by Schijf at al. [27] which includes knowledgeof disciplinary paradigms and interdisciplinarity and skills on reflection, critical reflection,communication, and collaboration. The questionnaire includes questions in all six categories(knowledge and skills) and measures responses using the 5-point Likert-scale from (1) ‘veryinaccurate’ to (5) ‘very accurate.’ A paired two sample t-Test for means was performed for eachattribute of interdisciplinary understanding to compare student perceptions of their proficiency beforetaking the interdisciplinary project experience course and after completion of the course.ResultsWhile the analysis results indicate that students reported statistically significant improvementson the majority of the attributes
few engaged in interventionist approaches like design-basedresearch [08.pdf, 34.pdf] or action research [09.pdf] to improve their teaching practice throughiterative changes and one adopted an ethnographic approach that reflected on past experiencesover a number of years teaching at different institutions [13.pdf]. Data was collected primarilyusing mixed methods, to collect qualitative and quantitative data (23 papers or 48%), see Figure1 b. In addition, 16 papers (33%) reported on qualitative studies typically involving studentperceptions of teamwork which were gathered through individual and group interviews. Theremaining 9 papers (19%) discussed experimental studies that tested hypotheses and typicallyanalysed quantitative data to determine
never namedsurface strategies such as flashcards or memorization). Nonetheless, ‘distance’ from the coursecontent–such as Internet searches vs. office hours–does explain group differences. Studentsshould be advised to stay close to course content when studying.Our questionnaires may have forced students to reflect on which strategies they used and howuseful they were; these kinds of metacognitive skills in monitoring the use of and planning the useof study strategies should be modeled and encouraged to help students study most effectively.Interventions could include making students more self-reflective about their own strategy use(e.g., a self-regulated learning intervention).Our study had a few limitations; even relative to undergraduate
discussed their experiences and best practicesaround the topic of discussion. Circle topics included finding balance in graduate school,building resilience through self-reflection, advisor relationships, and self-advocacy. Resourcessuch as goal setting and tracking journals, books on mentoring and community building, andonline resources on building mentoring relationships and thriving in graduate school weredistributed during these workshops and group mentoring sessions. Mentors and menteesgenerated a list of potential discussion topics to use during the mentoring circles for the secondcycle.Data collection and analysisIn the first cycle, at the kick-off workshop, we collected data from mentors on potential areas ofmentee support based on the
interpretation of extensive reference tables and diagrams [3] to understandhow the system will respond and when it will change how it behaves. This interplay ofconceptual understanding and applied problem-solving is further compounded by the need forproficiency in complex mathematics, a combination that frequently frustrates learners andhinders their academic success.The consequences of these challenges are reflected in concerning academic outcomes; forinstance, a longitudinal study at the University of Texas, San Antonio, revealed that less than53% of students completed introductory thermodynamics on their first attempt, with even lowersuccess rates for repeat attempts [4]. These findings underscore the urgent need for innovativestrategies to enhance
week of classes, students areencouraged to reflect on their calling to be an engineer and their thoughts are discussed in a groupdiscussion setting. The feedback of the course is collected and recorded using various tools.Table 1: Course Schedule of the Unified Introductory Course Week 1 Making the Transition from High School to College Week 2 Getting Involved with an Engineering Organization Week 3 What is Engineering? Week 4 Engineering Disciplines Week 5 Engineering Design Process Week 6 Engineering Code of Ethics Week 7 Tower Building Project Week 8 Bridge Building Project Week 9 Bridge Building Project (contd.) Week 10 Digital Circuits Project Week 11
data was comprehensive and reflective ofparticipants’ experiences [8], [4].AnalysisThe analysis began with an initial round of open coding, where segments of the data wereassigned codes based on broad themes. This process was guided by the research question: “Whatinformation-seeking strategies do engineers use to equip themselves with the technical skillsneeded on the job?” During this phase, the coding focused on identifying excerpts related to"information-seeking behavior" and "organizational socialization" [4], [5].In the subsequent phase, secondary-cycle coding was conducted to refine and organize the initialcodes into broader thematic categories. This step involved an inductive approach, with themesemerging through repeated readings of
management, courseenrollment, and financial literacy, as well as other topics related to academic, personal, andprofessional development. Mentors also attend and engage in the seminars, which serve as aplatform for community building among participants.Program staff, consisting of faculty, professional staff, and student leaders, track menteeengagement through seminar attendance and digital meeting reports submitted by mentors.PMPECS operations rely on several digital tools to support program tracking and oversight.Mentors are expected to log biweekly meetings using a shared online spreadsheet and submitdetailed reflections via Qualtrics™, an experience management platform, with prompts focusedon retention risk indicators (e.g., academic, social
, company visits, and professional development activities, including a"DTech Circles" program where students share weekly reflections with their peers. Thesestudents were selected to address this driving question as this program was designed to foster aninclusive environment, allowing participants to reflect on this experience in addition to theirexperience in their university and academic departments.Recruitment occurred through program newsletters, targeted outreach to DTech students fromminoritized groups, and general recruitment emails to all DTech members. Nine studentsparticipated in focus groups and interviews, representing a range of academic years (33%sophomores, 33% juniors, 33% seniors) and racial/ethnic backgrounds (33% Black or
trends. These efforts aim not only to maintain curricular relevance butalso to enhance student retention and academic progression.In 2017, the program introduced a revised curriculum (DUN 2401-2016) and, in 2022, initiatedanother cycle of curricular innovation. As part of this process, evaluating the academictrajectories of students who enrolled in March 2017 is crucial in assessing potential gaps betweenthe intended curriculum design and students' progress. The findings from this evaluation willprovide valuable insights to guide the ongoing curriculum revision. While this study is limited tothe 2017 cohort in one university, the observed patterns may reflect broader structural challengescommon to engineering education programs in similar
6Curriculum Choice: For all chosen data science programs, we chose the syllabi from core datascience curriculum for our content analysis. Core curriculum is determined by whether it coversthe top competencies identified by previous study [18] and listed in above Table III.Tables VII and VIII present the number of core data science courses selected for this study,organized by country and by institution, respectively. Both tables demonstrate that the Chineseand U.S. program samples are comparable in terms of competency coverage and the balancebetween required and elective courses. Table VIII further highlights variations betweeninstitutions, which may reflect either broader curricular options or differences in syllabusavailability. In this study, “core
throughinteractive teaching, workbook activities, crafts, videos, games, and research. The highlight ofthe camp was the Robot Rally, where campers performed original skits from their research intohealthcare challenges they were motivated to solve using a humanoid robot.This paper captures our reflections, details of implementation, lessons learned, evaluation results,and the effectiveness of exposing AI, robotics, and healthcare solutions to underrepresentedminorities when presented within a culturally responsive curriculum.OverviewThe first AI+ Health and Humanoids (AI+H+H) camp was held June 10-14, 2024, by theArtificial Intelligence, Algorithmic Integrity, Autonomy Innovation Center (AI3C at The CitadelMilitary College in Charleston, South Carolina). The
. This digital evolution highlights the critical needfor a dynamic response to these safety and security challenges. The Bureau of Labor Statisticsprojects a growth of 33% in Information Security Analysis and related fields between 2023 and2033 [1], reflecting the growing importance of data protection at both the individual andcorporate levels. In response to this, cybersecurity professionals must not only possess technicalknowledge and education but be equipped to anticipate and adapt to both current threats andemerging risks.Recognizing the limitations of traditional classroom instruction alone, many universities havedeveloped hands-on lab courses and summer programs to broaden students’ experiences.Programs like the National Science
open challenge 6 . There have been effortssuch as the Data Science Corps: Wrangle-Analyze-Visualize (DSC-WAV) and the Attitude,Skills, Communication, Collaboration, and Reflection (ASCCR) that have tried to teach studentshow to collaborate but often do not focus exclusively on teaching the social skills necessary forsuccess in collaboration. Thus, this work seeks to contribute an approach for teaching CPS to datascience students. We’ve developed a module for teaching CPS that allow students to learn andapply their skills in a mock data science project. This work is grounded in well-establishedframeworks for CPS and follows a simulation-based approach to teaching these skills.Although several existing frameworks provide a foundation for
, wepresent students with four pairs of short paragraphs. Each pair covers the same topic, but the twoparagraphs are differed to reflect distinct aspects of each of the four dimensions of the Felder-Silverman model. This model seeks to explain how students best perceive (sensory/intuitive),receive (visual/verbal), process (active/reflective), and understand (sequential/global) newinformation [14]. Providing diverse pairs of training texts in this way helps us capture variouslearning preferences in the students’ profiles. Doing so is critical as the training texts provide theinitial data for each profile.Note that traditionally termed learning style models, including the one developed by Felder andSilverman, have garnered significant criticism for
to the civil engineering profession and their chosen major.This is accomplished through discussion topics, including the engineering design process,aspects of a profession, codes of ethics, sustainability, and technology. CE201 was added to thecivil engineering curriculum during the fall of 2018 and has subsequently been offered every fallsemester. As a required course in the civil engineering curriculum, it is commonly taken during astudent’s first semester in the program, but occasionally it is taken later by students who transferinto the program late. The course is typically team-taught by 2-3 instructors.There are multiple writing assignments within CE201. One reflective essay requires students towrite about their process of selecting
classes themselves are so difficult and emotionally taxing … it'shard for me to place myself in the position of being an engineer because it seems like somethingthat has been so unachievable”.4. Do other people (such as family, friends, peers, or professors) see you as an engineer? Is itimportant to you that other people see you as an engineer?Student responses to this question exhibit how external validations, particularly by strangersthrough social interactions in college, in a conference, in an engineering classroom, or off-campus during an internship, are reflected in some students’ sense of belonging and self-beliefsabout being an engineer.People in Mia’s inner circle, i.e., family, friends and some peers, who are aware of her pursuit
graduate Foundations I -Estimating, and the case involves the use of Togal.AI, ChatGPT, and MS Copilot forconstruction estimating solutions. This approach is suitable because it allows for in-depthexploration of students' perceptions, challenges, and the impact of AI tools on learningoutcomes. An IRB approval was obtained for the study.ParticipantsThe participants in this study are students enrolled in a graduate course (n=9). These studentswere assigned a semester project to explore and utilize various AI tools to complete constructionestimating tasks. Students provided individual submissions detailing their experiences,preliminary research, and reflections on using AI tools during the semester. Their variedbackgrounds, ranging from minimal
favorof there being multiple cognitive schemas available to a person depending on the specificsituation they are considering, although there can be a preferred schema. Despite the shift intheoretical frameworks, the DIT remained a primary assessment tool for studying moralreasoning, although the interpretation of results changed.The original DIT required test takers to read six stories concerning moral dilemmas and then rateand rank items related to the stories. In the 1990’s, the DIT was revised, producing the DIT-2,with new stories that reflected the changing social context [2].The original DIT used a numerical index, the P-score, that measured the percentage of post-conventional responses to a moral dilemma. The DIT-2 also uses the P-score
Computing Education: Research Landscape Over the Past Decade Introduction The construct of sense of belonging (SB) has garnered significant scholarly attention in thefields of engineering and computing education in recent years, reflecting a growing awareness ofits pivotal role in shaping student success and well-being. This surge of interest aligns with broadertrends observed in STEM education, where SB has emerged as a crucial factor in fosteringinclusive learning environments and promoting academic persistence [1]. However, the rapidproliferation of research in this domain has revealed several areas in need of further explorationand synthesis, particularly in generating knowledge on
. The curriculum will be delivered in multiple levels,or “tiers.” This paper will detail the development of the so-called level one (core) curriculumwhich covers a broad range of topics and is intended to build foundational knowledge for anunexperienced audience. Additional METAL training levels, still in development, will providedeep dives into industry-relevant and advanced topics.3 METAL Level One (Core)METAL trainings are intended to cover a wide range of industry relevant topics in metalworkingbeginning with basic, foundational knowledge and progressing through advanced university-level research topics. The training curriculum is colloquially referred to as “tiered” or“stackable” reflecting that each subsequent level builds upon
learning tools into these design processes could influence students’metacognitive practices by encouraging reflection, facilitating self-assessment, and providingtailored feedback that adapts to the specific demands of hardware- or software-focused designtasks.The distinctive nature of engineering design tasks also gives rise to unique cultural frameworks,behaviors and practices shaped through interactions in various social contexts. AnthropologistPierre Bourdieu [18] speculated that these cultural environments influence the design process.He argued that individuals’ everyday activities shape how they approach design tasks, suggestingthat “learning and doing is more than a cognitive activity. Ways of knowing and doing are uniqueto each group and
grounded theory approach, we chart and characterize the state of IntegratedEngineering principles and learning competencies in each study. The contribution of this work isto engage readers to reflect on their views towards Integrated Engineering within EngineeringEducation literature, gain an understanding of models and competencies of IntegratedEngineering often explored within Engineering Education literature, and inform content from thegathered data that is useful for teaching and learning Integrated Engineering.1.1 What is Integrated Engineering, and why is it important in Engineering EducationIntegrated Engineering, motivated by pushes to connect topics across disciplines in practicalcontexts better, was noted by Froyd et al. to begin with
. Moreover, walkable urban environmentsalleviate traffic congestion, enhancing mobility and accessibility for all, including vulnerablepopulations. These features encourage face-to-face interactions, foster social engagement, andstimulate local economies, reflecting thoughtful urban planning and a commitment to long-termsustainability. Walkability encompasses a range of built-environmental features that directly andindirectly enhance population health and well-being. Initially conceptualized in the 1960sthrough studies of sidewalks and pedestrian safety in U.S. downtown areas, the concept ofwalkability has evolved into a fundamental pillar of modern urban planning [1]. Jeff Speck, aleading urban planner, articulated the General Theory of Walkability
)The Pilot SKI (SKI 1.0) administered in Spring 2024 consisted of fifteen problems, includingeight MCQs and seven procedural problems. The second problem set, administered in Fall 2024(SKI 2.0), included eleven problems, five MCQs, and six procedural problems. Both problemsets incorporated drawing FBDs and multi-part procedural problems, allowing us to evaluatestudents' conceptual understanding, problem-solving skills, and computational accuracy. We alsoincluded two reflective questions with each problem in both sets to assess students' self-reportedconfidence and perceived difficulty. Additionally, both problem sets concluded with tworeflective prompts asking students to (1) reflect on where/when they had learned the relevantconcepts or
influence of internships on undergraduate success in engineeringtechnology and related disciplines. While many students opt for summer classes to accelerategraduation, internships are critical for developing practical skills, understanding career paths, andbridging the gap between academic learning and industry practice. Using Kolb's ExperientialLearning Theory as its framework, the research explores how internships enhance activeexperimentation and reflective observation, helping students apply theoretical knowledge to real-world contexts. The study focuses on Architecture, Construction Engineering Technology,Electronic Engineering Technology, and Facilities Management programs, using surveys to assessstudents' perceptions of internships. It
evidence ofwork beyond expectations. This differs from a traditional rubric which pre-establishes thresholdsfor categories such as “Above Expectations” or “Meets Expectations.” In the Introduction toEnvironmental Engineering course at The Citadel, a series of “mini projects” are used to exposestudents to topics in environmental engineering practice. Each project is also aimed to allowstudents to practice each of the three ABET models of communication (visual, written, and oral).These projects are each graded using single point rubrics. This paper details the assignment andrubric structure, grade distributions for the assignments when single-point rubric grading wasused, and reflections from faculty and students on best practices for this rubric
while students have oneprimary mentor, they often experience interactions with additional members of the clinicalpractice. Student-reported data indicated an average of 50 +/- 13.6 hours of clinical contact overthe 14-week semester and reflected a range of minimum semesterly contact time of 30 hours(~2.1 hours per week) to a maximum of 80.5 hours (~8 hours per week) over the entire cohort.All students also spent one additional hour per week (total of 14 hours) for in-class reflectiveactivities on their experiences as well. In addition, weekly written assignments and in-classdiscussions allow students to collaborate and reflect on their experiences in the operating room(OR). The course also consists of three larger assignments through the
further-work section identifying additional topicareas for model implementation follows a brief discussion on student reception andeffectiveness.Literature ReviewStudents in engineering mechanics courses develop many skills, chief being conceptualawareness of how the created world works. As they pursue the true, the beautiful and the good inan objective world, student engineers demonstrate their conceptual competence throughconcurring representational competence reflected in their descriptions, diagrams, andmathematics [5], [6]. However, far too often students revert to memorized responses to idealizedcontexts rather than rationally exploring real world conditions [7]. To this end, physical modelsin the classroom are a well-established method for
Education, 2025 Integration of Nearpod to Promote Active Learning in Undergraduate-level Thermodynamics CourseAbstractThis instructional initiative in the format of a full paper highlights compelling teaching techniqueswith the integration of a web-based technology tool, ‘Nearpod’, in undergraduate-level, non-coding, engineering course ‘Thermodynamics’. This course integrates engineering concepts withquantitative problem-solving techniques. This study prioritizes evaluating students' experienceswith Nearpod rather than analyzing its impact on academic grades.An active learning classroom is essential in creating a dynamic learning environment that infusesengagement and interaction, self-assessment and reflection