% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ThreeEngineeringDisciplines_Ryan.R
\docType{data}
\name{ThreeEngineeringDisciplines_Ryan}
\alias{ThreeEngineeringDisciplines_Ryan}
\title{Mechanical, Civil, and Electrical Engineering Curricula Networks}
\format{
A list of 155 'igraph' objects.
}
\source{
Collected from institutional websites, Summer 2025.
}
\usage{
ThreeEngineeringDisciplines_Ryan
}
\description{
A collection of curricular networks for three engineering disciplines across
multiple institutions in the United States. Each network is an
'igraph' object representing courses as nodes and prerequisite/corequisite
relationships as directed edges.
}
\details{
#' @section Author:
Olivia Ryan (Virginia Tech)

Context: These curricula are from 48 of the 50 institutions that awarded the
most engineering bachelor’s degrees in 2023, based on ASEE By the Numbers,
plus five additional smaller institutions used as counter cases. This data
was collected as part of Olivia Ryan’s dissertation.

Sampling: Curricular plans were collected from 53 total institutions for
Civil, Electrical, and Mechanical engineering programs (n = 155). The
curriculum plans in the dataset include information from the 2023, 2024, or
2025 catalog year, depending on which plan was most recently available. Many
curriculum plans included upper-level electives or choices throughout the
curriculum, which needed to be accounted for to represent an accurate plan of
study. A set of rules was applied to determine which electives to include.
First, curriculum plan requirements were used to determine whether electives
needed to be distributed across specialty areas, and courses were selected to
meet those requirements. Next, electives were chosen to align with common job
sectors in the field. Within these specialties, electives with the fewest or
most common prerequisites were selected to approximate a typical student
pathway. To maintain flexibility, two to three electives were left without
prerequisites, reflecting options that allow students to take courses in other
disciplines.

Data structure: The dataset is a list of 155 '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 primarily on large, research-intensive
institutions across three engineering disciplines. Users should exercise
caution when extrapolating beyond this scope. Although a systematic process
was used to determine upper-level electives, selections may not be perfectly
consistent across programs and institutions.
}
\keyword{datasets}
