bkt {BKT}R Documentation

Bayesian Knowledge Tracing

Description

Create a BKT (Bayesian Knowledge Tracing) model object with initial parameters. This function constructs a BKT model by taking in various parameters such as parallelization options, number of fits, random seed, and other model-specific settings. These parameters can later be modified during the fitting or cross-validation process.

Usage

bkt(
  parallel = TRUE,
  num_fits = 5,
  folds = 5,
  seed = sample(1:1e+08, 1),
  model_type = rep(FALSE, 4),
  forgets = FALSE,
  fixed = NULL,
  defaults = NULL,
  ...
)

Arguments

parallel

Logical. Indicates whether to use parallel computation. If set to TRUE, multithreading will be used to speed up model training.

num_fits

Integer. Number of fit iterations. The best model is selected from the total iterations.

folds

Integer. Number of folds used for cross-validation. This parameter is used during cross-validation to divide the data into parts.

seed

Numeric. Seed for the random number generator, which ensures reproducibility of results.

model_type

Logical vector. Specifies model variants to use. There are four possible variants: 'multilearn', 'multiprior', 'multipair', and 'multigs'. Each corresponds to a different modeling strategy.

forgets

Logical. Whether to include a forgetting factor in the model. If set to TRUE, the model will account for the possibility that learners may forget knowledge.

fixed

List. A nested list specifying which parameters to fix for specific skills during model fitting. Each skill can have certain parameters, such as "guesses" and "slips", set to TRUE (to fix) or FALSE (to let them vary). For example: list("skill_name" = list("guesses" = TRUE, "slips" = TRUE)).

defaults

List. The defaults parameter is a list that functions as a query dictionary. It is used to map column names in the data to the expected variables in the model. This helps ensure that the model can work with different datasets that may have varying column names.

...

Other parameters.

Value

A BKT model object, which can be used by other functions such as fitting the model, cross-validation, or making predictions.

Examples

model <- bkt(seed = 42, parallel = FALSE, num_fits = 1)

[Package BKT version 0.1.0 Index]