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quantum computers and software toolchain) quantum computingresources through guided lectures and tutorials. These sessions covered: - IBM Quantum Composer: A graphical tool for building quantum circuits by dragging anddropping operations 10 . Students learned how to: 1) Visualize qubit states using interactive q-spheres and histograms; 2) Generate OpenQASM or Python code automatically from their circuits;3) Customize their workspace for optimal circuit design. - Circuit Execution on Real Quantum Hardware: Participants ran their designed circuits onactual IBM quantum systems, gaining insights into: 1) The effects of device noise on quantumcomputations; 2) Differences between simulated results and those from real quantum hardware; 3)The
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advancing engineering innovation and excellence. Thepath forward requires sustained commitment at individual and institutional levels, guided byempirical research that centers student experiences and acknowledges the full complexity ofcreating inclusive educational spaces.References[1] S. Alburakeh, “Infographic: A Snapshot of Women in Engineering Today,” The American Society of Mechanical Engineers. [Online]. Available: https://www.asme.org/topics- resources/content/infographic-a-snapshot-of-women-in-engineering-today[2] D. Riley, A. L. Pawley, J. Tucker, and G. D. Catalano, “Feminisms in engineering education: Transformative possibilities,” NWSA J., vol. 21, no. 2, pp. 21–40, 2009.[3] A. Haverkamp, M. Bothwell, D. Montfort, and Q.-L
P1 P2 P3 P4 P5 Project Specs 5 1 1 1 1 1 Tutorials 15 3 3 5 4 6 Lectures 17 2 3 4 4 4 Labs 11 2 2 3 2 2 Q/A Threads 2538 647 535 474 495 387Table 1: External data provided to each of the specialist chatbots. The one-project specialistchatbot used data from one project while the all-project specialist chatbot used the combined data.Some materials (such as tutorials) spanned multiple projects.3.3.3 System promptsThe system
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=0.890; Experiences:CFI=0.867, TLI=0.855) are on the higher side, which can be interpreted as acceptable fits.One possible shortcoming of our data that may be contributing to a poor RMSEA is our samplesize to questionnaire item ratio. Kline notes that for every q parameters in a model, there shouldbe at least 20q data points [19]. For our Values model (𝑞𝑣 = 17), this would suggest at least 340data points, and for our Experiences model (𝑞𝐸 = 23), 460 data points. Our N values, 116 and102, respectively, fall significantly short of these criteria. As such, a poor fit can be morereasonably expected, and stronger weight should be placed on the CFA and TLI indices whenconsidering the fit of the models.Outside of measures of fit, we observe that
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