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Use `catR`-Package for Adaptive Testing

cat_with_catR.Rmd

This example shows how to use the packages catR and ShinyItemBuilder to implement an adaptive test. We will use the tasks provided as demo03.

By default, ShinyItemBuilder is configured to give access to anyone that knows the URL.

library(shiny)
library(ShinyItemBuilder)

item_pool <- getDemoPool("demo03")
item_pool <- item_pool[1:20,]       # Currently only 20 Tasks are defined in 'ImageCat.zip'

assessment_config <- getConfig(Verbose = T)

Prepare ItemPool

For adaptive testing, item parameters are required. For demonstration purposes we use random item parameters provided for the example.

library(openxlsx)

# read item parameter from excel file

assessment_config$itemPoolDescription <- read.xlsx(
  file.path(system.file("static", package = "ShinyItemBuilder"), "IBProjects/demo03/pool.xlsx"))

it.parameters <- assessment_config$itemPoolDescription[,c("A","B","C","D")]
it.parameters <- it.parameters[1:20,]  # Currently only 20 Tasks are defined in 'ImageCat.zip'

assessment_config$bank <- it.parameters

Note that the item bank (i.e., the IRT item parameters) are stored in the configuration as assessment_config$bank.

Define Termination Criterion

In this example we use the package catR. The package expects a list that defines stop criteria (see ?catR::checkStopRule for details).

library(catR)

assessment_config$stop <- list(rule = c("length", "precision"), thr = c(10, 0.1))

Define Scoring

The CBA ItemBuilder tasks used in this example for adaptive testing are created in the following way. Each item is implemented as separate task. Each task contains one class ScoredResponse with the hit-names Item{ItemNumber}Correct, Item{ItemNumber}Wrong and Item{ItemNumber}Missing.

For adaptive testing with dichotomous IRT models, we need the response variable as 11 (solved) or 00 (not solved). Hence, we define a custom score function that assigns the value 11 to the variable solved_current_item if the (active) hit name contains the word Correct:

assessment_config$score=function(pool, session, score, current_item){

  # Score response

  solved_current_item <- ifelse(length(grep("Correct",score[score$Active,1]))>0,1,0)

  # Store score and item in history

  history_items  <- getValueForTestTaker(session, "history-items",default=list(), store = F)
  setValueForTestTaker(session, "history-items", append(history_items, current_item))

  history_scores  <- getValueForTestTaker(session, "history-scores",default=list(), store = F)
  setValueForTestTaker(session, "history-scores", append(history_scores, solved_current_item))

}

Note that the function score is also used to store the history of all selected items and scores.

Adaptive Algorithm

The adaptive algorithm is entirely implemented in the navigation-function provided to ShinyItemBuilder as part of the configuration. In the first part of the function, the history is loaded.

If the current_item==0 (i.e., navigation is called the first time), the first item is selected using the function nextItem() of the catR-package (see ?nextItem for details).

If the function navigation is called during the test (i.e., if already responses are observed), the functions thetaEst() and semTheta() of the catR-package are used to estimate the ability and the corresponding standard error. For later use, the estimated parameters are stored in the history.

Finally, the function checkStopRule() is used to check the termination criteria. Only if none of the criteria is reached, the function nextItem() is used again to select the next item.

assessment_config$navigation = function(pool, session, direction="NEXT"){

  # Load history
  
  current_item <- getValueForTestTaker(session, "current-item-in-pool", default=1, store = F)
  history_items  <- as.numeric(unlist(getValueForTestTaker(session, "history-items",default=list(), store = F)))
  history_scores  <- as.numeric(unlist(getValueForTestTaker(session, "history-scores",default=list(), store = F)))

  if ((current_item==0  && direction=="START")||
      (length(history_items) ==0)){

    # Select first item

    res <- nextItem(assessment_config$bank, theta=0, randomesque = 5, random.seed=1)
    current_item <- as.numeric(res$name)

    if (assessment_config$verbose)
      print(paste0("Selected first item: ", current_item))
  }
  else
  {
    if (direction=="NEXT"){

      # Estimate theta

      th <- thetaEst(rbind(assessment_config$bank[history_items,]), history_scores, method = "WL")
      se <- semTheta(th, rbind(assessment_config$bank[history_items,]), history_scores, method = "ML")

      # Store history

      history_th  <- getValueForTestTaker(session, "history-theta",default=list(), store = F)
      setValueForTestTaker(session, "history-theta", append(history_th, th))
      history_se  <- getValueForTestTaker(session, "history-se",default=list(), store = F)
      setValueForTestTaker(session, "history-se", append(history_se, se))

      if (assessment_config$verbose)
        print(paste0("Updated interrim estimate: theta = ", round(th,3), " (se = ", round(se,3), ")"))

      # Check test termination

      check <- checkStopRule(th = th, se = se, N = length(history_items),
                             stop = assessment_config$stop)

      if (check$decision)
      {
        current_item <- -1 # end the assessment
      }
      else
      {
        if (assessment_config$verbose)
          print(paste0("Items excluded for item selection: ", paste0(history_items, collapse = ";")))

        # Select next item

        res <- nextItem(assessment_config$bank, theta=th, out = history_items)
        current_item <- as.numeric(res$name)

        if (assessment_config$verbose)
          print(paste0("Selected item for step ", length(history_items)+1, ": ", current_item))
      }
    }
    else if (direction=="PREVIOUS"){
      # ignore
    }
    else if (direction=="CANCEL"){
      current_item <- -2 # pause the assessment
    }
  }

  setValueForTestTaker(session, "current-item-in-pool",current_item)

  current_item
}

Show CAT Results

To show the results of the adaptive test, the following end function can be used to show a plot with all administered items, the theta-history and the final ability estimate.

assessment_config$end <- function(session) {

  data <- runtime.data[[session$userData$cbasession]]
  a <- NULL
  for (d in 1:dim(data$ResultData)[1]){
    s <- parse_ib_scoring(data$ResultData[d,"Resultdata"])
    a <- rbind(a,cbind(Project=data$ResultData[d,"Project"],
                       Task=data$ResultData[d,"Task"],
                       Scope=data$ResultData[d,"Scope"],
                       s[s$Active,c("Name","ResultText")]))
  }

  history_items  <- as.numeric(unlist(getValueForTestTaker(session, "history-items",default=list(), store = F)))
  history_scores  <- as.numeric(unlist(getValueForTestTaker(session, "history-scores",default=list(), store = F)))
  history_th  <- as.numeric(unlist(getValueForTestTaker(session, "history-theta",default=list(), store = F)))
  history_se  <- as.numeric(unlist(getValueForTestTaker(session, "history-se",default=list(), store = F)))

  template <- '---
    output: html_document
    ---
    
    ```{r, echo=F}
    history_b <- assessment_config$bank[history_items,"B"]
    plot(c(1,length(history_items)+1),c(-4,4),type="n",bty="n",xlab="Step (Test length)",ylab="Theta",
         main=paste0("Estimated Ability: ", round(history_th[length(history_th)],2), " (SE ", round(history_se[length(history_se)],2), ")"))
    arrows(x0=1:length(history_items), y0=history_th-history_se, x1=1:length(history_items), y1=history_th+history_se,
           code=3, angle=90, length=0.05, col="gray")
    points(1:length(history_items), history_th,type="l")
    points(1:length(history_items), history_b,type="p", pch=history_scores+1)
    legend("topleft",inset=0.1,pch=c(1,2),c("not solved","solved"),bty="n")
     ```
'
 write(template, file=paste0(session$userData$cbasession,".rmd"), append = FALSE)

  tmpfile <- rmarkdown::render(paste0(session$userData$cbasession,".rmd"))
  b64 <- base64enc::dataURI(file = tmpfile, mime = "text/html")

  if (file.exists(tmpfile))
    file.remove(tmpfile)

  if (file.exists(paste0(session$userData$cbasession,".rmd")))
    file.remove(paste0(session$userData$cbasession,".rmd"))

  showModal(modalDialog(
    title = "Feedback",
    footer = tagList(actionButton("endActionButtonOK", "Restart")),
    renderUI({
      tags$iframe(src=b64, height=400, width=550, frameBorder=0)
    }),
  ), session)
}

Start the Test

After defining item_pool and assessment_config, the Shiny app is started using the following call:

shinyApp(assessmentOutput(pool = item_pool,
                          config = assessment_config,overwrite=T),
         renderAssessment)

About the Example Test

The example item ImageCAT.zip contains images from the following resource:

Jungjohann, J., Lutz, S., Barwasser, A., & Gebhardt, M. (2021). Bildkarten für Unterricht und Förderung. https://epub.uni-regensburg.de/49355/1/2021_Jungjohann%20et%20al_Bildkarten_fuer_Unterricht_Foerderung.pdf