Elo-style updates of person and item parameters — update_maths

This update function updates both person and item parameters according to the approach from the paper "Computer adaptive practice of Maths ability using a new item response model for on the fly ability and difficulty estimation" (Klinkenberg, Straatemeier, and van der Maas, 2011). Learning rates are tunable using supplied K_theta and K_b arguments.

Usage

update_maths_garden(pers, item, resp, K_theta = 0.1, K_b = 0.1)

Arguments

pers: A dataframe of current respondent parameter estimates.
item: A dataframe of current item parameter estimates.
resp: A long-form dataframe of only observed item responses.
K_theta: User supplied learning rate for person ability updates. Defaults to 0.1
K_b: User supplied learning rate for item difficulty updates. Defaults to 0.1

Value

An list of three objects, two of which are updated from the function input: pers is a dataframe with updated respondent parameter estimates, item is the dataframe of updated item parameter estimates. resp_cur is the dataframe of observed item responses.