaccessibility and computer science education. Digital accessibility is central to this study,focusing on accessibility in digital and online environments. Computer science or computingeducation refers to the educational context and curricular aspects of computer science. Fromthose core themes we also included the associated terms online learning and inclusive education.Online learning reflects the shift towards digital education, especially relevant due to impact ofCOVID-19. Inclusive education encompasses broader educational principles that
aerospace engineering from the University of Michigan - Ann Arbor and a B.S.E. in civil engineering from Case Western Reserve University, both in the areas of structural engineering and solid mechanics.Dr. 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
for a lesser cost [11].Some textbooks have the option of renting a digital version as well. Students may also be piratingtextbooks online. Nonetheless, this continual increase in textbook cost has resulted in decreasedtextbook purchases. A survey of 1,067 students in 2016 found 66% of students claiming they didnot purchase the required textbook for a given class [12]. Another study from 2020 yielded similarresults with 65% of students claiming they did not buy a required textbook for class due to costs[7]. Cost may not be the only issue at fault, as there is still a lack of usage from students who doacquire the required textbook.Lack of textbook usage is reflected in a 2008 study where undergraduate finance students weresurveyed on the
white and Asian, and 80% identify as men [3]. Usinga sample representative of the discipline would result in an instrument that not only did notaccurately reflect participants who are neither white, Asian, or men, but also would notaccurately reflect the nuance within minoritized groups. For example, Black computingundergraduates attending an HBCU may have differing academic experiences (as part of thedominant racial group on campus) from those attending PWIs (who are part of a non-dominantgroup both in computing and on campus). Students may also be part of a non-dominant group(e.g., race) and dominant group (e.g., gender or ability) based on different parts of their identity.In addition, Cross et al. [1] note that because people from non
itscapabilities, limitations, and ethical implications in different contexts. A visual representation ofthe participants’ perceptions is shown in Fig 1. Fig 1. Visual representation of students’ perceptions of ChatGPTQ2. How do you see ChatGPT evolving in the future and what impact do you think it will haveon education?In analyzing the responses to this question, we employed NVIVO to auto-code the responses.Through this process, a diverse array of themes reflecting various perspectives on ChatGPT'sfuture evolution and its potential educational impact. The question itself bifurcates into two distinctaspects: one regarding future developments and the other pertaining to its educationalramifications. To streamline our analysis, we initially
techniques that accurately reflect the varied ways in whichstudents learn. Starting from this, new evaluation methods are being sought that better fit the wayof learning of each student, so our research will focus on finding a new form of evaluation basedon frequent unannounced evaluations to improve student learning. and contribute to academicintegrity. This new method was applied in civil engineering and architecture courses, along withactivities that develop student learning.Background/FrameworkAcademic integrity within the student environment is related to honesty, responsibility, andrespect, and implies that students must follow rules and regulations, demonstrating theircommitment to responsibility and ethics against frowned upon activities
Virginia Tech. Prior to joining VT, Dr. Pitterson was a postdoctoral scholar at Oregon State University. She holds a PhD in Engineering Education from Purdue University and othDr. Emily Dringenberg, The Ohio State University Dr. Dringenberg is an Associate Professor in the Department of Engineering Education at Ohio State University. She holds a B.S. in Mechanical Engineering (Kansas State ’08), a M.S. in Industrial Engineering (Purdue ’14) and a Ph.D. in Engineering Education. Her current career purpose is to learn about and reveal beliefs that are widely-held as an implicit result of our socialization within systems of oppression so that she can embolden others to reflect on their assumptions and advance equity
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
acknowledges the unique experiences and identity development of male andfemale students who identify as Black. How they have achieved different stages of their racial identitydevelopment affects their STEM reflective identity, competence/ability, value/interest, and assimilationinto STEM culture [10]. Black males and females construct their STEM identities as they develop theirgender identities. Collins [10] notes how racial identity development and gender identity begin to formbefore the development of any STEM interest. The relationship between Black students' gender-basedracial identity and their interest and persistence in STEM is complex. Collins [10] places the gender-basedracial identity of a student in the center of the visualization to mirror
hadcollected. This was followed by each team conceptualizing, designing, and testing theirprototype. Finally, in the fifth stage, each team had to give an approximately 10-minutepresentation. They shared their model, identified the materials they utilized for their prototype,and explained their solution to the problem. For the high-rise activity, during the presentation,the teams had to simulate an earthquake shake test to demonstrate the building’s ability towithstand a possible earthquake. Once every group had presented, the entire class reflected onthe problem and discussed each team’s prototype or model [10], [11]. Throughout the study, theteacher facilitated the learning through questioning and engaging in student discussions whilemonitoring
demands[14].Moreover, authentic learning can enhance students’ personal competencies. Under authentic learning,students have the chance to participate in real-world simulated work, acquire complex information, engagein deep inquiry and ongoing reflection about the “real problems” during the collaborative learning process,which facilitates the higher-order thinking, such as critical thinking, reasoning skills, and engineeringcreativity. Further, authentic engineering learning provides dynamic and interactive engineering scenariosthat involve interdisciplinary knowledge and multidisciplinary collaboration, helping students to becomefamiliar with, understand, and solve real, unstructured, complex engineering problems. Students could gainexperience
. Across campuses andcolleges, dissemination of MHW and other academic support-related information throughcomprehensive and organized means has been advised by the Hunt Institute of public education aswell [26]. Dissemination of such information could be vital to create MHW awareness in highereducation and hence result in reduced stigmatization of students suffering from mental healthproblems [27].Students expected institutional intervention to improve their first-year experiences. MHW andlifelong learning skills integration in first-year engineering courses have been advised byresearchers for student success [28]. In its simplest form, the integration of MHW and personallearning reflections in first-year engineering courses may have positive
materialare those of the author(s) and do not necessarily reflect the views of the National ScienceFoundation. References[1] Council of Graduate Schools, “Ph.D. completion and attrition: Analysis of baseline data from the Ph.D. completion project,” Council of Graduate Schools, Washington, DC, USA, 2008.[2] C. Wendler et al., “The path forward: The future of graduate education in the United States,” Educational Testing Service, Princeton, NJ, USA, 2010.[3] J. M. Jones, “The dual pandemics of COVID-19 and systemic racism: Navigating our path forward,” School Psychol., vol. 36, no. 5, pp. 427-431, Sep. 2021, doi: 10.1037/spq0000472.[4] C. Davies, C. A. Arbeit, and M. Yamaner
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
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
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
statistically significant differences for Scenario 3.LimitationsThere are several limitations inherent to this work. Given the diffuse subject recruitment strategy,it is possible that ethically minded individuals are overrepresented in the sample (i.e., thatethically minded individuals would be more likely to respond to a voluntary survey onengineering ethics). Further, this survey examined individuals at one Research 1 institution in theUnited States and the results may to a degree reflect that (e.g., individual’s views on code sharingmay be influenced by institutional academic integrity culture and rules). Subjects were askedabout their perceptions of the views of industry, but contemporaneous surveying of individualsfrom industry was not an
toconsider the various aspects of wellbeing for the design of instruction as well as policy.Acknowledgements We thank Erin Rowley, the engineering librarian at the University at Buffalo, for hersupport in the database selection and helpful recommendations for conducting this systematicreview. We also thank Joseph McCusker, engineering student at University at Buffalo, and anundergraduate researcher at DARE to CARE lab, for his invaluable assistance with the review ofthe studies. This material was partially supported by the National Science Foundation Grant No.2147193. Any opinions, findings, and conclusions, or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the National
graduatestudents. Items that received lower average scores focused on mentoring skills related tocommunication, coordination, personal relationships, and career planning. This was reflected inthe open-response questions, where participants frequently cited these areas as problems orpoints of stress in their relationships with their advisor(s). Items that received higher averagescores focused on research skill building, resource acquisition, feedback, and trust. These areastend towards some of the more technical aspects of mentoring that advising requires, whichengineering doctoral advisors may feel more comfortable with. For example, setting researchgoals with students may come more naturally for faculty members than helping students preparefor a career
.,instructors and teaching assistants) were guided to take training and were provided withguidelines to effectively administer the oral exams. In terms of training, online modules weredeveloped and were followed up with reflection activities on relevant topics (e.g., reducingstudents’ anxiety; effective communication and making the student comfortable whenadministering the oral exams). Assessors were encouraged to implement grading rubrics and 5scripts that incorporated those practices (e.g., anxiety-reducing gestures, scaffolding studentswith expectations, minimizing time pressure) to standardize the procedures and fully capture thestudents
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