Copertina

Tools and Recommendations for Reproducible Teaching

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dogucu, Mine, Cetinkaya-Rundel, Mine
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Other Statistics Computation
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Ten simple rules for teaching data science
di: Timbers, Tiffany A., et al.
Pubblicazione: (2026)

The Current State of Undergraduate Bayesian Education and Recommendations for the Future
di: Dogucu, Mine, et al.
Pubblicazione: (2021)

A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science
di: Meyer, Elijah, et al.
Pubblicazione: (2025)

Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations
di: Seo, JooYoung, et al.
Pubblicazione: (2022)

Engaging students with statistics through choice of real data context on homework
di: Medina, Catalina, et al.
Pubblicazione: (2026)

Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses
di: Dogucu, Mine, et al.
Pubblicazione: (2021)

The Design and Implementation of a Bayesian Data Analysis Lesson for Pre-Service Mathematics and Science Teachers
di: Dogucu, Mine, et al.
Pubblicazione: (2023)

Statisticians Training STEM Educators in Statistics Methods and Pedagogy: A Case Study of Instructor Training in Bayesian Methods
di: Dogucu, Mine, et al.
Pubblicazione: (2025)

Automated grading workflows for providing personalized feedback to open-ended data science assignments
di: Ricci, Federica Zoe, et al.
Pubblicazione: (2023)

Understanding Pedagogical Content Knowledge of Data Science Instructors: An Inaugural Framework
di: Demirci, Sinem, et al.
Pubblicazione: (2025)

A Systematic Literature Review of Undergraduate Data Science Education Research
di: Dogucu, Mine, et al.
Pubblicazione: (2024)

Teaching for large-scale Reproducibility Verification
di: Vilhuber, Lars, et al.
Pubblicazione: (2022)

The Importance of Discussing Assumptions when Teaching Bootstrapping
di: Totty, Njesa, et al.
Pubblicazione: (2021)

More than Formulas -- Integrity, Communication, Computing and Reproducibility in Statistics Education
di: Furrer, Eva, et al.
Pubblicazione: (2024)

Beyond Reproducible Research: Building a Formal Representation of a Data Analysis
di: Peng, Roger D.
Pubblicazione: (2026)

A Mathematical Lens for Teaching Data Science
di: Hardin, Johanna
Pubblicazione: (2025)

Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes
di: McNamara, Amelia
Pubblicazione: (2022)

LLteacher: A Tool for the Integration of Generative AI into Statistics Assignments
di: Furfaro, Emanuela, et al.
Pubblicazione: (2025)

Perceptions and Utilization of GenAI Tools among Data Science Students and Faculty
di: Hasan, Abeer M., et al.
Pubblicazione: (2026)

Understanding and Evaluating Engineering Creativity:Development and Validation of the Engineering Creativity Assessment Tool (ECAT)
di: Akdemir-Beveridge, Zeynep G, et al.
Pubblicazione: (2025)

Efficient estimation for a smoothing thin plate spline in a two-dimensional space
di: Cavieres, Joaquin, et al.
Pubblicazione: (2024)

COMBO and COMMA: R packages for regression modeling and inference in the presence of misclassified binary mediator or outcome variables
di: Webb, Kimberly A. Hochstedler, et al.
Pubblicazione: (2025)

Examining the Interface Design of Tidyverse
di: Tanaka, Emi
Pubblicazione: (2025)

Gaussian spatial regression using the spmoran package: case study examples
di: Murakami, Daisuke
Pubblicazione: (2017)

Generative AI for Data Science 101: Coding Without Learning To Code
di: Bien, Jacob, et al.
Pubblicazione: (2024)

Score Matching Riemannian Diffusion Means
di: Rygaard, Frederik Möbius, et al.
Pubblicazione: (2025)

Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo
di: Hird, Max, et al.
Pubblicazione: (2023)

More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
di: Cha, Suman, et al.
Pubblicazione: (2026)

Finite- and Large- Sample Inference for Model and Coefficients in High-dimensional Linear Regression with Repro Samples
di: Wang, Peng, et al.
Pubblicazione: (2022)

Parameterization of Copulas and Covariance Decay of Stochastic Processes
di: Pumi, Guilherme, et al.
Pubblicazione: (2012)

A review of regularised estimation methods and cross-validation in spatiotemporal statistics
di: Otto, Philipp, et al.
Pubblicazione: (2024)

To democratize research with sensitive data, we should make synthetic data more accessible
di: van Kesteren, Erik-Jan
Pubblicazione: (2024)

Data-Driven Strategies for Detecting and Sampling Misrepresented Subgroups
di: Lancia, G., et al.
Pubblicazione: (2024)

Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
di: Liquet, Benoit, et al.
Pubblicazione: (2024)

A Comprehensive Guide to Item Recovery Using the Multidimensional Graded Response Model in R
di: Soguksu, Yesim Beril, et al.
Pubblicazione: (2024)

Pivoting the paradigm: the role of spreadsheets in K-12 data science
di: Tirschwell, Oren, et al.
Pubblicazione: (2025)

Welcome to the Statverse: A Metaverse for Data Science
di: Vallejos, Ronny, et al.
Pubblicazione: (2026)

Efficient inference of dynamic gene regulatory networks using discrete penalty
di: Ravikumar, Visweswaran, et al.
Pubblicazione: (2025)

Benchmarking AI Performance on End-to-End Data Science Projects
di: Hughes, Evelyn, et al.
Pubblicazione: (2026)

gwpcorMapper: an interactive mapping tool for exploring geographically weighted correlation and partial correlation in high-dimensional geospatial datasets
di: Percival, Joseph Emile Honour, et al.
Pubblicazione: (2021)