Automatically check for data entry issues

Description

This function takes in a plan of study and a course, then checks for potential data entry issues. It will detect issues in formatting with the csv (such as notes creating empty rows), if there are cycles in the network, and if pre- and corequisites are appropriately defined.

Usage

admissibility_test(plan_of_study)

Arguments

Value

List of errors to correct for cycles, prereqs, and coreqs

Calculates the average sequencing in a program

Description

This function calculates the average sequencing in the program using the delay factors of the courses. The second argument, expected_time_to_degree is optional. If it is not NULL, the average sequencing will be for courses extending the student's time to degree.

Usage

average_sequencing(plan_of_study, expected_time_to_degree = NULL)

Arguments

Value

Numeric - the average sequencing in the program

Calculates the blocking factor of a course

Description

This function takes in a plan of study and a course, then finds that course's blocking factor. The value is the number of courses 'blocked' by failing the given course.

Usage

blocking_factor(plan_of_study, course, include_coreqs = TRUE)

Arguments

Value

Numeric - the blocking factor

Calculates the core collapse sequence for a plan of study

Description

This function takes in a plan of study network and constructs the "core collapse sequence." The core collapse sequence progressively removes courses from the plan of study with increasing prereq counts and calculates the proportion of courses deleted at each step. The process stops when all of the vertices have been removed. A sequence that decreases quickly to zero typically indicates that the network is generally uniform with its prereqs. A sequence with more erratic values that does not settle to zero smoothly would imply more dense sets of prereqs.

Usage

core_collapse(plan_of_study)

Arguments

Value

List of two items: (1) sequence - the core collapse sequence, (2) the associated network for each entry

Create a plan of study igraph object

Description

This function takes in a set of courses, their terms, prerequisites, and corequisites. Optional arguments include the number of credits, pass rates, lost credits from transferring, and the frequency of course offerings. The function creates an igraph structure of edges and nodes with the given qualities.

Usage

create_plan_of_study(
  Course,
  Term,
  Prereq,
  Coreq,
  Credits = NULL,
  LostCredits = NULL,
  PassRate = NULL,
  Timing = NULL,
  Institution = NULL
)

Arguments

Details

It is recommended that the user imports the data from a csv file to ensure the indices for each atomic vector correspond to the attributes of one course.

Value

An igraph object of the prerequisite structure

Calculates the cruciality of a course

Description

This function takes in a plan of study and a course, then finds that course's cruciality. The value is the sum of the blocking and delay factors of the course

Usage

cruciality(plan_of_study, course, include_coreqs = TRUE)

Arguments

Value

Numeric - the course's cruciality

Calculates the curriculum rigidity

Description

This function takes in a plan of study and then finds the curriculum's rigidity. The rigidity is the beta index of the graph, which is the number of prerequisites divided by the number of courses

Usage

curriculum_rigidity(plan_of_study)

Arguments

Value

Numeric - the curriculum rigidity

Calculates the deferment factor of a course

Description

This function takes in a plan of study and a course, then finds that course's deferment factor. The value captures the number of terms the student can fail the course before extending their time to degree.

Usage

deferment_factor(plan_of_study, course, expected_time_to_degree)

Arguments

Value

Numeric - the deferment factor

Calculates the delay factor of a course

Description

This function takes in a plan of study and a course, then finds that course's delay factor. The output is the longest path of prerequisites through the given course.

Usage

delay_factor(plan_of_study, course, include_coreqs = TRUE)

Arguments

Value

Numeric - the delay factor

Calculates the explained complexity of courses extending time to degree

Description

This function takes in the subcomplexity graph from the transfer excess courses function, then finds the transfer delay factor. The output is the proportion of complexity explained by courses extending time to degree.

Usage

explained_complexity(
  plan_of_study,
  expected_time_to_degree,
  term_weighted = FALSE
)

Arguments

Value

Numeric - the explained complexity

Finds the bottlenecks in the plan of study based on prerequisite relationships

Description

This function takes in a plan of study and three parameters. In this case, we choose min_prereq,min_postreq, and min_connections. The value of min_prereq is the minimum number of prerequisites defining a bottleneck (in the user's perspective), whereas min_postreq is the minimum number of courses the given course is a prerequisite for. Finally, min_connections is the minimum total of the number of prerequisites and the number of courses the given course is a prerequisite for. A course is a bottleneck if it meets at least one of the parameters

Usage

find_bottlenecks(
  plan_of_study,
  min_prereq = 3,
  min_postreq = 3,
  min_connections = 5,
  include_coreqs = TRUE
)

Arguments

Details

Suggested values for typical usage is find_bottleneck(x,3,3,5), which are #' provided by default. Note that min_connections >= min_prereq + min_postreq - 2. If this is violated, a warning is provided and corrected to the suggested minimum value of min_prereq + min_postreq - 2.

The output is an atomic vector of possible bottlenecks based on the user-defined parameters.

Value

atomic vector - list of courses meeting at least one condition of the three parameters

Find all possible prerequisites to a course

Description

This function takes in a plan of study and a course, then finds all the courses it is related to through its prerequisites

Usage

find_inbound_courses(plan_of_study, course)

Arguments

Value

An atomic vector of vertex ids for the course's prerequisites

Find all possible courses that depend on a particular course

Description

This function takes in a plan of study and a course, then finds all the courses it is related to through its prerequisites (after the course).

Usage

find_outbound_courses(plan_of_study, course)

Arguments

Value

An atomic vector of vertex ids for the course's following courses

Calculates inflexibility factor of a plan of study

Description

Calculates the inflexibility factor for courses that have specific offering times extending chains beyond the expected time to degree.

Usage

inflexibility_factor(plan_of_study, time_to_degree)

Arguments

Value

list of (1) a dataframe of inflexibility factors and (2) a total inflexibility factor

Plots the plan of study with courses ordered by term

Description

This function takes in a plan of study and plots it in the 'plot' window. The courses are ordered horizontally by term and vertically by the outdegree (i.e., number of prereqs) of the vertices in that column/term. The shading of the nodes corresponds to the cruciality of the course. A darker blue indicates higher cruciality while white indicates lower cruciality.

Usage

plot_plan_of_study(plan_of_study)

Arguments

Details

Note that there can be some overlap where a course is covering a path for a prereq, which may make is seem like a course is a prereq for some other course when it is in fact the course in a previous semester.

Value

Plots the plan of study in the 'plot' window

Calculates the reachability factor of a course

Description

This function takes in a plan of study and a course, then finds that course's reachability factor. The value is the number of courses needed to be passed before enrolling in the given course.

Usage

reachability_factor(plan_of_study, course, include_coreqs = TRUE)

Arguments

Value

Numeric - the reachability factor

Convert requisites to original notation

Description

This function takes in either the pre or corequisites of a plan of study as a vector, then removes any additional information like OR relationships and minimum grades such that the network can be analyzed using the traditional functions.

Usage

simplify_requisites(requisites)

Arguments

Value

vector object - A simplified vector describing the pre and corequisites (as strings)

Calculates structural complexity of a plan of study

Description

This function takes in a plan of study, then finds the plan of study's structural complexity.

Usage

structural_complexity(
  plan_of_study,
  term_weighted = FALSE,
  include_coreqs = TRUE,
  quarters = FALSE
)

Arguments

Value

list of (1) a dataframe of course crucialities, delay factors, and blocking factors; (2) a numeric value of structural complexity

Calculates the student mobility turbulence for a program

Description

This metric captures the volatility in student progression by analyzing the withdraws and major changes, which can indicate structural barriers or inefficiencies in the curriculum. This is most useful to apply to a combination of programs or a unit, like a department or college. There are two coefficients, withdrawn and changed major that can be used to prioritize either of the two causes for turbulence. They are set to 1 and 0.5 by default, respectively.

Usage

student_mobility_turbulence(
  number_withrawn,
  number_changed_major,
  total_number_of_students,
  withdrawn_coefficient = 1,
  changed_major_coefficient = 0.5
)

Arguments

Value

numeric - The student mobility turbulence

Creates a subcomplexity graph for a course

Description

This function takes in a plan of study and course, then constructs the subcomplexity graph for the course.

Usage

subcomplexity_graph(plan_of_study, course)

Arguments

Value

igraph object representing the course's subcomplexity graph.

Calculates the transfer delay factor of a course

Description

This function takes in the subcomplexity graph from the transfer excess courses function, then finds the transfer delay factor. The output is the sum of the longest paths of prerequisites through courses related to those beyond the expected time to degree.

Usage

transfer_delay_factor(plan_of_study, expected_time_to_degree)

Arguments

Value

Numeric - the transfer delay factor

Finds the subcomplexity graph of courses beyond expected time to degree

Description

This function takes in a plan of study and the expected time to degree, then outputs a subcomplexity graph that contains all of the courses beyond the time to degree and their prerequisites.

Usage

transfer_excess_courses(
  plan_of_study,
  expected_time_to_degree,
  include_coreqs = TRUE
)

Arguments

Value

igraph object - the subcomplexity graph