Elo-style updates with paired item comparisons (Prowise Learn).

Updates both person and item parameters following Vermeiren et al. (2025), "Psychometrics of an Elo-based large-scale online learning system." Item difficulties are updated using paired comparisons of consecutively administered items, which controls the rating drift that can occur with naive Elo updates.

Usage

update_prowise_learn(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_prowise_learn(data$pers_tru, data$item_tru, R, admin)