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- Creator:
- Gradwohl, Kelsey M.
- Description:
- The data set includes one file: Dermatology Clerkship Chalk Talks Raw Dataset which is the raw data collected from the surveys. This raw data was then coded and scored with the following analysis. Objective knowledge questions were asked for each chalk talk which was scored by authors. A knowledge assessment score was calculated by adding the total number of points accumulated by the student, dividing it by the total number of points possible, and summarizing the score as a percentage. Pre- and post-talk knowledge assessment scores were compared for each chalk talk and for the entire curriculum using 2-tailed paired sample t-tests with statistical significance if p<0.05., Before and after each talk, students were asked how confident they felt differentiating conditions within each disease group. For the erythroderma and immunobullous talks, students were also asked how confident they felt working up the conditions. Answer choices were on a Likert scale ranging from 1 (not at all confident) to 5 (extremely confident). Pre- and post-chalk talk scores were summarized as means with standard deviations and compared using 2-tailed paired sample t-tests with statistical significance if p<0.05. , After each talk, students were asked about its efficacy in terms of enhancing their understanding of the diseases, providing a framework or approach to work-up, and facilitating interaction between student and teacher. Answer choices were on a Likert scale ranging from 1 (not at all effective) to 5 (extremely effective), and summarized as means with standard deviations. Students were asked for written feedback regarding what they liked about the talk and suggestions for improvement. Qualitative data were sorted into categories and scored by two independent raters (cohen's kappa =0.8)., and In the response Likert scale, "Not at all"=1, "Not so (much)"=2, "Somewhat"=3, "Very"=4, and "Extremely"=5.
- Keyword:
- Chalk talk, Dermatology clerkship, Dermatology education, Virtual learning, and Online learning
- Discipline:
- Health Sciences
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- Creator:
- Mathieu, Johanna L, Balzano, Laura, and Ledva, Gregory S
- Description:
- This data set contains the relevant time series for constructing and testing electricity load models within the related paper. The files within are a '.mat' file that contains the data and a 'readme.txt' file detailing the contents of the data.
- Keyword:
- Output feedback, Online learning, Machine learning, Real-time filtering, and Energy disaggregation
- Citation to related publication:
- Ledva, G.S., Balzano, L., Mathieu, J.L., 2018. Real-Time Energy Disaggregation of a Distribution Feeder’s Demand Using Online Learning. IEEE Trans. Power Syst. 33, 4730–4740. Accessible at https://arxiv.org/abs/1701.04389 and https://doi.org/10.1109/TPWRS.2018.2800535
- Discipline:
- Engineering