extract_student_info {grouper} | R Documentation |
Extract student information
Description
Converts a dataframe with information on students to a list of parameters. This list forms one half of the inputs to prepare_model(). The other half comes from extract_params_yaml.
Usage
extract_student_info(
dframe,
assignment = c("diversity", "preference"),
self_formed_groups,
demographic_cols,
skills,
pref_mat
)
Arguments
dframe |
A dataframe with one row for each student. The columns could possibly contain demographic variables, an overall skill measure, and a column indicating self-formed groups. It is best to have an id column to identify each student. |
assignment |
Character string indicating the type of model that this dataset is for. The argument is either 'preference' or 'diversity'. Partial matching is fine. |
self_formed_groups |
An integer column that identifies the self-formed groups, submitted by students. |
demographic_cols |
A set of integers indicating the columns corresponding to demographic information, e.g. major, year of study, gender, etc. This argument is only used by the diversity-based assignment. |
skills |
A numeric measure of overall skill level (higher means more skilled). This argument is only used by the diversity-based assignment. This argument can be set to NULL. If this is done, then the model used only maximises the diversity. |
pref_mat |
The preference matrix with dimensions equal to the num of groups x B*T, where T is the number of topics and B is the number of sub-groups per topic. This argument is only used in the preference-based assignment. See the Details section for more information. |
Details
For the diversity-based assignment, the demographic variables are converted
into an NxN dissimilarity matrix. By default, the dissimilarity metric used
is the Gower distance cluster::daisy()
.
For the preference-based assignment, the preference matrix indicates the preference that each group has for the project topics. For this model, each topic has possibly B sub-groups. The number of columns of this matrix must be B*T. Suppose there are T=3 topics and B=2 sub-groups per topic. Then the order of the sub-topics should be:
T1S1, T2S1, T3S1, T1S2, T2S2, and T3S2.
Note that higher values in the preference matrix reflect a greater preference for a particular topic-subtopic combination, since the objective function is set to be maximised.
Value
For the diversity-based assignment model, this function returns a list containing:
N: number of students
G: number of self-formed groups
m: a (student x groups) matrix, indicating group membership for each student.
d: dissimilarity matrix, NxN
s: skills vector for each individual student (possibly NULL)
For the preference-based assignment model, this function returns a list containing:
N: number of students
G: number of self-formed groups
m: a (student x groups) matrix, indicating group membership for each student.
n: a vector of length G, with the number of students in each self-formed group.
p: The preference matrix from the input argument.