% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NSF_SUCCESS.R
\docType{data}
\name{NSF_SUCCESS}
\alias{NSF_SUCCESS}
\title{NSF SUCCESS Engineering Curricula Networks}
\format{
A list of 494 'igraph' objects.
}
\source{
Collected from institutional websites and the Internet Archive Wayback
Machine, Fall 2022.
}
\usage{
NSF_SUCCESS
}
\description{
A collection of curricular networks for five engineering disciplines across
multiple MIDFIELD institutions in the United States. Each network is an
'igraph' object representing courses as nodes and prerequisite/corequisite
relationships as directed edges.
}
\details{
#' @section Authors:
David Reeping (University of Cincinnati)
Nahal Rashedi
Elliot Setser
Sushant Padhye
Autri Banerjee
Emily Hodge
Levi Smith

Context: These curricula are from institutions in the Multiple-Institution
Database for Engineering Longitudinal Development (MIDFIELD), which contains
nearly two million undergraduate student records from 21 U.S. universities
spanning 1987-2024. This dataset complements MIDFIELD by capturing curricular
structure and complexity as part of the project "Studying Undergraduate
Curricular Complexity for Engineering Student Success (SUCCESS)."

Sampling: Curricular plans were collected from 13 MIDFIELD institutions
over 10 years for Mechanical, Electrical, Chemical, Civil, and Industrial
Engineering programs using institutional websites and the Internet Archive
Wayback Machine.

Data structure: The dataset is a list of 494 'igraph' objects. Each object
corresponds to a plan of study for a specific discipline, institution, and
year. Vertices represent courses with attributes course_name,
"course_code", "term", and "credits". Edges represent
prerequisite and corequisite relationships with an attribute "type" of
"prereq" or "coreq"

Limitations: The dataset focuses on large, research-intensive institutions
and five engineering disciplines. Users should exercise caution when
extrapolating beyond this scope. Minor data entry errors may exist for older
plans of study, but overall trends are robust.
}
\references{
Reeping, D., Padhye, S. M., & Rashedi, N. (2023). A process for systematically
collecting plan of study data for curricular analytics. In \emph{Proceedings of
the 2023 ASEE Annual Conference & Exposition}.

Padhye, S., Reeping, D., & Rashedi, N. (2024). Analyzing trends in curricular
complexity and extracting common curricular design patterns. In
\emph{Proceedings of the 2024 ASEE Annual Conference & Exposition} (Paper 46580).

Reeping, D., Ebrahiminejad, H., Ohland, M., Reid, K., & Rashedi, N. (2026).
Analyzing the curricular complexity of engineering programs across disciplines
and time. \emph{IEEE Transactions on Education}.

#' Rashedi, N., Reeping, D., Wei, S. (2026).
A scoping review of methods used to analyze engineering curricula quantitatively
using curricular analytics \emph{Engineering Education Review}.
}
\keyword{datasets}
