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:

For the preference-based assignment model, this function returns a list containing:


[Package grouper version 0.3.1 Index]