instructors.References1. R.S. Harichandran, N.O. Erdil, M-I. Carnasciali, J. Nocito-Gobel, and Q. Li, “Developing an Entrepreneurial Mindset in Engineering Students using Integrated E-learning Modules.” Adv. Eng. Educ., vol. 7, no. 1, 2018. Available: https://advances.asee.org/wp- content/uploads/vol07/issue01/Papers/AEE-Mindset-8-Harich.pdf2. N.O. Erdil, R.S. Harichandran, J. Nocito-Gobel, M.Carnasciali, and C.Q. Li, “Integrating E- Learning Modules into Engineering Courses to Develop an Entrepreneurial Mindset in Students.” in Proceedings of the 123rd ASEE Annual Conference & Exposition, New Orleans, LA, June 2016. Available: https://strategy.asee.org/25800.3. N.O. Erdil, R.S. Harichandran, J. Nocito-Gobel, C.Q. Li, and M.Carnasciali, “Impact
. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-End Factor Analysis for Speaker Verification,” IEEE Trans. Audio Speech Lang. Process., vol. 19, no. 4, pp. 788–798, May 2011, doi: 10.1109/TASL.2010.2064307.[7] L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized End-to-End Loss for Speaker Verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2018, pp. 4879–4883. doi: 10.1109/ICASSP.2018.8462665.[8] C. Wang, A. Zhang, Q. Wang, and Z. ZHU, “Fully supervised speaker diarization,” US11031017B2, Jun. 08, 2021 Accessed: Apr. 09, 2023. [Online]. Available: https://patents.google.com/patent/US11031017B2/en[9] J. K. Bergum, “Text Emotion Prediction in Browser
, doi: 10.2304/elea.2009.6.4.372.[9] B. Nkonge and L. E. Gueldenzoph, “Best practices in online education: Implications for policy and practice,” Bus. Educ. Dig., no. 15, pp. 42–53, May 2006.[10] T. A. Fuhrmann and J. Hoth, “Interactive online learning modules for engineering students based on JiTT and PI,” in SEFI 49th Annual Conference: Blended Learning in Engineering Education: Challenging, Enlightening - and Lasting?, Berlin, Germany, 2021, pp. 825–835.[11] R. S. Harichandran, M.-I. Carnasciali, N. O. Erdil, C. Q. Li, J. Nocito-Gobel, and S. D. Daniels, “Developing entrepreneurial thinking in engineering students by utilizing integrated online modules,” presented at the 2015 ASEE Annual Conference & Exposition
the course is to develop modeling and analysis abilities ofstudents for the investigation of inventory, logistics, and supply chain problems faced by today'sfirms. The specific topics include a brief introduction into inventory management systems(focusing on definitions, inventory-related costs, motivations to keep inventory, and distributionvalue analysis); deterministic inventory modeling (concentrating on economic order quantity andits extensions); stochastic inventory modeling (describing single-period and multiple-periodnewsvendor models, (q,r) policy with backlogging and lost sales as well as service levels);logistics system design (transportation-related decisions and their impact on inventorydecisions); supply chain management and
. Nadeem, “STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity,” Pew Research Center Science & Society, Apr. 01, 2021. https://www.pewresearch.org/science/2021/04/01/stem-jobs-see-uneven-progress-in- increasing-gender-racial-and-ethnic-diversity/ (accessed Feb. 04, 2023).[2] “The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics,” AAUW : Empowering Women Since 1881. https://www.aauw.org/resources/research/the-stem-gap/ (accessed Feb. 04, 2023).[3] J. Handelsman et al., “More women in science,” Science, vol. 309, no. 5738, Art. no. 5738, 2005.[4] S. E. Carrell, M. E. Page, and J. E. West, “Sex and science: How professor gender perpetuates the gender gap,” Q. J
/9781315258041.[18] S. A. Davis and R. P. Bostrom, “Training End Users: An Experimental Investigation of the Roles of the Computer Interface and Training Methods,” MIS Q., vol. 17, no. 1, p. 61, Mar. 1993, doi: 10.2307/249510.[19] S. A. Sukiman, H. Yusop, R. Mokhtar, and N. H. Jaafar, “Competition-Based Learning: Determining the Strongest Skill that Can Be Achieved Among Higher Education Learners,” Reg. Conf. Sci. Technol. Soc. Sci. (RCSTSS 2014), pp. 505–516, 2016, doi: 10.1007/978-981-10-1458-1_47.[20] G. Issa, S. M. Hussain, and H. Al-Bahadili, “Competition-Based Learning: A Model for © American Society for Engineering Education, 2023 2023 ASEE Annual
Teaching Module to Improve Student Understanding of Stakeholder Engagement Processes Within Engineering Systems Design. 57–67. https://doi.org/10.1007/978-3-319-32933-8_6Friedman, B., & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press. https://books.google.com/books?hl=en&lr=&id=8ZiWDwAAQBAJ&oi=fnd&pg=PR13&d q=value+sensitive+design+moral+imagination&ots=vchlHBMvLP&sig=FHupw7lAlTzwR _2hSj601EwARU8#v=onepage&q=value sensitive design moral imagination&f=falseFriedman, B., & Hendry, D. G. (2012). The Envisioning Cards: A Toolkit for Catalyzing Humanistic and Technical Imaginations. SIGCHI Conference on Human Factors in Computing
. 100-112, 2022.[12] O. Simpson, “Access, retention and course choice in online, open and distance learning”.European Journal of Open, Distance and E-learning, 7(1), 2004.[13] M. Scott, and D.A. Savage, “Lemons in the university: asymmetric information, academicshopping and subject selection”. Higher Education Research & Development, 41(4), pp. 1247-1261, 2022.[14] D. Bukhari, “Data science curriculum: Current scenario”. International Journal of DataMining & Knowledge Management Process, Vol. 10, 2020.[15] D. Li, E. Milonas, and Q. Zhang, “Content Analysis of Data Science Graduate Programs inthe US,” 2021 ASEE Virtual Annual Conference Content Access, 2021.[16] Z. Chen, X. Liu, and L. Shang, “Improved course recommendation algorithm
https://automeris.io/WebPlotDigitizer/ 5 https://www.youtube.com/watch?v=Mv5nqAPCKA4Figure 4: The number of hydrogen bonds per time (the left figure) and the number of amino acids partici-pating in helical (blue) and beta sheet (red) secondary structures per extension of the protein molecule (theright figure).parameters that are reported in Daggett’s paper and compare the drawn curves using the onlinegraphing calculator. How do these plotted graphs differ from the ones obtained from the nanoHubsimulations?DiscussionFollowup presentation and student Q&AWe had a followup presentation for the students who were involved with this PBL module wherethe first author did a presentation on the research efforts that lie at the intersection of
will inform future initiatives aimed at supportingthe academic journeys of female minority STEM students and ensuring their success.6.2 Initiative Two: ActivitiesActivity One: Panel Discussion and Q&A • Description: A panel discussion and Q&A session featuring minority female STEM professionals from various STEM disciplines will be organized. The objective of this panel is to allow these female STEM professionals to share the educational, professional and personal experiences, including the challenges faced and the successes achieved with female minority STEM students. • Goal: This event will provide female minority students with the opportunity to see themselves represented in the STEM fields and
datasets (pre-standard course, post-standard course, pre-honors course, post-honors course) and conductedstatistical tests to determine any differences within our datasets. significance testing, we analyzedfor any pre/post differences within and across course types via these statistical tests andconducted tests for normality via the Shapiro-Wilk Test and Q-Q plots. These normality testswere used to establish if parametric or nonparametric testing was appropriate. Nonparametrictests were used given normality testing indicated non-normal data for each variable(stakeholders, value categories, value created) in all four datasets. Thus, we applied theWilcoxon Signed Rank Test for pre/post significance testing within courses and the Mann-Whitney U Test
given questionQ, we define Cohen’s d [43], including Hedges’ small sample size correction [44], as 2 3 N 2 µpost µpre 4 5, dQ = q 2 (1) pool N 2 N 1 2 2 2 (npre 1)s2pre + (npost 1)s2post pool
under grants EEC#1929484 and #1929478. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author and do not necessarily reflect the views of theNational Science Foundation.References[1] R. L. Spitzer, K. Kroenke, J. B. Williams, and P. H. Q. P. C. S. Group, “Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study,” Jama, vol. 282, no. 18, pp. 1737–1744, 1999.[2] R. P. Cameron and D. Gusman, “The primary care PTSD screen (PC-PTSD): development and operating characteristics,” Primary Care Psychiatry, vol. 9, no. 1, pp. 9–14, 2003.[3] D. Van Dam, T. Ehring, E. Vedel, and P. M. G. Emmelkamp, “Validation of the Primary Care Posttraumatic Stress Disorder
pre-program to prepare the participants for the summer program. In thefirst year, the RET participants learned more about faculty mentors in the pre-program throughtheir bios, project descriptions, short video introductions, and more. We also hosted a virtualmeet-the-mentors session, where RET participants met with the mentors virtually, learned moreabout their research, and discussed potential project ideas. In the second year, based on the first-year evaluation feedback, we changed the pre-program to information Q&A sessions where theteachers learn about the program requirements, ask questions and share concerns, while theprogram team also learns more about the teachers, their subject areas, and their goals for theprogram.Summer Program
Change Communication.[17] J.E. DeWaters, C. Andersen A. Calderwood, and S.E. Powers, “Improving climate literacy with project-basedmodules rich in educational rigor and relevance, Journal of Geoscience Education, September 2014, 62:3, 469-484,DOI: 10.5408/13-056.1.Appendix: Full SurveyTitle: Climate Literacy and Engagement Survey[Note: Correct answers are indicated by an asterisk, unless otherwise indicated.]I. Climate Science/Mitigation Section:1) What is the greenhouse effect? [Q. 1 of Climate Literacy Quiz, Cleanet.org] a. Certain gases in the atmosphere trap heat and warm the Earth * b. Life on Earth 'exhales' gas that warms up the atmosphere c. The tilt of the Earth changes the amount of solar energy the Earth receives
projects; completing Clifton Strengths testand individual career assessment before attending the class. For weekly classes, studentsparticipate in small and large group discussions to gain an understanding of course topics.Following the 50 min lecture, students participated in the post-lecture activities such as smallgroup peer reviews for reflective writing, discussion of the application of PM skills, and Q&Awith guest lecturers (see Table 1).Table 1. Course Content Week Course Topic Learning Activity 1 Introduction Icebreaker game 2 Project Charter Building project charter for thesis/ research
and examined Q-Q plots, so non-parametric tests for significant differencesbetween the sections were performed using Kruskal-Wallis to analyze for significant differencesbetween the medians of each section. Subsequently, a post-hoc Dunn’s test with Bonferronicorrections was conducted to analyze which sections were significantly different. The sample sizefor each of the sections were as follows: Section A had a sample size of 33 (n = 33), Section B had asample size of 38 (n = 38), and Section C had a sample size of 50 (n=50).Table 2. Descriptive statistics of student engagement scores (n = 121) Measurement Total Emotional Physical Cognitive Mean 67.61 23.13
Exact Test is used with twonominal variables to find out if the proportions from one variable are different among values ofthe other [Bind & Rubin, 2020]. Due to the test's exact nature, it is more accurate than a Chi-SquareTest alone.Figure 1. Items A, G, and M relate to aspirational capital. Items B, H, and N relate to linguistic capital.Items C, I, and O relate to familial capital. Items D, J, and P relate to social capital. Items E, K, and Qrelate to navigational capital. Items F, L, and R relate to resistance capital. Additionally, Items A, B, C,D, E, and F relate to having or holding a CCW capital dimension. Items G, H, I, J, K, and L relate to adeveloping CCW capital dimension. Items M, N, O, P, Q, and R relate to not having a CCW
was run to examine if LCDLMs offered differentialbenefits or effects based on the gender of participants. Four modes of engagement were assessed:Interactive, constructive, active, and passive scores. Participants were grouped by their gender:male and female. First, we checked preliminary assumptions, and results revealed that data wasnormally distributed, as assessed by inspecting the Normal Q-Q plots. There were no univariateand multivariate outliers, as assessed by boxplot; there were linear relationships, as evaluated byscatterplot, and no multicollinearity; and variance-covariance matrices were homogeneous, asassessed by Box’s test of equality of covariance matrices (p = 0.473); variances werehomogeneous, as assessed by Levene’s Test of
, vol. 104, p. 197–214, 2019.[15] A. Pedro, Q. T. Le and C. S. Park, "Framework for integrating safety into construction methods education through interactive virtual reality," Journal of professional issues in engineering education and practice, vol. 142, p. 04015011, 2016.[16] Q. T. Le, A. Pedro and C. S. Park, "A social virtual reality based construction safety education system for experiential learning," Journal of Intelligent & Robotic Systems, vol. 79, p. 487–506, 2015.[17] C. Boton, "Supporting constructability analysis meetings with Immersive Virtual Reality- based collaborative BIM 4D simulation," Automation in Construction, vol. 96, p. 1–15, 2018.[18] R. Sacks, J. Whyte, D. Swissa, G
development of research self-efficacy in NHERI-REU participants, apre- and post- assessment was administered. A paired-samples t-test was used to determinewhether there was a statistically significant mean difference between the pre and post researchself-efficacy of REU participants. While outliers were detected (question pairs 1,5, 11, 13, and20) that were more than 1.5 box-lengths from the edge of the box in a boxplot, inspection of theirvalues did not reveal them to be extreme, and they were kept in the analysis. Since there weremore than 50 participants, the Normal Q-Q lot method was used to analyze and demonstrate thatthe difference score between question pairs was approximately normally distributed for allquestions. Further, paired samples t
://www.nsf.gov/pubs/2020/nsf20101/nsf20101.jsp (accessed Feb. 13, 2022).[2] C. Wang, J. Shen, and J. Chao, “Integrating Computational Thinking in STEM Education: A Literature Review,” International Journal of Science & Mathematics Education, vol. 20, no. 8, pp. 1949–1972, Dec. 2022, doi: 10.1007/s10763-021-10227-5.[3] X. Tang, Y. Yin, Q. Lin, R. Hadad, and X. Zhai, “Assessing computational thinking: A systematic review of empirical studies,” Computers & Education, vol. 148, p. 103798, Apr. 2020, doi: 10.1016/j.compedu.2019.103798.[4] Y. Yin, R. Hadad, X. Tang, and Q. Lin, “Improving and Assessing Computational Thinking in Maker Activities: the Integration with Physics and Engineering Learning,” J Sci Educ Technol, vol. 29
points from 1 as “None at all” to 9 as “A great deal”.Our RET site survey also adopted nine points Likert scale to allow for finer data comparison ofpre-program and post-program results. 4. Evaluation Result and Discussion 4.1 Survey Result and Discussion The developed RET site survey was used to measure the self-efficacy of teachers in summer2022. Because each cohort only has 12 teachers which are not sufficient for drawing statisticallysignificant results, we only use descriptive statistics to compare the pre-program and post-programresults, as shown in Table 2. Figure 1 illustrates the difference in percentage. Table 2. Descriptive statistics to compare the pre- and post-program results Q# Pre
gravity is point A. For balance, if p= 4 ft and q = 1 ft, the counterweight must be: A. 5 lb B. 10 lb C. 20 lb D. 40 lb E. None of the above9. The footprint for the glass tabletop is resting on twosupports is A. B. C. D.10. A star-shaped base supports a glass tabletop. Thefootprint is: A. Blue dotted big circle A B. Black dashed hexagon B C. Red small hexagon C D. Purple dotted circle D E. The star shape.11. A force F is applied to the block. The weight of theblock is W. Determine the necessary condition for the blockto tip. A. F ³ µW and F ³ 2W B. F £ µW and F ³ 0.5W C. F ³ µW and F ³ 0.5W D. F £ µW and F £ 2W12. Angle q is 300, and the coefficient of friction is 1. If thedimensions of the block are
, 2012, doi: 10.1016/j.cej.2012.07.028.[13] S. Haase, D. Yu, and T. Salmi, “Chemical Engineering Research and Design Review on hydrodynamics and mass transfer in minichannel wall reactors with gas – liquid Taylor flow,” Chemical Engineering Research and Design, vol. 113, pp. 304–329, 2016, doi: 10.1016/j.cherd.2016.06.017.[14] C. Ye, M. Dang, C. Yao, G. Chen, and Q. Yuan, “Process analysis on CO 2 absorption by monoethanolamine solutions in microchannel reactors,” Chemical Engineering Journal, vol. 225, pp. 120– 127, 2013, doi: 10.1016/j.cej.2013.03.053.[15] R. Ramezani, I. M. Bernhardsen, R. Di Felice, and H. K. Knuutila, “Physical properties and reaction kinetics of CO2 absorption into unloaded and CO2
exposure to actual data. • Establish Teach-the-teacher and inter-institutional translation documentation in the form of a webinar, self-reflection materials, best practices documentation, and shared feedback from prior professors who taught the material • Establish a website that covers the following attributes: a Q&A forum for professors, repository for educational materials, surveys, and example code tailored to AE and MATSE students, repository for community related datasets, and teach-the-teacher and inter-institutional translation documentation.A Contextualized DS Approach in MATSE and AE A review of the most prevalent and useful data-centered skills was conducted to ensure thatemerging
they were resolved. I think 2) is really hard in a 3-day workshop. The R operations aren’t intuitive and it’s probably really hard for a newbie to make much sense of them. So, I think I'd probably spend more time thinking about storyboarding how someone might use MF to approach a question. That was touched on during Thurs, but something like: To answer X, you need to know Y. You could get Y by combining Z and Q. Z is in MIDFIELD, but Q would need to come from outside. I think that might have been more helpful on Thurs am than the R exercise. But I think there are good nuggets in the R exercises. They're worthwhile, but to really understand the scripts takes time. • I think the data visualization
26-29, 2016, 15129. [Online]. Available: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiAu- u44fn8AhVfM1kFHZrZDhMQFnoECBwQAQ&url=https%3A%2F%2Fpeer.asee.org%2Fprediction- and-reflection-activities-in-a-chemical-engineering-course-fundamentals-of-heat-and-mass- transfer.pdf&usg=AOvVaw19qKmZky_hMTDhrGfNdpmq.[7] M. Chorazy and K. Klinedinst, "Learn by doing: A model for incorporating high-impact experiential learning into an undergraduate public health curriculum," Frontiers in Public Health, vol. 7, pp. 1-7, 2019, doi: https://doi.org/10.3389/fpubh.2019.00031.[8] G. Gibbs, Learning by doing: A guide to teaching and learning methods. London
) introduce the website interface/functionality/moduledesign and what optimization (or testing) techniques did the team use in 2 minutes; 3) brieflydemonstrate the workflow of the website in 1.5 minutes. Each team has a Q&A section wherethey can answer questions from other students and assessors.We invited three evaluators to grade students’ projects, including two females and one male. Theevaluators have had at least 3 years of experience working as full stack/back-end web developers.They were asked to grade the students’ presentations from five aspects: 1) the novelty of the idea;2) the technical depth; 3) the website’s design; 4) the presentation; and 5) the Q&A session. Thefinal grade for each team was 25 points, which was evenly divided
: Theme 3: Theme 4: Engineering Interactions 1 Interactions 2 Active Learning (Problem solving) (Office hours) (Q&A) (Experiential)While the importance of interactions between students and instructors is a critical element ofundergraduate education that is common to all fields and disciplines, the remaining two topicsthat emerged from topic modelling were more specific to engineering. Topic 1 emphasizedstudent preferences for more problem-solving time and practice with TAs. This relates directly tothe theme of problem-solving which is highlighted by the ABET (accreditation board forengineering and technology) student outcome #1: “an ability to identify