Data Files

Data

Note that the field SubjID is common to all data fields, and can be used to join tables.

High Level Data Files

Pretest and Posttest

Bayes net scores

Item-level Data

ACED Observables/Item Scores

Raw Responses from the Data file.

Note: First line gives the key.

Split Half Reliabilities

Form A

Form B

Data Formatted for Shiny Item Analysis

Pretest/Posttest files

Form A Selections Form A Key

Form B Selections Form B Key

Internal Bayes net data

Items

Items (Shiny Item Analysis Format)

Item Subsets

Solve Geometric Problems. These might be better called distinguish series.

Common Ratio

Both of the above scales together

Validity Criteria

Scaled Pretest

Scaled Posttest

Internal Scores

Expected Value of SGP This is the overall Bayes net score running from -1 to 1

Number Correct Number of internal items correct.

Groups (for DIF studies)

Classroom Which classroom was the student from.

Condition Study Condition

Race Coding is not available.

Academic Level Academic level of classroom.

Redone to be in Shiny’s prefered 0,1 format

Gender

Feedback Full feedback 1 versus accuracy only 1

Sequencing Adaptive 1 versus linear 0

Race Focal Group 2 Group 2 versus 7

Race Focal Group 3 Group 3 versus 7

Race Focal Group 6 Group 6 versus 7

ELL English language learners versus regular ed.

Special Ed Special versus regular ed.

Metadata

List of competencies – Has both long and short names.

Evidence-model level Q-matrix

Item-level Q-Matrix

Task Names (for ACED tasks)

R Package containing data

The ACED package available on R-universe contains many of these data files. It can be accessed as follows:

install.packages("ACED",repos="https://ralmond.r-universe.dev")
library(ACED)
data(ACED)

Building the R package

ACED.scores <- read.csv("data/bnscores.csv")
ACED.items <- read.csv("data/allItems.csv")
ACED.prePost <- read.csv("data/prepost.csv")
ACED.skillNames <- read.csv("data/skillNames.csv")
ACED.taskNames <- read.csv("data/taskNames.csv")
ACED.splitHalves <- list()
ACED.splitHalves$A <- read.csv("data/formA.csv")
ACED.splitHalves$B <- read.csv("data/formB.csv")
ACED.pretest <- list()
ACED.pretest$Araw <- read.csv("data/PretestAraw.csv")
ACED.pretest$Ascored <- read.csv("data/PretestAScored.csv")
ACED.pretest$Braw <- read.csv("data/PretestBraw.csv")
ACED.pretest$Bscored <- read.csv("data/PretestBScored.csv")
ACED.posttest <- list()
ACED.posttest$Araw <- read.csv("data/PosttestAraw.csv")
ACED.posttest$Ascored <- read.csv("data/PosttestAScored.csv")
ACED.posttest$Braw <- read.csv("data/PosttestBraw.csv")
ACED.posttest$Bscored <- read.csv("data/PosttestBScored.csv")
ACED.Qmatrix <- read.csv("data/Qmatrix.csv")
ACED.QEM <- read.csv("data/QEM.csv")
save(ACED.scores,ACED.items,ACED.prePost,ACED.skillNames,
     ACED.taskNames,ACED.splitHalves,ACED.pretest,ACED.posttest,
     ACED.Qmatrix,ACED.QEM,file="data/ACED.RData")