Final Reflection on goals, self-efficacy assessment 15 Final Reflection on goals, self-efficacy assessment overall course feedback and open discussion Table 1: Course schedule and assignments by semester with new and updated content schedule.process during once-a-week class time.At the end of the Spring 2024 course, we administered a programming and streaming self-efficacysurvey. In the subsequent semester, we administered the same survey at the beginning and end ofthe course. To measure self-efficacy, we have adapted questions from Ramalingam andWiedenbeck’s Computer Programming Self-Efficacy Scale and Hiranrat et al.’s surveymeasurements for software development career [41, 42
computational thinking, engineering design, technology, and systems thinkingthrough hands-on, collaborative, student-driven projects. Camp sessions are co-facilitated by localK-12 teachers and undergraduate student mentors from the University of Florida. The GGEEprogram prioritized the hiring of undergraduate student mentors who were from the school districtshosting the camps.In this exploratory mixed methods study, undergraduate student mentor perceptions of near-peermentorship are used to assess the GGEE program’s impact on participant STEM identity andexplore the personal benefits of participation. This paper reports on the following researchquestions: 1) How does serving as near-peer mentors to K-12 student mentees in an educationalSTEM summer
. Throughout the program,participants undergo assessment via pre- and post-tests, leadership surveys, and evaluations of theirprojects.This paper examines the outcomes of the Fall 2024 cohort, with a focus on the changes in participants'understanding of electricity, hardware, and computer science. Additionally, it explores the developmentof their teamwork skills and attitudes throughout the program.The findings from the Fall 2024 implementation highlight the program's positive impact on participants'content knowledge, as evidenced by significant improvements in their comprehension of core conceptsand practical applications. Participants demonstrated enhanced proficiency in working withmicrocontrollers, designing mobile applications, and applying their
. 2. To assess the role of perceived usefulness, ease of use, and active learning principles in shaping AI adoption among students and educators. 3. To examine ethical concerns and propose strategies for the responsible integration of AI in education.Literature ReviewOverview of AI in EducationArtificial Intelligence (AI) has become a transformative force in education, enabling enhancedpersonalization, automation of administrative tasks, and innovative pedagogical methods. Theintegration of AI into education began with tools like Intelligent Tutoring Systems (ITS) in the1980s, which provided adaptive learning experiences based on student responses [11]. Since then,the adoption of AI technologies has expanded significantly
equivalency decisionsmade by receiving institution faculty may reflect program-specific concepts of rigor (A.Richardson, 2021; Senie, 2016). When sending-institution coursework is assessed asinequivalent, the transfer function of higher education contributes to the perception of a fracturedengineering education ecosystem. The assessment of coursework equivalency is particularlycentral to transfer admissions, when compared to undergraduate and graduate admissions. Incombination with other aspects of transfer admissions, the process of determining courseequivalency contributes to student experiences of increased time-to-degree and credit loss.Credit loss Credit loss occurs when a transfer student’s college coursework is not accepted by
-method approach was employed, combining surveys, focus groups, and interviews togather both quantitative and qualitative data from students, faculty, and administrators. TheGlobal Diversity and Inclusion Benchmark (GDIB) and the Motivated Strategies for LearningQuestionnaire (MSLQ) were used to assess diversity, inclusion, and engagement. Data analysiswas conducted using Excel, focusing on descriptive statistics and percentage distributions tointerpret findings.The results indicated that while the graduate engineering program at the HBCU showed strengthsin inclusiveness, such as an inclusive curriculum and support systems, there were notablechallenges regarding intercultural experiences, international students’ adaptation to weather, andlimited
and their measurablesuccesses in demonstrating the efficacy of their program model in its first iteration, the programwas not given the institutional support needed for a second iteration and left rendered obsolete byits host department. This paper presents a critical self-reflective auto-ethnography of the twograduate students in concert with a third party sociology scholar studying how universitymechanisms interact with its individual decision makers influencing the design, implementationand resulting efficacy and sustainability of university programs designed to address academicinequity and injustice. In it, we begin at an assessment of the university’s loose organizationalstructure and garbage can decision making process that leaves
GradTrack Scholars program and other mentoring circle program structures. To the author’sknowledge, this is the first study to develop a k-means clustering algorithm applied to mentoringpurposes. Future study should evaluate the comparison between manual and algorithm basedmentoring group formation in connection to assessment of mentoring group success.Introduction and Literature ReviewBackground on the GradTrack program and mentoring circlesThe GradTrack Scholars program focuses on preparing undergraduate students for graduateschool, with a specific focus on increasing access to graduate education and broadeningparticipation in engineering. The program was established in 2020 and works by developing smallmentoring circles that are units in a
syllabi, how manyaddress knowledge unit XXX?” This experiment was conducted by providing up to six individualsyllabi simultaneously (limited by the platforms and their associated context windows). A secondversion of this experiment was conducted by providing a single combined PDF document, whichincluded all 16 syllabi. This document was optimized and text-recognized using Adobe Acrobatto assist with readability by the LLM. The authors used the Policy, Legal, Ethics, and Compliance(PLE) knowledge unit, which was known to be unique to one specific syllabus, where many of theothers could have been generalized. This selection was made to help assess the accuracy of theevaluation. For ease of identification, the single combined document experiment
(Pritchard, 1969). Over time, the field has evolved and bibliometrics isnow widely used to assess research trends, publications and the relationships between authors,institutions and disciplines. It is particularly valuable for identifying research patterns,understanding collaboration dynamics and discovering emerging areas of focus within specificfields (Pessin et al., 2022). Thus, this study adopts the bibliometric technique to map and identifyresearch trends, patterns and emerging themes in the field of modular construction. Scopus wasused for this study due to its comprehensive database, which includes a vast range of peer-reviewed journals, conference papers and other scholarly articles across various disciplines.According to Aliu et al., (2023
. Muhsin Menekse, she researches how reflection on learning activities can support engineering students in engaging in their class and improving their learning achievement.Alfa Satya Putra, Purdue University at West Lafayette (COE) Alfa Satya Putra is a 3rd year PhD student at School of Engineering Education at Purdue University. He has Bachelor’s and Master’s degree in Electrical and Computer Engineering from Purdue University. Before joining the PhD program, Alfa has served as a lecturer in Indonesia. Alfa is mainly interested in investigating the implementation of reflective activities in large classrooms and assessing how reflective activities affect student learning and academic performance.Dr. Muhsin Menekse, Purdue
physical attributes, familial background/statusIn addition to the thematic analysis, the frequency with which the interviewee mentioned ordiscussed each code was analyzed quantitatively to highlight trends among participants. The totalaggregated code frequencies are found in Figure 1. Figure 1: Code frequency across all interviewsTo further understand nuances in the data among the participants, code frequencies weredisaggregated by treatment (those who received the UBelong intervention) and control (thosewho did not receive the UBelong intervention). This disaggregation was performed to assess ifthe UBelong intervention led to any variation among the interviewees’ responses as shown inFigure 2.While the overall success of
causes of cybersickness. For example, the user’s visionfades in and out when teleporting and entering new scenes to prevent visually jarring changes.Movement is restricted to a slow pace, and extraneous movement is replaced by teleportation.Some scenes were decreased in complexity to increase framerate, as some of the 3D models usedwere computationally heavy and not optimized for VR use. In addition, during the lab, studentswere given formative assessments to complete on their laptops. This was partly to engage thinkingand partly to provide a break from using the headset, as the total simulation time exceeded therecommended 30 minutes.Before VR use, students were given the instructions seen in Figure 1. Despite these precautionarymeasures, 7 out
. All these strategies notwithstanding, there are ways in which the study validity could beimproved. For purposes of triangulation, I could compare journals and interview responsesagainst focus group video of the students as they moved through the EDP. (This is planned forfuture work, discussed in the final section of this paper.) I could employ peer debriefing as ameans to cross check my analysis.Findings This section is organized into three subsections. In the first subsection, I summarize thedesign challenge for each unit, the performance of each team in the study who learned the unit,and the team’s assessment of whether or not and to what extent their first or second designs forthat unit failed. The second examines students’ exposure
study requirements, including: attending the aforementionedprofessional development; teaching their assigned engineering curriculum, along with theirregular science units, for two years; completing implementation logs after each lesson;conducting and gathering student surveys and assessments; and completing surveys and otherresearch instruments. Most teachers who applied to participate in the E4 Project were accepted,so long as they were eligible. Eligibility included that: they were currently teaching 3rd, 4th, or 5thgrade; they had not taught engineering extensively to students in the past (a few had taught someengineering design challenges, but had not explicitly used an EDP in their instruction); and thestudents that they would teach in the
assess, detect, analyze threats, while securing & protecting data & data-driven systems ü Master technical strategies, tools, techniques to secure data and information in the enterprise ü Understand & apply cybersecurity, crime, tort, & privacy law to the management of data & systems ü Understand disclosure, notification, breach, & other privacy & transparency obligations under state, federal, & international law ü Detect & identify common malicious software and attack protocols in order to assist organizations with continuously monitoring &
. Centralto the module was providing definitions of virtue and of teamwork as a virtue and implementingstrategies from an empirically-grounded framework to develop students as virtuous teamworkers. Drawing from Lamb et al. (2021), strategies included “(1) habituation through practice,(2) reflection on personal experience, (3) engagement with virtuous exemplars, (4) dialogue toincrease virtue literacy, (5) awareness of situational variables, (6) moral reminders, and (7)friendships of mutual accountability.”Teamwork-relevant outcomes were assessed using two approaches: self-report and peer-assessment. Students reported perceived embodiment of fifteen teamwork attributes forthemselves and for each of their teammates pre- and post-Project 2. The most
) throughout the semester with the instructors from QU to discusscurrent experience with the course collaboration. Some additional observations were elicitedduring these meetings.5 ResultsThis section reports results from applying the course collaboration at MTU and for comparisonincludes the corresponding results from QU. The response rates for the end-of-semester surveysat each institution are shown in Table 5. The questions from the end-of-semester surveysdiscussed in this section are the ones that address the research questions stated in Section 1. Someof the other survey questions (described in Section 4) are pertinent to our course assessment andcontinuous improvement processes and are thus not discussed here. As previously stated, thecourse
2006-374: A COGNITIVE STUDY OF MODELING DURING PROBLEM-SOLVINGThomas Litzinger, Pennsylvania State University Thomas A. Litzinger is currently Director of the Leonhard Center for the Enhancement of Engineering Education and a Professor of Mechanical Engineering at Penn State, where he has been on the faculty since 1985. His work in engineering education involves curricular reform, teaching and learning innovations, faculty development, and assessment. He can be contacted at tal2@psu.edu.Peggy Van Meter, Pennsylvania State University Peggy Van Meter is currently the Professor in Charge of the Educational Psychology Program and an Associate Professor of Education at Penn State where she has
researchers) during their first semester at UT Austin. The program,now in its third year, may eventually be implemented across multiple engineering disciplines andserve as a framework for future initiatives aimed at increasing undergraduate participation inresearch. This paper details the motivations, framework, and course content for this newlyimplemented freshman research program and provides a preliminary assessment of itseffectiveness and suggestions for improving its implementation. 2. Background The FIRE program is inspired partially by the highly recognized Freshman Research Initiative(FRI) in the College of Natural Sciences at UT Austin [11]. FRI is a 9 credit-hour program thatallows freshmen students in the natural sciences to
retention and performance, women’s success in engineering, diversity, teaching effectiveness, and collaborative learning.Dr. Beth A Myers, University of Colorado Boulder Beth A. Myers is the Director of Assessment and Accreditation at the University of Colorado Boulder. She holds a BA in biochemistry, ME in engineering management and PhD in civil engineering. Her interests are in quantitative and qualitative research and data analysis as related to equity in education.Dr. Janet Y Tsai, University of Colorado Boulder Janet Y. Tsai is a researcher and instructor in the Engineering Plus program at the University of Col- orado Boulder. Her research focuses on ways to encourage more students, especially women and those from
simulated classroom environments can be used to help inservice and preservice elementary teachers learn to lead argumentation discussions in science and engineering.Dr. Jamie Mikeska, Educational Testing Service Jamie Mikeska is a Research Scientist in the Student and Teacher Research Center at Educational Testing Service (ETS). Jamie completed her Ph.D. in the Curriculum, Teaching, and Educational Policy graduate program at Michigan State University in 2010. Her current research focuses on three key areas: (1) de- signing, developing, and conducting validation studies on assessments of content knowledge for teaching (CKT) science; (2) examining and understanding validity issues associated with measures designed to
. Enhancements can provide learning aids, such as contextual help systems and visualizations; or can increase learning productivity, such as through automation of calculation or assessment. 3. Transformation – The game allows for the inclusion of learning tasks that would not be feasible otherwise. Transformation can involve the introduction of new subject matter, teaching practices, or learning processes.Several studies have demonstrated that the RAT framework is useful in categorizing instructionaltechnology integrations with respect to how technologies modify learning tasks (e.g., Kimmonset al., 2015; Smidt et al., 2012). In this study, we applied the RAT framework to games, whichwe consider to be instructional technologies
Affiliate Associate Professor in the Earth & Space Science Department at the University of Washington and a Research Scientist/Engineer at NorthWest Research Associates. Jeremy believes that curricula should be student-centered and embedded within an engaged, collaborative community who understand the broader, societal implications of their work. He aims to achieve this through the de- sign of project-based and experiential curricula, including a recent redesign of the Computer Engineering program. He also leads ABET accreditation and coordinates assessment for the Computer Engineering program. Jeremy’s research is in space physics and electrical engineering, including atmospheric electricity, ra- dio wave
Administrative Sciences and Sociology at the Universities in Kiel, Bielefeld (Germany), and Lancaster (UK). Doctorate in Sociology from the University of Bielefeld. Worked from 1992-2000 with Academy for Technology Assessment in Baden-Wuerttemberg (Germany). Since 2000 professor for Technology Assessment and Social Science Innovation Management at University of Applied Sci- ences Darmstadt. From 2010 to 2013 Vice President for Research and Technology Transfer since 2012 Head of the Graduate School Darmstadt. c American Society for Engineering Education, 2018 The T-Shaped Engineer as an Ideal in Technology Entrepreneurship: Its Origins, History, and Significance for Engineering EducationFrom
chosendiscipline, the Department of Aerospace Engineering at Mississippi State University began amajor overhaul of its undergraduate curriculum in fall 1994 which, among many changes, led tothe creation of three freshman/sophomore "Intro-to-ASE" courses. While providing an overviewof the curriculum and activities conducted in each course, this paper discusses students' andinstructor's assessments of effectiveness of these courses and highlights apparent successes andremaining challenges.I. Introduction and BackgroundAlthough many factors influence a student's selection of a particular major in college, experienceseems to indicate that most entering freshmen have very limited knowledge or a skewedunderstanding of what their chosen disciplines entail
�on of their degrees (Berryman et al., 2015;Pesonen et al., 2020; Chrysochoou et al., 2022; Cueller et al., 2022; Riley, 2013; Stenning & Rosqvist,2021).Traits, Struggles, and StrengthsNeurodivergence as a disability is complex and while assessments use dis�nct disability categories likeau�sm, ADHD, dyslexia, and other cogni�ve differences (Cleveland Clinic, 2024), many neurodivergentstudents have overlaps among these categories (Bolourian et al., 2018). For instance, ADHD is a commoncomorbidity with au�sm though a person can be one or the other as well, meaning each person hasunique experiences, abili�es, and needs (Hours et al., 2022). For au�s�c students, many struggle withextreme sensory sensi�vi�es like hearing, smell, vision, and
exposure to civil engineering disciplines. This study introduces a blended teachingapproach, in which students are actively involved in delivering lectures on selected topics, ratherthan relying solely on the instructor. Pre-class and post-class surveys were administered to thestudent presenters to gauge their perceptions on delivering team lectures. The surveys also aimedto assess whether their knowledge improved, their roles in team presentations, and theirdevelopment of effective presentation skills. Additionally, audience feedback on the grouppresentations was collected and it was observed that the majority of students reported an increasein their knowledge after lecture delivery. This not only developed a sense of student ownershipin the
syllabi, this skill may not receive the same focus as the technical skills in practice andassessment within the course. For example, a study of teaching creativity in engineering foundmany engineering courses that had fostering creativity as a learning outcome includedassessments of convergent thinking skills like evaluation and analytic thinking but little to noassessment of the divergent skills necessary for creativity like openness to uncertainty andexploring ideas and problems [34]. This is a potential signal to students that if a skill is notassessed, it is not important and valued in the classroom, as students are motivated to learn andengage in knowledge and skills from an alignment of learning goals, activities, and assessment[35]–[37