Paper ID #38560A Process for Systematically Collecting Plan of Study Data forCurricular AnalyticsDr. David Reeping, University of Cincinnati Dr. David Reeping is an Assistant Professor in the Department of Engineering and Computing Education at the University of Cincinnati. He earned his Ph.D. in Engineering Education from Virginia Tech and was a National Science Foundation Graduate Research Fellow. He received his B.S. in Engineering Education with a Mathematics minor from Ohio Northern University. His main research interests include transfer student information asymmetries, threshold concepts, curricular complexity, and
Education with a Mathematics minor from Ohio Northern University. His main research interests include transfer student information asymmetries, threshold concepts, curricular complexity, and advancing quantitative and fully integrated mixed methods.Nahal Rashedi , University of Cincinnati PhD Student of Engineering Education ©American Society for Engineering Education, 2024 Analyzing Trends in Curricular Complexity and Extracting Common Curricular Design Patterns AbstractThis research paper explores how curricular design patterns can be extracted from plan of studydata systematically. Engineering is a notoriously sequential
specific, its aid in instructional and course design. The METM program curriculum offers courses that focus on Project Management,Strategic Planning and Management, Financial Resource Management, etc., that are included inthe Engineering Management Body of Knowledge (EMBOK)[3]. At the conclusion of theMETM program, students must research, design, and showcase a real-world project that requirescomprehensive application of the knowledge they have learned throughout the program, in orderto bring significant impact to the stakeholders of their chosen organizations. The Capstone course spans over two semesters, Fall (Capstone I) and Spring (CapstoneII); it was first offered in 2019, and in 2023, the fifth student cohort started their
follow with the students through PFE 3, where theindustry scenario is simulated and implemented. These courses also integrate different methodsto incentivize students to improve professional competencies on their own through the help ofqualification plans and peer feedback. For example, the courses provide opportunities forstudents to engage with local companies, encouraging connections and facilitating visits to theirpremises for face-to-face interaction with employers. Additionally, research lab visits areplanned for students to gain insight into the academic side and provide potential opportunities forthem to participate as undergraduate research students.The Qualification Plan (QP; a key activity and assignment) in PFE courses is integral
international accreditationmovements of business schools around the world, is of interest to the management of curricula assystematic processes and assessment plans that collectively demonstrate that students achievecompetences of learning for the programs in which they participate. The objective of this work is toanalyze the implementation of the management of learning process at Unisinos University’sPolytechnic School, examining its impact on the curriculum management from the programcoordinators' perspective. This implementation process was designed as a training program forcoordinators of the 19 undergrad programs involved aiming at their development as managers of theprocess as the get involved in the process itself and organized in different
Safety, Human-robot Interaction, and Engineering Education. ©American Society for Engineering Education, 2024 Enhancing Teamwork Skills in STEM Education: A Behavioral Theory-Based Approach AbstractThe ability to work in a team is one of the most important skills a college graduate can acquirefrom an educational institute. However, some students do not appropriately participate in courseprojects, making teamwork more challenging than it needs to be for others. As a result, manystudents fail to develop teamwork skills, and some become frustrated with course projects. Thisstudy adopted the Theory of Planned Behavior (TPB) to develop tools
broaddimensions related to entrepreneurship such as identifying opportunities, management, planning,decision making, and marketing [9].Researchers have generally developed ESE instruments by either leveraging existing research touse the items from existing studies or develop their own instruments. The validation of thesedeveloped instruments has been performed by factor analysis by either extracting factors throughprincipal component analysis [9], [14] or principal axis factoring [18]. Table 1 provides a summaryof articles which focus on the development of an ESE instrument. The table presents the stepsperformed in validation process used by the researchers (e.g., numbers of factors extracted afterfactor analysis, sample and sample size, number of
entrepreneurship education program at the university. Throughexploratory factor analysis, the ESE-E demonstrated a 7-factor solution. Factors includedproduct ideation, business planning, customer discovery, team and network formation, ideapitch, people and human resources, and finance. Additionally, correlational analysesdemonstrated that these seven factors were related to each other positively. This means that ifstudents are confident about one entrepreneurial-related skill described in this instrument, theyare likely to feel confident about other entrepreneurial-related skills described in the instrument.Further and interestingly, students with a growth creative mindset tended to have high self-efficacy for product ideation, team formation, and people
-regulation in action (SRA) or strategicaction (SA), is the basis of self-regulated learning (SRL). SRC is comprised of iterative andrecursive cycles of interpreting requirements, planning (e.g., resources, time, strategies),implementing cognitive processes, monitoring progress, evaluating progress against internal andexternal standards, and continually refining approaches to better achieve goals (see Figure 1)[16]. This iterative process continues until a problem is solved or the student abandons the goal.As students manage their activities in tasks, they engage in iterative cycles of strategic activity,including actively interpreting requirements (i.e., interpreting task), developing a plan of action(i.e., planning), acting on a developed plan, and
‡ Department of Computer Science • School of Information University of Arizona ? School of Computer Science Georgia Institute of TechnologyAbstractStudents in engineering programs are typically among those having the highest time-to-degree forany of the programs offered on a university campus. Keeping a cohort of students on track to-wards on-time graduation is extremely difficult given the tightly prescribed nature of engineeringprograms. Any deviation from the standard degree plan, for any reason
identified by the other model. The GPT-4 model tended to identifymore basic relationships, while manual analysis identified more nuanced relationships.Our results do not currently support using GPT-4 to automatically generate graphicalrepresentations of faculty’s mental models of assessments. However, using a human-in-the-loopprocess could help offset GPT-4’s limitations. In this paper, we will discuss plans for our futurework to improve upon GPT-4’s current performance.IntroductionAssessments are found in every engineering classroom and are an important part of our educationsystem [1]-[3]. Assessments play many different roles, including understanding studentimprovements in learning [4], acting as a tool to assist students with learning [5], [6
execution [17].The main characteristic of this stage is that the team develops the working mechanism toeffectively guide their collaborative work with strategies and plans. Continued collaboration thenleads to the fourth stage, Performing. At this stage, all members understand the expertise,position, working style, and personality of everyone to a certain degree. In addition, the teamcould prevent or even harvest from potential conflicts with constructive conversations.Adjourning is the last stage and refers to the period of time when the team disbands or finishesthe project [11-12]. After successful team experiences, teammates share feelings of sadness,express a willingness to work more in the future, recognize and appreciate the importance ofeach
engineering, non-engineering, and engineering adjacentactivities, and finally, elicit their understanding of how their goals are connected (or not connected)to the activities they participate in.Data Collection Plans – We are presently recruiting engineering students to participate in 45-60minute semi-structured interviews. These students are being recruited through institutionallistservs. After a saturation recruitment of 15-20 students, we will purposefully sample a subset of8-12 students that capture as many academic years and engineering disciplines as possible.Participants will be interviewed by the research team using our protocol. These interviews will betranscribed using the Otter AI platform. Our sample size is appropriate for deductive
career goal is to do lab employment: Students’ Engineer.” research.” career plans Competencies and “Combined with the strong set knowledge: Identify “I designed a project with of communication and competencies and knowledge another intern, which helped me leadership skills I have built, I gained related to career learn team work skills.” know I will be successful in preparation getting a Ph.D. position.” Personal and professional
data through exploratory factor analysis allows grouping teaching into related modules. Priorstudies have focused on areas such as STEM PhD students’ perceptions of their skills in relationto their career plans and self-perceptions of graduate students’ teaching skills in regard todetermining the efficacy of a teaching workshop, but prior studies have not investigated the generalself-perceptions of engineering PhD students regarding teaching [14-15].This study is a part of a bigger project focused on understanding engineering doctoral students’perceptions of their preparedness to teach. In this study, the focus is only on the design anddevelopment of the survey instrument and validated the survey instrument by exploratory factoranalysis. In a
opportunity to build another company as acontractor, but that did not go as planned either. I decided to go back to school again for the lasttime in 2021 and have been in school since then.In the Summer of 2023, I was contacted by Dr. Jaafar, with the opportunity of undertakingundergraduate research with his mentorship. I was given the grant proposal to go over to find outif I would be interested. The proposal also provided an idea of what would be expected from me,and what the research goals were. I accepted the offer since I felt that it would be beneficial tome, especially since I intend to further my studies after graduation. I also felt that my experiencedoing research in industry would help me in this regard. I was excited to start work on
statements were given, these were primarily focused on the broaderimplications given by proposals. These included impacts to specific communities or populations,systemic changes, and broad changes to the field of engineering. Most mentions of broaderimpacts were highlighted positively, as 85.8% of MO1 codes were positive comments. Oneparticipant shares their evaluation on a proposal’s broad impacts: Furthermore, the research planned in the proposal begins to help individuals understand hidden curricula mechanisms via mentoring, social support programs, and reflective/culturally relevant academic and social integration models in engineering.As shown by this quote, positive impacts of broader impacts are often highlighted, but
].Constructive feedback from mentors helps students to refine their research questions, developrobust methodologies, and critically analyze their findings [14]. Furthermore, feedback is notjust limited to academic or technical aspects; it also encompasses guidance on professionaldevelopment and career planning, significantly influencing students' future paths [15]. Thequality and frequency of feedback are key factors in the success of undergraduate researchexperiences, impacting students' confidence, motivation, and overall learning outcomes [16].Current feedback methods in URPs often involve informal discussions, written comments onwork, and periodic evaluations. However, these methods can sometimes be inconsistent andlack timely responses, which are
they could not complete the working styles assessment and final reflection,so we may need to reconsider the timeline as well.The class’s reflection assignment showed promising results. When considering what toimplement, many students considered their personal weaknesses and identified strategies toimprove as team members. Responses included, “I plan to be more decisive and set my goalsearly to be more productive”, “I plan to try to avoid being too strict with specific criteria and selfcreated deadlines, compared to in the past where I maintained a strict schedule and becameannoyed if it wasn't maintained”, and “I tend to procrastinate my work, especially if it's adifficult task, so I will try to start my work early and be more considerate of
example, Intel offers several programs forstudents to learn and solidify AI skills (Intel® Distribution of OpenVINO™ Toolkit) anddeveloping in cloud environments (Intel® Developer Cloud) [1]. For instructors, they offer acollection of lesson plans, labs, and assessments for the same curriculums mentioned [2]. In thesecond case, the company develops core products specifically meant for assisting instructors andstudents in learning. For example, Blackboard’s core product is a learning management systemfor hosting courses and handling classroom management. In addition to publishing textbooks,Pearson has developed the Mastering platform to provide interactive assessments for variouscourses and textbooks. In both of these cases, industry has an
computational essays that use text, along withcode programs, interactive diagrams, and computational tools to express an idea [7]. Theimportance of computational notebooks is to provide programming environments for developingand sharing educational materials, combining different types of resources such as text, images,and code in a single document accessible through a web browser [17]. These are specific ways inwhich the projects were scaffolded to guide students: • The tasks for each project were broken down into smaller sub-tasks. For example, as shown in Table 1 below, the sub-tasks included planning, collecting data, defining functions, performing calculations, and visualizing results. • A detailed outline or a
urbanplanning method. These approaches shift the power relationships traditionally established ininterview settings and allowed student participants to shape the direction of their interviews andstorytelling.In this paper, we first describe the central ethical and justice challenges to soliciting andengaging BIPOC students in research about their experiences. After describing the goals of thestudy, we explain two key strategies that allowed us to address these challenges in our datacollection: 1) Use of boundary objects to elicit participants narratives, and 2) the integration ofparticipatory urban planning methods.We show sample data sets to explain the ways our methods provided opportunities to learn morefrom students, to gain a comprehensive
Network Analysis (ENA)One possible strategy for analyzing the connections between these frame elements is ENA, amethod that uses coded data to find temporal connections between ideas within an individual orcommunity. Each of these codes are represented as a node in the network, and edges betweennodes represent the strength of an individual or community’s connection between those twocodes. For example, epistemic network analysis has been used to investigate how engineeringidentity emerges as students participate in a medical device company simulation [12], howstudents develop an epistemic frame when completing an urban planning simulation [13], andhow engineering values and epistemology emerge as students participate in a four-weekengineering
accrediting agencies, institutional influences of college missionsand resources, and unit-level influences of faculty, discipline, and student characteristics. Starkalso created the Contextual Filters Model that provides an overview of the various contexts thatinfluence course planning for college faculty (Lattuca & Stark, 2011; Stark, 2000; Stark et al.,1988). A study by Lund and Stains examines unique environments and contexts of departmentsin influencing STEM faculty’s teaching practices and finds that disciplinary differences exist andhave shown potential associations to level of adoption of evidence-based instructional practices(2015). Another study shows similar findings where faculty’s teaching practices differ based onthe contexts they
to the stages of self-regulated learning,i.e., planning, performance, and self-reflection.Results: Results indicate that students had prior knowledge of project management but lackedfamiliarity with the research process. Students encountered project management challenges, buteffective communication and clear goal setting were key strategies in meeting deadlines andcompleting coursework. Students valued collaboration and continuous mentoring, and the coursehad a positive impact on students' understanding and interest in research, as well as theirdevelopment of transferable skills for future practice. Overall, this study highlights theimportance of project management skills and mentorship in promoting self-regulated learningand research skills
, thispaper contributes to the ongoing discourse on the role of AI in education and its impact onfuture learning and assessment models. The findings and discussions presented here mayoffer insights for educators, policymakers, and AI developers.Methodology and findings The Fundamental Competence Exam (FCE) is a prerequisite to obtain a Bachelor ofEngineering degree and its objective is to assess students' fundamental engineeringcompetences. To give the test students need to first pass a list of courses that are part of acommon access plan that all the engineering undergraduate students take in the first two yearsof studies. This is because these courses are then assessed in the FCE. The subjects that FCE aims to assess range from
experiences in math and science and thedevelopment of postsecondary plans in STEM. In combination, the results suggest that forstudents who do not initially identify as STEM career-bound, afterschool programming may notnecessarily promote preparation for STEM careers due to an accumulation of weak math andscience school experiences and other socio-environmental influences.Index terms: engineering, high school, math self-efficacy, minoritized students, urban education I. INTRODUCTIONPerformance in math, particularly algebra, is a major barrier to student participation,enthusiasm, and success in STEM among minoritized 4 students in U.S. high schools.Furthermore, the transition between middle school and high school is a liminal and tumultuoustime for
Multilingual Board GameIntroductionSerious games are a category of games that are often used in education to provide access tocomplex systems. In past research and curriculum development, engineering teachers haveimplemented curriculum around STEM-focused games [1], such as for urban planning [2],transportation engineering [1], chemistry education [3] and computational thinking [4]. Due tothe increased interactive engagement of games compared to lecture [5], [6], [7], engineeringeducators have utilized games to positively impact students' learning. However, theseeducational games are often only available in English. Students whose first language (L1) is notEnglish may be limited in how they present their ideas to peers in these playful spaces
College, where her primary role is to coordinate data collection, interpretation and dissemination to support teaching and learning, planning and decision-makinLeah Mendelson, Harvey Mudd College Leah Mendelson is an Associate Professor of Engineering at Harvey Mudd College.Steven Santana, Harvey Mudd College ©American Society for Engineering Education, 2024 The Impact of Diaries and Reflection on Self-Assessments of Learning in a First-Year Undergraduate Engineering Design CourseAbstractThis work-in-progress (WIP) paper communicates the impact of diary and reflection activities onstudents’ self-assessments of their learning in a first-year, studio-format undergraduateengineering design course
1) that addresses lifelong learning across and between undergraduateengineering education and career trajectories.While there are numerous formulations of lifelong learning and its dimensions, we incorporatedthe Transferable Learning Orientations model [29] which has been developed in the Canadianengineering education context and is based on the Motivated Strategies for LearningQuestionnaire [30], [31] with sufficient emphasis on attitudinal dimensions of lifelong learning.We consider how immediate and long-term learner outcomes are influenced by curricularexperiences and the curriculum planned and enacted at higher levels (Planned-Enacted-Experienced curriculum; [32]–[34]) as well as individuals’ incoming characteristics anddemographics