physically and digitallyModule 2: Process digital twin of an extrusion-based 3D printing Extrusion-based 3D printing process leverages heat to fuse the input filament and deposit it topreviously printed layers. This process involves multi-physics, including thermal, structural, andmaterials. To improve students’ learning of the “additive” concept, we let them touch the “manufacturing”process, by using “Lego bricks” to simulate how a part in being built. Students are grouped intoteam, each team is allocated 400 lego blocks, as shown in Figure 3. In this lab activity, studentsneed to use lego to build a cone layer by layer. Specifically, they will: • Plan building into layers: Students will determine how many layers to build the given
9 Black 5 2 1 Hispanic/Latino 2 4 2Limitations and Future workTracking long-term enrollment outcomes post-participation is a future focus, necessitating longerstudies and collaboration with more institutions. Assessment of the program's impact on students'academic and career choices through follow-up surveys or interviews is vital, however, ensuringscalability and replicability across diverse educational settings requires refining the program'smaterials and implementation strategies. The team is planning to employ a more comprehensiveevaluation methods such as pre- and post-program assessments and
expertise are valuable. Ultimately, the virtual workshops included 10-15 participants each; the in-person workshop had 22 community participants. We were hoping for closer to a 1:1 ratio with our class of 38 students. till, the inaugural run of the course has been completed with much success and many lessonsSlearned. As we plan to rerun this course in Fall 2024, we anticipate several changes. Initially, we aim to develop two new virtual reactor models and provide more time for students to explore those models. One goal would be to hold a community open house in the VR lab, where students would host the participants on virtual tours of the reactors in person. These tours could be a starting point for interviews
transfer KSAs from one module to another, as there are many issues leftunanswered. As mentioned above, having the students consciously transfer what they knowfrom prior modules could academically challenge them more than usual; Any intervention ofa similar nature might face the same problem. A student having an improved attitude totransfer could find it harder to transfer what they know, thus creating more barriers. Thebalance between these two elements is a topic that is without a doubt important and needs tobe explored further. Ultimately, this intervention makes for a good starting point to increasethe transfer of learning behaviours in engineering students.Future Directions (Work-in-Progress)Moving forward, this research plans to conduct
: Using Context-Adaptive Planning, 1st edition. Allyn & Bacon, 2003.[2] M. E. Huba and J. E. Freed, Learner-Centered Assessment on College Campuses: Shifting the Focus from Teaching to Learning, 1st edition. Boston: Pearson, 1999.[3] S. M. Ismail, D. R. Rahul, I. Patra, and E. Rezvani, “Formative vs. summative assessment: impacts on academic motivation, attitude toward learning, test anxiety, and self-regulation skill,” Lang. Test. Asia, vol. 12, no. 1, p. 40, Sep. 2022, doi: 10.1186/s40468-022-00191-4.[4] H. Roediger and J. Karpicke, “Test-Enhanced Learning Taking Memory Tests Improves Long-Term Retention,” Psychol. Sci., vol. 17, pp. 249–55, Apr. 2006, doi: 10.1111/j.1467- 9280.2006.01693.x.[5] K. Mate and J. Weidenhofer
Strategic, not Question is about how to plan or optimize strategic approach to problem-solving Wonderment Wonderment, not Question explores boundaries of application of question wonderment statics content, such as “What would happen if…”Table 1. Current coding scheme. Codes marked with a * indicates that this code would result inthe end of the coding process, e.g. a question that was unrelated to statics content would not becoded in any subsequent categories. ^We recognize that the definition of high-level needs to bemore explicitly defined. A next step in the code-refining process is to look at the questions thatare coded consistently as high-level conceptual and
into the Manufacturing Facilities and STEMIndustry partners served as guest speakers sharing information about their company, the productsit made, and the underlying science and engineering. Plans for the project initially called forindustry partners to allow student tours into manufacturing facilities. Due to Covid-19 andconstraints related to the age of students, a shift to virtual or video tours occurred. One of theteachers shared the importance of offering students the ability to learn about the physicalenvironment in each company, potential jobs, and how some of the products made impact theirdaily life. He offered, One of the things that hasn't happened that I would like to happen is, I would like to see the students be able
member checking. In addition, we plan tosend the survey to a large sample with a concerted (but not exclusive) recruiting focus onparticipants who do and do not bring expertise in DEI. Taken together, these efforts will bolstervalidation of our findings and serve as a check of agreement across participant groups, thusunderstanding to what extent the views captured in our dataset resonate with engineeringacademicians and practitioners across the US.AcknowledgmentThis material is based upon work supported by the National Science Foundation (NSF) underGrant No. EEC-2027519 and EEC-2027486. Any opinions, findings, conclusions orrecommendations expressed in this paper are those of the authors and do not necessarily reflectthe views of the NSF. We
classroom.The final design for the analyzer, how it will be assembled, parts to be used, etc., is beingdetermined, and up-to-date results will be presented. The geometry of the mixing chamber withattached reservoirs for adding reagents must be optimized for small samples. The plan is todesign a 3D model in SolidWorks and then cut out a prototype from an acrylic sheet with a lasercutter. The prototype will then be tested for leaks. The module itself will consist of the channelsheet glued between two other sheets, making assembly straightforward. Introduction: Over the course of the past five years, our project group has developed several Low-CostDesktop Learning Modules, or LCDLMs, for the purposes of miniaturizing and
. Kasprzyk, Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: Theory, research and practice, 2015. 70(4): p. 231.10. Vogel, D.L. and P.J. Heath, Men, masculinities, and help-seeking patterns. 2016.11. Kasprzyk, D., D.E. Montaño, and M. Fishbein, Application of an integrated behavioral model to predict condom use: a prospective study among high HIV Risk Groups 1. Journal of Applied Social Psychology, 1998. 28(17): p. 1557-1583.12. Wright, C.J., S.A. Wilson, J.H. Hammer, L.E. Hargis, M.E. Miller, and E.L. Usher, Mental health in undergraduate engineering students: Identifying facilitators and barriers to seeking help. Journal of Engineering Education
and its variations. Case studies involve, among others, the transportation problem, the assignment problem, and the allocation problem. Through mathematical modeling, it seeks to design, improve, and operate complex systems in the best possible way. Manufacturing Process Engineering EIN3398 (3 Credit Hours) This course will address the planning, development, and optimizing production system processes. It will also discuss the methods and techniques used to improve manufacturing productivity in assembly, testing, and fabrication areas, including advanced topics in networking and the protection of those systems from cybersecurity attacks. Probability and Statistics for Engineers EGN3440 (3 hours) This course applies
. This dimension relates toorganization and clarity of activities. For example, Activities in this class are clearly andcarefully planned. This positive effect aligns with the nature of adaptive lessons, particularly theorganization of the online resources and assessments. The second largest classroom environmenteffect with the adaptive lessons was Satisfaction (d = 0.36; padj = 0.007). Thus, the adaptivelessons may be associated with enhanced student satisfaction, such as The students look forwardto coming to classes. The Individualization dimension did not exhibit a shift with adaptivelearning, as expected (d = -0.01). This dimension relates to individual and differential treatment,which is a key goal of adaptive learning. However, several of
, public high school of study. We havepublished on the teacher-informed curriculum to the K12 teacher community [15]. The school ofstudy also now has a section of the library dedicated to stories of diverse scientists and engineersto support inquiry and provide resources for students outside of the science classroom in thelibrary which also houses a maker space and supports student research and projects. In addition to implementing our curriculum at the planned high school of study, we’vehad the opportunity to use the intervention developed for this project in outreach contexts [9],[10], [14], [25], and as part of an undergraduate soft robotics course [26]. We also delivered aworkshop for a broad community of teachers at the ASEE Pre
. https://doi.org/10.17226/19007.[2] D. Stokols, (2013). “Methods and Tools for Strategic Team Science.” Presented at the Planning Meeting on Interdisciplinary Science Teams, January 11, National Research Council, Washington, DC. Available: http://tvworldwide.com/events/nas/130111/[3] M. Bennett and H. Gadlin. (2012) "Collaboration and Team Science: From Theory to Practice." J. of Investigative Medicine, 60 (5):768-75. doi:10.2310/JIM.0b013e318250871d.[4] H.B. Love, B.K. Fosdick, J.E. Cross et al. “Towards Understanding the Characteristics of Successful and Unsuccessful Collaborations: A Case-based Team Science Study.” Humanities and Social Sciences Communications, 9, 371 (2022). https://doi.org/10.1057/s41599-022-01388-x[5] G
-level understanding of thedifferent algorithms taught in class and aided in recalling the materials as they prepared for theexams. In future iterations of the course, we plan to revise the language and frequency of theassignments to more effectively assess students' understanding of the theoretical concepts.1. IntroductionMachine learning courses are gaining more popularity in electrical and computer engineering(ECE) programs. They offer the students an opportunity to practice multiple concepts related toalgorithms and software programming while learning an important topic. A typical machine-learning course focusses on the theory of different machine learning algorithms during class timewhile focusing on programming and application in the
independently.This change can be problematic for mini-courses that are outside of a student’s area of interest:EE’s taking Engineering Graphics or ME’s taking Applied Programming. When combined into asingle course in the old sequence, there was significant faculty effort and collaboration tointegrate the different skills presented, but now that the topics are separated, instruction istrending toward more traditional siloed approaches.Future WorkThe data analysis for this paper has raised several additional questions that the authors plan toaddress in the future. Data will continue to be collected longitudinally to control for pandemicrelated effects. An investigation is needed to increase understanding of low success rates forstudents who start in
examined as we continue to monitor students’ motivation towards degreecompletion to ensure that students have the best possible experience and do not feel pressuredtowards degree completion.Current Status and Future WorkBy the end of the Fall 2023 semester, all CEE graduate students had met with an industrymember and were in the process of appointing graduate committees that explicitly included anindustry member. Moving forward, we plan on continuing to monitor CEE graduate studentprofessional identity development through periodic written assignments and will measurechanges in students’ motivation and perceptions of the cognitive apprenticeship components oftheir program on an annual basis. Consent forms will be distributed again to students based
2(b) – was underway with the initial aim of having an integrated Soft PLCand I/O components lab console simulator (termed the PLC System Simulator) and to have aworking prototype in the shortest period of time possible to meet the immediate needs of thecoming semester. The plan was to build the software with four major components: Fig 2. (a) The Lab PLC and Components (b) The Lab Console Simulator i) The Soft PLC should be programmed in Ladder Diagram language (should support a subset of most commonly used instructions, but include advanced instructions available in modern PLCs, such as, log, exponential and trigonometric instructions) and should be able to execute the program. ii) The ladder diagram
Mines (Mines) using pre,post, and delayed-post Perceptions of Teaching as a Profession (PTaP) surveys with control(2021 n=103; 2022 n=163) and treatment groups (2021 n=210; 2022 n=380). For each year, weran paired t-tests and determined Cohen’s D effect sizes in R on pre/post, post/delayed, andpre/delayed data sets for both groups.Outcomes: Across both years, the post-test and delayed post-test results for the treatment groupshowed that many student perceptions of the teaching profession became significantly morepositive (pre/post p<0.001) and remained more positive throughout the semester (pre/delayedand post/delayed p<0.001), regardless of their plans to pursue teaching. “Medium” and “large”effect sizes showed the practical significance
the object to learn about the different parts of theobject. The current supplemental videos provide a high-level view of the concepts, but theycould be split into smaller chunks or more targeted concepts/misconceptions to help the students.For future work, our team is focusing on developing the baseline VR/AR tool on normalsurfaces, as illustrated in this paper, the supplemental video, and the next integration of theenvironment and the video. We plan to pilot the tool in summer and fall classes this year.References[1] S. A. Sorby, N. Veurink, and S. Streiner, “Does spatial skills instruction improve STEM outcomes? The answer is ‘yes,’” Learn Individ Differ, vol. 67, pp. 209–222, Oct. 2018, doi: 10.1016/j.lindif.2018.09.001.[2] S
: 1) a biography of a STEM scientist (CVP1; e.g., “TheLife of Lise Meitner”); 2) a position statement on a STEM controversy (CVP2e.g., “Should theU.S. continue manned space exploration?”); 3) a tutorial on a STEM technique (CVP3; “What areorthogonal contrasts and degrees of freedom?”); and 4) a methodological critique of a STEM peer-reviewed research article (CVP4; e.g., critiquing the experimental plan of a research article inScience Magazine).USTEM1 and USTEM2 engage in PCSC, RM-I, RM-II, and URE. In addition, USTEM2 alsoengages in CVP1, CVP2, CVP3, and CVP4. These activities are arranged in a timeline as follows:During the summer before college, USTEM1 does PCSC, while USTEM2 does PCSC and CVP1.In the fall semester of freshman year, USTEM1
timestamps of interactions, the first author hasidentified 12 codes with sub-codes found in Table 1 as part of the video analysis and deep diveinto the actions and interactions on stream. With these codes, we also plan on analyzing thefrequency and duration of on- and off-topic conversations and problem-solving duration.Video Selection and Analysis We identified live streamers by browsing streams on Twitch in theSoftware & Game Development category and YouTube Live using the search andrecommendations feature, sampling from streamers with the most viewers to no viewers.Selecting from a varied range of viewership provides a more comprehensive view of live streamsand the types of engagement that can occur at all levels of stream popularity. We
concepts was achievedand retained. It would be especially interesting to see how students handle 3D problems in thesefollow-on courses, since the Statics course primarily focuses on 2D problems only.Preliminary results from this study have helped to identify common misconceptions, which can beused to inform the development of hands-on activities and physical demonstrations for the course.As mentioned previously, lab activities with a more qualitative focus have been successfullypiloted at Stevens. Based on positive student feedback, plans to develop additional qualitative labsand exercises are underway. Comparisons of different course implementations (with and withoutthe lab activities) can be used to evaluate the efficacy of the labs in helping
-income individuals in construction trades would aid in more resilient post-disaster reconstructionThe main problems of informal construction identified by this study are: (1) lack of structuralknowledge, reflecting a mean of 4.63; (2) lack of quality control, yielding a mean of 4.61; (3)poor or inadequate construction methods, resulting in a mean of 4.58; and (4) lack ofprofessional advice, reflecting a mean of 4.47. These results are presented in the box plots ofFigure 7. Additional problems reported by experts include unfamiliarity and non-compliancewith building codes or city ordinances, high cost of materials, health and safety concerns forimplementers, lack of economic resources, insufficient planning, and lack of
often in contrast with students’desired learning experience, as further explained in the discussion.Survey Quantitative ResultsAs summarized in Table 2, all participants used laptop computers to access Ecampus coursematerials, and 48 of the 58 participants used their phone for coursework as well. Others also useddesktop computers (23 participants) and tablets (14 participants). For content accessed via a webbrowser, Chrome was the most common browser for engaging with Ecampus course material (37participants). Next were Firefox (12) and Safari (7), followed by one user for each of Edge andOpera. For the tablet and phone users, Wi-Fi was more common than using phone plan data forconnecting with course materials, but not all respondents used Wi-Fi
the experience in the way of fieldnotes after the observationis completed. In the context of engineering and STEM education, several observations protocolshave been developed to study teaching practices and instructional effectiveness. Below wedescribe some of the most commonly used observation protocols:Teaching Dimension Observation Protocol (TDOP). Based on the instructional systems-of-practice framework, the TDOP was developed to observe course planning and classroominstruction [5], [6]. The TDOP is broken down into six dimensions of practice: teaching methods,pedagogical strategies, cognitive demand, student-teacher interactions, student engagement, andinstructional technology. Each of these dimensions has between four and 13 individual
Processing for Assisting in Writing English SentencesAbstractMany non-English speaking international students come to the United States to pursueundergraduate engineering programs. However, most of them struggle to learn and use Englishproficiently. This struggle to learn and use English poses various challenges. For example, suchstudents struggle to describe their plans and thoughts to their college peers and colleagues atwork. Also, it is mostly harder for such students to make their place in academic or industrycareers. Some of these difficulties arise because students cannot identify sentence structures ordifferences between various types of sentences in English. Writing in complete sentences is oneway to convey
future work, we plan to usedeterministic methods of partner assignment subject to this constraint, perhaps in combinationwith existing team formation software such as CATME.LimitationsOther factors, such as students’ outside relationships with their classmates, use of office hours,and outside resources may have influenced the spread of information through the class.Furthermore, we do not know how the outcome of limited student mixing would compare tomaximizing their connections with other students. For example, if the same two students workedtogether for all four design projects, would their selection remain the same throughout? Futurework should investigate these factors to better determine the impact of team formation.ConclusionSequential team
- Getting by • FALL 23 END skill levels related to this course 3. Intermediate - Generally learning objective: good at 4. Competent - Very good at CLO 3: Distinguish the focus 5. Master - Extremely good at areas in BAE disciplines to plan for degree concentration.Sense of In the BAE 200 class… Likert scale: • FALL 22 ENDBelonging 1 • I feel that I belong to the biological and 1. Strongly disagree • FALL 23 END agricultural engineering 2. Disagree
with these please!! I plan on making my own for future classes.”Figure 2. Example ENG1101 Student Learning and Emotional Journey through Unit 2Figure 3. Example ENG1101 Student Learning and Emotional Journey through Unit 3Figure 4. Example ENG1101 Student Learning and Emotional Journey through Unit 4There is a lot of data to unpack in these learning journeys, but our analysis in this WIP paper willprimarily focus on the Unit 2 learning journeys. When we started this unit in Fall 2021, oneinstructor noticed that after session 8 (S08), the students appeared to be overwhelmed and apractice day (S09) was added to the unit. This journey map reflection was implemented after thestudents completed the exam (Unit 2 demo) to see how the students