Lectures | Reading and Materials | |
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Week 1 |
Meeting 1: Introduction; qualitative research in data science education; teaching functions. Meeting 2: Teaching functions: an algebraic approach; learning goals; brainstorming on approaches. Slides. Meeting 3: Precept 1 post-mortem. Tidy data and a refresher on dplyr. Refresher/preview on dplyr |
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Week 2 |
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Week 3 |
Meeting 1: study organization Meeting 2: ggplot.R Future reading: Handbook of Computing Education Research Future reading: Open Science Collaboration, Estimating the Reproducibility of Psychological Science Future reading: Yarkoni, The generalizbility crisis |
Reading:
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Week 4 |
Meetings: Notes on field notes; cost functions and inference. Minimizing cost functions with fminb + a factory function (Rmd source). Just for fun: John von Neumann and the fly |
Reading:
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Week 5 |
Reading: |
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Week 6 |
Cost functions ⇄ inference slides, continued |
Reading: Ch. 11 of Gelman and Hill (N.B., available for free online via PU library) |
Week 7 |
Meeting 1: Hierarchical models, modelling Radon (Rmd, srrs2.dat), continued. Recording Meeting 2: Hierarchical models 2 Recording |
Reading: Gelman and Hill Ch. 14. Reading: Yarkoni, The Generalizbility Crisis |
Week 8 |
Meeting 1: Hierarchical models 3 + the generalizbility crisis. Recording Meeting 2: Algorithmic fairness. Recording Meeting 3: The Generalizability Crisis. Recording. |
Reading:
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Week 9 |
Meeting 1: p-values via simulation, p-values via sampling distributions. Recording. Meeting 2: intro to Landy et al (slides). Random-effect models for meta-analysis. Recording. Meeting 3: sampling distriubtions of coefficients, a diversion on using PCA to summarize an analyze test scores. Recording |
Reading: |
Week 10 |
Reading: |
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Week 11 |
Reading: |
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Week 12 |
Patitsas et el, Evidence that computer science grades are not bimodal, Communications of the ACM |