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CurricularAnalytics is an R package that provides comprehensive functionality for implementing a Curricular Analytics framework in university curricula.
Metric Calculations: CurricularAnalytics includes a collection of functions to calculate important metrics such as the delay factor, blocking factor, centrality, and structural complexity. These metrics provide quantitative measures of various aspects of your curriculum, helping you assess its efficiency and effectiveness.
Curriculum Graph Manipulation: This package provides intuitive functions to create, manipulate, and analyze curriculum graphs. You can easily construct curriculum graphs from your own data or existing formats and perform operations like adding or removing courses, modifying prerequisites, and more.
Visualization: CurricularAnalytics offers a range of visualization options to help you explore and present your curriculum data effectively. You can generate visual representations of curriculum graphs, highlighting important nodes and edges, to gain a visual understanding of your curriculum’s structure.
To install CurricularAnalytics, you can use the devtools package for the latest unstable version or CRAN for the latest stable version:
# unstable
devtools::install_github("Danyulll/CurricularAnalytics")
# stable
install.packages("CurricularAnalytics")Once you have installed CurricularAnalytics, you can import it into your R environment and start utilizing its functionalities. We have provided detailed documentation and examples in the vignette to help you get started quickly.
For a complete introduction to the topic of Curricular Analytics please see (Heileman et al. 2018). Currently CurricularAnalytics only implements the concepts found in the above paper. There are future plans to implement predictive models and an interactive R Shiny app based on the metrics in this package.
Heileman, Gregory L, Chaouki T Abdallah, Ahmad Slim, and Michael Hickman. 2018. “Curricular Analytics: A Framework for Quantifying the Impact of Curricular Reforms and Pedagogical Innovations.” arXiv Preprint arXiv:1811.09676.
Hickman, Michael S. 2017. “Development of a Curriculum Analysis and Simulation Library with Applications in Curricular Analytics.”
Slim, Ahmad, Gregory L Heileman, Chaouki T Abdallah, Ameer Slim, and Najem N Sirhan. 2021. “Restructuring Curricular Patterns Using Bayesian Networks.” In EDM.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.