an explanation can be found in the published dissertation. Asis traditionally followed in IRT, item fit statistics were obtained. Cut-off criteria for a reasonablefit were SRMR and RMSEA < 0.08, CFI and TLI > 0.90 or 0.95 [43]. Items with |Yen’s Q3| >0.20 (Q3 fit statistic represents the correlation between the residuals for a pair of items) has localdependence and significant item fit values (p < 0.05) revealed misfit items [44]. Finally, itemand test information functions graphically reflected the reliability (1 - [1 / peak information]) ofthe items and the test as a whole in estimating the construct over the entire scale range [45].FIGURE 3. Hypothesized 2-D measurement model for the APT-STEM instrument [12]ResultsThe results
thinking, data modeling, communication, reproducibility and ethics [11]. In a similar study [13], researchers monitored trends across Europe in order to assess thedemands for particular Data Science skills and expertise. They [13] used automated tools for theextraction of Data Science job posts as well as interviews with Data Science practitioners. Thegoal of the study [13] was to find the best practices for designing Data Science curriculum whichinclude; industry aligned, use of industry standard tools, use of real data, transferable skill set,and concise learning goals. The best practices for delivery of Data Science Curriculum includemultimodality, multi-platform, reusable, cutting-edge quality, reflective and quantified, andhands-on. In
science (statistician,computer scientist, industrial engineering, operations researchers, etc.) are in-demand and requirehighly skilled professionals with knowledge of data science, which has resulted in a highlycompetitive labor market. While the median annual salary for data scientists is quite high, about$122,000, according to the BLS, this reflects the higher educational, experience, and skill levelrequirements needed for such positions, as well as geographical differences related to keyemployer locations.Employers have recognized that data science professionals will be a critical resource to theiroperational excellence, as well as for the future of their innovation ecosystems. This need fordata science professionals has naturally driven an
learn the material and could complete the experiment without instructor intervention.Henke et al [4] used a hybrid approach where students are able to design control algorithms tocontrol electro-mechanical models in the online lab. In this format, the experiment actually takesplace, and the data reflects interactions between physical devices, not virtual entities. However,these remote web-accessible laboratories are in some respect similar to simulations in that thestudent does not have to be co-located with a particular piece of laboratory apparatus. Nedic et al.[5] developed remotely controlled labs called NetLab that allows multiple students to run anexperiment remotely in real time. Amiguid et al. [6] evaluated 100 web-based remote labs
varyconsiderably and we found no evidence of programs sharing the same assessment instruments orprotocols. A few examples are below. They describe evaluation from different viewpoints and we presentthem here to show examples of the diversity of methods employed, and some research outcomes andreflections. • One paper described the use of specific assessment methods including competency rubrics, individual development plans, and ePortfolios for evaluation (Chang, Semma, Fowler, & Arroyave, 2021). The rubrics encompassed professional and technical skills including: 1) interdisciplinary knowledge generation, 2) collaboration, 3) conflict resolution, 4) oral communication, 5) written communication, 6) self-reflection, 7
perspective, we assume the following principles: problematize status quo,look at the use of language as clues to how ways of thinking and behaviour are structured, lookfor existing mechanisms of inequality, and look for creative alternatives for a more just/equitableoutcome.First, in order to describe what mechanisms of exclusion exist and become significant in studentexperiences, we looked for student accounts of their direct experiences (e.g. of barriers to fullparticipation in engineering education). Students also reflected on their observations on thecontrast between exclusion and inclusion. This resulted in the identification of: the location ofrepresentation gap that became influential; socially-mediated mechanisms that actually lead
STEM Education (IUSE) program under Award Numbers DUE-1562773 and DUE-1525112. Any opinions, findings, and conclusions expressed in this material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation. The authorswould like to thank the reviewers for their thoughtful and encouraging feedback on improving thepaper.References [1] C. Ebert and S. Counsell, “Toward software technology 2050,” IEEE Software, vol. 34, no. 4, pp. 82–88, 2017. [2] H. Krasner, “The cost of poor quality software in the us: A 2018 report,” Consortium for IT Software Quality (CISQ), September 2018, https://cra.org/data/Generation-CS/ (retrieved August, 2020). [3] R. Florea and V. Stray, “A global view on the hard skills
level contributes to this vision. Despite some gains in recent decades, women faculty inengineering are still underrepresented. Between 2006 and 2016, the proportion of women facultyin engineering grew from 16% to 23% at the assistant level, from 11.9% to 18.3% at theassociate level, and from 3.8% to 10.6% at the full professor level [2], [3]. While the proportionof women faculty at the lower ranks has increased significantly, the limited representation ofwomen at higher faculty ranks limits their potential for reaching leadership roles andcontribution with significant decision-making to influence engineering education [4]. Althoughthe presented gains are of value, and may already reflect the effect of multiple initiativesimplemented to support
Source.Simulation of microwave transmission lines andmicrowave filters (Alabama A&M University)Participants simulate two popular microwavetransmission lines and two microwave filters.First, participants use two simulation software:AppCAD and Sonnet Lite. They learn thestructures of the two transmission lines and theoperations of two software. They use AppCAD tosimulate both transmission lines and to investigatehow the dimension parameters influence thecharacteristic impedances. They use Sonnet Liteto simulate the microstrip on reflection coefficientand insertion loss. Participants also simulate a Figure 6. Coplanar waveguide on AppCADmicrostrip band stop filter and a microstripbandpass filter using AppCAD, and then simulateboth filters using
Room IS 105Worcester Polytechnic InstituteTeaching laboratories is an essential component of chemical engineering education. They are designed to help students think criticallyabout chemical engineering principles and practices by planning and execution of experimental work followed by reflection, analysis, andinterpretation of data. However, operating teaching laboratories with social distancing measures poses significant logistical and safetychallenges, and alternative modes of delivery could be a realistic way forward in adapting engineering curricula to the post COVID-19world. This paper is aimed at identifying common approaches and strategies implemented in transforming hands-on labs into hybrid, virtualor remote operation to achieve
thinkers, students learn that the process of coming up with something new involves many trials, errors and mistakes and even failure. However, students learn that occasional failure and mistakes are part of the creative and innovative processes rather than a discouragement to an adventurous spirit. They learn to reflect on and to evaluate their experiences and to work with others to improve on those experiences, so as to come up with better or new ways of doing things.” [8]Typically, learning outcomes are used to describe knowledge and skill competencies thatstudents should attain from their learning. In innovation pedagogy learning outcomes are termed“innovation competencies” and are organized into three categories: “1) individual