Inertia 106Thermodynamics 1072 Enthalpy of mixing ideal gases 1396 1073 Entropy of mixing ideal gases 1387 1287 Enthalpy of mixing two-non-ideal liquids 904 C A D BFig. 1. Mechanics Concept Questions (A) 4975, (B) 4976, (C) 5703, and (D) 6141 A
distributions, normality, separability,and strong correlations within the dataset. For this module, students were shown the process ofEDA using the canonical “Iris Setosa” dataset, and were then encouraged to explore the conceptusing a dataset of concrete mix designs (Figure 2). (A) (B)Figure 2. Interactive exploratory data analysis on the properties of concrete, and their correlationwith concrete compressive strength. A) histograms showing admixture quantities in kg/m3 on thex-axis against their frequency of occurrence on the y-axis. The number of bins is controllable by the user to allow visualization of distribution parameters. B) User driven visualization of the
. At the same time, we must ensurethat the student studies the material and develops critical andcreative thinking skills. “Students are early adopters and havebeen actively testing these tools,” says Johnny Chang, ateaching assistant at Stanford University pursuing a master’sdegree in computer science [4]. By learning how to use theavailable AI tools and resources, teachers can get theconfidence to navigate the challenges and seize theopportunities present and opening in this era [5].B. The Process The proposed approach lies around the central theme thatthe use of AI is welcomed and encouraged. The approach hasbeen tested twice and as with every active course, it willcontinue to be tested and refined this semester for the thirdtime. It
-cognitive engagement patterns, likely due to its token-wisebidirectional processing, which enables richer contextual representation of input features.(a) Performance of BERT-base and Llama 3.1 (b) Performance of BERT-base and Llama 3.18B on Non-Cognitive Data. 8B on Non-Cognitive + Background Data.Figure 1: Comparison of performance on Non-Cognitive Data (left) and Non-Cognitive + Back-ground Data (right) using BERT-base and Llama 3.1 8B.[RQ2]: To what extent does incorporating student background data alongside non-cognitivefeatures in students’ LE data enhance the accuracy of forecasting their lecture-basedengagement? To address RQ2, we evaluated the impact of integrating background data (NC+B)on the
in which resistors were employed were thefor two matching inputs (0,0 or 1,1) and because the simplified user consoles, or switchboards, as displayed in Figs. 3 and 4.Boolean expressions were fully composed in terms of A̅ , B̅, C̅, Current-limiting resistors of 470 Ω were placed before purpleand D̅ (all LOW signals). Table 1 displays the truth table for LEDs, which would shine when their correspondingXOR gates, where A and B are inputs, and X is the output. pushbutton was pressed, acting as visual conformation of the players’ passcode/guess. A 1500 Ω resistor was also wired toC
which force components do and do not cause a moment about the given point. a. Ability to recognize vertical forces that cause moment. b. Ability to recognize horizontal forces that cause moment. 2. Find the moment arm distance for each force component. 3. Determine the direction of each moment of force. 4. Add to find the resultant moment. Figure 1. Exam 1 problem on moments of forces.The errors identified for each of the above skills are summarized in Table 1 below. Minorcalculation errors were not included in the analysis. Table 1. Errors identified for Exam 1 problem on moments of forces. Fundamental Skill Errors Identified
two main steps.First, the registered point cloud data from RealWorks is exported to Recap Pro. This step iscrucial as it converts the point cloud data into a format compatible with Revit. Figure 4 illustratesthe (a) exterior view and (b) interior view of the scan in Recap Pro. Second, the point cloud datafrom Recap Pro is imported into Revit, where it can be viewed in different perspectives, such asplan view, elevation view, and cross-section view. (a) Exterior View (b) Interior View Figure 4 Imported Point Cloud in RecapFigure 5 presents four distinct perspectives: (a) a plan view, (b) an elevation view, (c) crosssection A, and (d) cross section B. These views can
collaborative entrepreneurship competencies should be integratedinto the curriculum. Incorporating specific subjects that target these competencies within variouscourses will enhance students' knowledge, while ensuring lathat entrepreneurship skills aredeveloped throughout their education.BIBLIOGRAPHY[1] Moscoso, B. E., and Fernández, C. J., 2023, “Modelo pedagógico para desarrollar competencias colaborativas de emprendimiento en estudiantes de administración de empresas en una universidad del Ecuador, 2022,” Ciencia Latina Revista Científica Multidisciplinar, 7(1), pp. 479–499. https://doi.org/10.37811/cl_rcm.v7i1.4405.[2] Moscoso, B. E., and Guerra, M. A., 2024, “WIP: Developing Collaborative Entrepreneurship Competencies for
pertinentto broaden the scope to examine the extent to which short-cycle programs in othernational contexts can be relevant.LimitationsLimitations of the work reported in this study include (a) the volunteer or conveniencenature of the sample, wherein students with certain characteristics (e.g., courage,curiosity, time) were more likely to volunteer than others, and (b) the language barriersthat necessitated having multiple interviewers and transcribers and led to someinconsistency in probing and/or depth of conversation from one interview to the next.This variance in interview procedures rendered a data set viable for thematic coding butweakly suited to phenomenological analyses (d) as one author had taught ten of theparticipants, this previous
logic gates thatbit ripple counter and reversible shift registers. Specifically, we implement the following logic operations: (A, B, C). Aintend to design reversible SIPO (Serial-In Parallel-Out) and schematic representation of the Toffoli gate is shown inSISO (Serial-In Serial-Out) shift registers, critical for data Figure 1, where the input-output mapping is clearlymanipulation and storage in digital circuits. By proposing a demonstrated.systematic approach to the synthesis of reversible digitalcomponents, this study aims to contribute to the advancement ofsustainable, low-power computing technologies. Keywords—Reversible logic gates, energy-efficient circuits, flip-flops, ripple counter, shift registers
for effective HSI classification by exploring and adaptability. Leveraging these strengths, we aim to addressboth standard and modified KAN approaches. Grounded in the the challenges of high dimensionality and complex structureKolmogorov-Arnold representation theorem, KANs provide a in HSI classification tasks.unique mathematical framework for adaptive function approxi-mation, capturing complex feature relationships with fewer archi- This study investigates the application of KAN Architecturetectural constraints. We evaluate KAN on benchmark datasets, with the activation function of B-Splines on popular HSIIndian Pines and Pavia University (PaviaU), analyzing the impact
Equality and Diversity to ensure the planning and implementation of relevant DEI training and educational opportunities for college faculty and staff, as well as with HR and the college leadership on initiatives to improve the recruitment and retention of diverse faculty and staff. Harris also coordinates with affinity student organizations and programs across the college including, NSBE, SHPE, and SWE to name a few, acting as secondary advisor as well as primary college contact for external affinity-based organizations. Prior to joining Drexel Engineering, Harris served six years as the Director of the Lonnie B. Harris Black Cultural Center at Oregon State University. As Director of the BCC, Harris worked collectively
results, allowing the student to absorb thematerial, and seek clarification on confusing topics. The student was advised to not focus onmemorizing the entire book. Instead, they were encouraged to explore the concepts at their ownpace and they were encouraged to ask questions about topics that they found interesting withoutworrying about initial confusion on any topics. This approach aimed to establish a solidtheoretical foundation, crucial for building upon it, and get the student interested in thesubject-matter. B. Transition to Hands-On Learning and Focus on Visual LoomingThen the student's learning path shifted to a practical approach under a PhD student's mentorship,focusing specifically on the field of visual looming. This concept
Xiaoye Michael Wanga, Jackie Liub, Timothy N. Welsha, Ariel W Chanb,* Faculty of Kinesiology & Physical Education, University of Toronto a b Department of Chemical Engineering and Applied Chemistry, Faculty of Applied Science and Engineering *corresponding author: ariel.chan@utoronto.ca AbstractThe Unit Operations Laboratory (UOL) provides chemical engineering students with hands-onexperience by applying engineering and science concepts to industry-scale equipment. The traditionalphysical lab environment has several limitations that hinder its effectiveness as a comprehensiveteaching tool
with guidance and feedback from theirproject sponsor, faculty advisor, and the capstone instructor. At the end of the spring term,project teams present their results, write a report, and participate in a poster session. B. CornerstoneAs discussed in the introduction, students frequently were unprepared for this complex teamproject, having had little to no team project experience. For this reason, we introduced thecornerstone project in 2018 to provide intermediate project experience before their senior year[2],[3]. The cornerstone sequence consists of two classes (ECE 211 and 212), preferably takenfall and winter terms of the sophomore year, but also offered in compressed form in summerterm for transfer students. These classes have two
needed for use in the ALU. 5. Memory Address Register: Temporary storage register for address of data memory to be fetched or stored. 6. Control Unit: manages the microcontroller’s operations by decoding instructions, and enabling path for data flow. 7. A and B registers: Temporary location for data to be manipulated by the ALU 8. ALU: Arithmetic Logic Unit: Processing portion of the microcontroller. Capable of adding/subtracting or performing logic operations like “ANDing” and “ORing” values found in A and B registers. 9. Carry and Zero Flag: Indicators after an ALU operation if a “Carry” or overflow has occurred, and whether the operation resulted in a value of “Zero”.III. Design
effective. While the game is still in development, the proposed design representsgreat potential to improve learning in a core engineering course.References[1] R. Austin and B. Hunter, “ict policy and implementation in education: Cases in canada, northern ireland and ireland,” European Journal of Education, vol. 48, no. 1, pp. 178– 192, Feb. 2013. doi:10.1111/ejed.12013[2] O. S. Kaya and E. Ercag, “The impact of applying challenge-based Gamification Program on students’ learning outcomes: Academic achievement, motivation and flow,” Education and Information Technologies, vol. 28, no. 8, pp. 10053–10078, Jan. 2023. doi:10.1007/s10639-023-11585-z[3] L. Jaramillo-Mediavilla, A. Basantes-Andrade, M. Cabezas-González
,” 2024.[4] A. A. D. BIA, “A creativity based goal modeling approach for accessibility of neurodivergent individuals,” 2023.[5] E. Kokinda, M. Moster, P. Rodeghero, and D. M. Boyer, “Informal learning opportunities: Neurodiversity, self-efficacy, motivation for programming interest.,” in CSEDU (2), pp. 413–426, 2024.[6] C. Bourke, “Introduction to git,” 2015.[7] B. K. Ashinoff and A. Abu-Akel, “Hyperfocus: The forgotten frontier of attention,” Psychological research, vol. 85, no. 1, pp. 1–19, 2021. 5
, 2006.[13] M. D. Koretsky et al., "For Systematic Development of Conceptests for Active Learning," in EDULEARN19 Proceedings, 2019: IATED, pp. 8882-8892.[14] A. S. Bowen, D. R. Reid, and M. Koretsky, "Development of interactive virtual laboratories to help students learn difficult concepts in thermodynamics," in 2014 ASEE annual conference & exposition, 2014, pp. 24.426. 1-24.426. 26.[15] M. A. Vigeant, M. J. Prince, K. E. Nottis, M. Koretsky, and T. W. Ekstedt, "Hands-on, screens-on, and brains-on activities for important concepts in heat transfer," in 2016 ASEE Annual Conference & Exposition, 2016.[16] J. Cook, T. Ekstedt, B. Self, and M. Koretsky, "Bridging the Gap: Computer Simulations and
history in Onshape® to confirm that the models were developed overtime. While not every model was individually verified due to course’s scale and distributedinstructional staffing, students were reminded that submission of unmodified public modelswould be considered a violation of academic integrity.AcknowledgmentThank you to the Rutgers University School of Engineering faculty and staff for supporting thecourse’s needs.References[1] E. P. Douglas, D. J. Therriault, M. B. Berry, and J. A. M. Waisome, "Comparing Engineering Students’ and Professionals’ Conceptions of Ambiguity," in 2022 IEEE Frontiers in Education Conference (FIE), 8-11 Oct. 2022 2022, pp. 1-4, doi: 10.1109/FIE56618.2022.9962415.[2] B. Jack, W. G. P. E
Anisotropic Composite Structures Under Extreme Multi-Axial Mechanical and Thermal LoadsParticipants spend the bulk of their time conducting research, but 4-8 hours each week are reservedfor professional develop activities, additional training on multiphysics software, and industry sitevisits, for example as seen in Figure 1. UCF hosts several REU sites, so participants also engagein social activities with the other students conducting summer research experiences to provide astronger social bond beyond the HYPER cohort. Other groups at UCF, like the Office of a) b) c) d) e) Figure 1: Students participate in social activities like escape rooms (a
space, it cannot be fully describedby any single process. Gajary et al. aimed to broaden the scope of convergence research (as anobject of study) by framing it as a systemic phenomenon that itself emerges from semi-autonomous systems-level interactions and transformations across different knowledge domains.Specifically, their formulation includes three ancillary systems that are each linked by processesof “inter-system feedback and synthesis” [5 p.10]. These systems are (a) collaboration systems, 4(b) inquiry systems, and (c) contextual systems—representing the interactions of (a) people, (b)research conduct, and (c) the social and physical
% 37 2.60 100.% 94.7 11.56 10.00 100 15 100 100 A 1 35% 27 06RL 1.00 81.5% 85.2 10.01 5.88 100 13 100 100 A 2 100% 5 1 91% 60 07BM 1.85 99.8% 56.9 5.97 5.69 100 28 100 77.03 A 2 100% 8 08AP 1 96% 60 1.15 73.4% 118.9 13.42 19.19 80 39 91.04 84.03 B 09LT 1 61% 60 1.25 62.4% 179.6 17.72 15.69 85 28 0 0 F 10MT 1 90% 12 2.05 100.% 23.9 3.30 4.38 100
non-technicalpowerful tools, offering a comprehensive view of affected users [17].areas and real-time monitoring capabilities [10]. Whencombined with advancements in artificial intelligence, these B. Comparison with Existing Methodstechnologies can revolutionize wildfire management [11]. Previous research has explored various AI-based wildfire detection methods, such as those using Sentinel-2 satellite imagery [2] and UAV-based multispectral imaging [4
Appendix B). Thefocus of this paper is on the first three phases.Plan: The plan phase focused on determining if case study methodology is compatible with theproposed study and forming the research questions. Based on these findings of the scopingreview, multiple-case study was chosen as the methodology. This study will examine a widearray of course types, focus on individual courses as opposed to the whole curriculum, andincorporate interviews of faculty of the courses examined. Given the varying types ofengineering courses within the curriculum (i.e., first-year, technical, elective, design, etc.), thisapproach allows for a more complex and nuanced understanding of how different courses shapethe curriculum, as each course type may require
opportunity topractice new concepts and expand their problem-solving capabilities in a low-stakesenvironment. Unfortunately, the importance of homework is often not impressed upon incomingfreshman as 56.7% of them report spending less than six hours per week working on homeworkduring their last year of high school, a behavior of which was sufficient since 97.5% had anaverage grade of an A or B [1]. The disconnect of earning good grades while not needing to putin meaningful work towards achieving them is a learned behavior which can harm students inhigher education, and it’s a difficult behavior to correct. The problem is exacerbated sinceassigned grades in high school are poor indicators of content knowledge because grades areawarded not just for
successes byrecruiting additional mentee-mentor matches and responding to the survey results by offeringmore purposeful connection points between all parties.Funding AcknowledgmentThis research is sponsored by a National Science Foundation (NSF) Broadening Participation inEngineering (BPE) Track 3 award (#22-17745). Any opinions, findings, conclusions, orrecommendations are those of only the authors and do not necessarily reflect the views of theNSF.ReferencesAmerican Society for Engineering Education. (2024). Engineering and engineering technology by the numbers, 2023. Engineering & Engineering Technology by the Numbers, 2023Buzzanell, P. M., Long, Z., Anderson, L. B., Kokini, K., & Batra, J. C. (2015). Mentoring in academe
the fundamentals required for 2D and 3D static systemanalysis and introduce 3D vectors in a statics course using social and financial designconsiderations [31]. The module presents participants with the challenge of locating the supportsfor a stayed energy generation system (nominally the balloon in Figure 1(b). A map of acommunity is provided that drives participants to consider the impact of their solution on peoplewhile they are also grappling with the ideal technical arrangement of the cables. Equilibrium of apoint in 3D space is also explored with 3D-printed pulley systems to ensure the participants havethe technical ability to solve a 3D statics problem.Styrofoam Beam DesignThe objective of the Styrofoam Beam Design project is to
information 2 Stage 1 + Circuit Analysis 3 Stage 2 + Programming + Digital DesignFor the prediction target, we classify students’ performance in ECE 301 into two categories: gradesA and B are labeled as good performance (“0”), while grades C, D, F, and W are labeled as poorperformance (“1”). By including grade C in the “poor performance” category, the program canproactively target a larger group of students for intervention, ensuring that the students who are onthe borderline receive the resources needed to improve their performance before facing academicdifficulties. With the designed stages and prediction targets, machine learning tools are applied toclassify and analyze both
,” The Journal of rheumatology, vol. 21, no. 3, p. 454—461, 3 1994. [Online]. Available: http://europepmc.org/abstract/MED/8006888[2] T. Audino, A. Pautasso, V. Bellavia, V. Carta, A. Ferrari, F. Verna, C. Grattarola, B. Iulini, M. D. Pintore, M. Bardelli, and et al., “Ticks infesting humans and associated pathogens: A cross-sectional study in a 3-year period (2017–2019) in northwest italy,” Parasites & Vectors, vol. 14, no. 1, 3 2021.[3] Unity Technologies, “Unity real-time development platform — 3d, 2d vr &; ar engine,” [online]. [Online]. Available: https://unity.com/[4] D. S. D¨uzkaya, G. Bozkurt, S. Ulupınar, G. Uysal, S. Uc¸ar, and M. Uysalol, “The effect of a cartoon and an information video about intravenous