stopping_criterion {mycaas} | R Documentation |
Stopping rule
Description
Rule to decide when terminate the assessment
Usage
stopping_criterion(
likelihood,
states,
termination = "likelihood_maximization",
SC = c(0.8)
)
Arguments
likelihood |
A vector of the likelihood distribution on the states in the structure. |
states |
A state-by-problem matrix representing the structure, where an element is one if the item is included in the state, and zero otherwise. |
termination |
Define and select one of the termination criteria: "likelihood_maximization" the assessment terminates when the likelihood of a knowledge state in a knowledge structure became higher of the termination criteria (Heller, and Repitsch, 2012). "items_discrimination" the assessment terminates if the marginal likelihood of all the items is outside the interval of the stopping criteria (Donadello, Spoto, Sambo, Badaloni, Granziol, Vidotto, 2017). |
SC |
The Stopping criterion for the assessment is a numeric vector of values between 0 and 1. When the "termination" parameter is "likelihood_maximization" this is a single scalar that corresponds to the likelihood that a knowledge state needed to terminates the assessment. When the "termination" parameter is "items_discrimination" this is a numeric vector of length two, the assessment terminate if the the marginal likelihood of each item is outside of the interval between the two elements. |
Value
Return TRUE if the assessment should terminates under the criteria, otherwise FALSE
References
Donadello, I., Spoto, A., Sambo, F., Badaloni, S., Granziol, U., & Vidotto, G. (2017). ATS-PD: An adaptive testing system for psychological disorders. Educational and psychological measurement, 77(5), 792-815.
Heller, J., & Repitsch, C. (2012). Exploiting prior information in stochastic knowledge assessment. Methodology.
Examples
# Consider the knowledge space and the parameters used in Brancaccio,
# de Chiusole, Stefanutti (2023) in Example 1
states<-matrix(c( 0,0,0,0,0,
0,0,0,0,1,
0,0,1,0,1,
0,0,0,1,1,
0,0,1,1,1,
1,0,1,0,1,
0,1,0,1,1,
1,0,1,1,1,
0,1,1,1,1,
1,1,0,1,1,
1,1,1,1,1), byrow=TRUE, ncol=5)
beta <-c(.004,.03,.02,.01,.007)
eta <-c(5e-06, 5e-05, 4e-05,.007,.08)
likelihood <-c(0,0,0,0,0,0,0,.49,0,0,.51)
#stopping criterion based on the likelihood mode
stopping_criterion(likelihood,states, termination="likelihood_maximization" ,SC=c(0.5))
#stopping criterion based on the items marginal probabilities
stopping_criterion(likelihood,states, termination="items_discrimination" ,SC=c(0.2,0.8))