update_maths_garden: Elo-style updates of person and item parameters (Maths...

In meow: Unified Framework for Computer Adaptive Testing Simulations

Elo-style updates of person and item parameters (Maths Garden).

Description

Updates both person and item parameters following Klinkenberg, Straatemeier, and van der Maas (2011), "Computer adaptive practice of Maths ability using a new item response model for on the fly ability and difficulty estimation." Learning rates are tunable through K_theta and K_b.

Usage

update_maths_garden(pers, item, R, admin, K_theta = 0.1, K_b = 0.1)

Arguments

pers	A data frame of current respondent parameter estimates.
item	A data frame of current item parameter estimates.
R	A respondent-by-item matrix of potential responses.
admin	An integer administration matrix; non-zero entries indicate administered items. See meow() for details.
K_theta	Learning rate for person ability updates. Defaults to 0.1.
K_b	Learning rate for item difficulty updates. Defaults to 0.1.

Value

A list with two entries: pers and item, the data frames of updated respondent and item parameter estimates.

Examples

data <- data_simple_1pl(N_persons = 10, N_items = 10)
admin <- matrix(0L, 10, 10)
admin[, 1:5] <- 1L
R <- matrix(data$resp$resp, nrow = 10, byrow = TRUE)
upd <- update_maths_garden(data$pers_tru, data$item_tru, R, admin)

meow documentation built on July 6, 2026, 5:11 p.m.