pks (version 0.6-1)

chess: Responses to Chess Problems and Knowledge Structures

Description

Held, Schrepp and Fries (1995) derive several knowledge structures for the representation of 92 responses to 16 chess problems. See Schrepp, Held and Albert (1999) for a detailed description of these problems.

Usage

data(chess)

Format

A list consisting of five components:

dst1

a state-by-problem indicator matrix representing the knowledge structure DST1.

dst3

the knowledge structure DST3.

dst4

the knowledge structure DST4.

N.R

a named integer vector. The names denote response patterns, the values denote their frequencies.

R

a person-by-problem indicator matrix representing the responses. Column names hdbgXX and grazYY identify responses collected in Heidelberg and Graz, respectively.

References

Schrepp, M., Held, T., & Albert, D. (1999). Component-based construction of surmise relations for chess problems. In D. Albert & J. Lukas (Eds.), Knowledge spaces: Theories, empirical research, and applications (pp. 41--66). Mahwah, NJ: Erlbaum.

Examples

data(chess)
chess$dst1  # knowledge structure DST1

## Precedence relation (Held et al., 1995, p. 215) and knowledge space
P <- as.binmat(c("1111011101111001",   # s
               # "0100000000000000",   # gs   mistake in Abb. 3
                 "0111010100111000",   # gs   correction
                 "0011010000011000",   # egs
                 "0011010000011000",   # eegs
                 "0000110000000000",   # cs
                 "0000010000000000",   # gcs
                 "0011011100111000",   # ts
                 "0011010100011000",   # ges
                 "1111111111111111",   # f
                 "0111010101111000",   # gf
                 "0011010000111000",   # gff
                 "0000000000010000",   # ggff
                 "0000000000001000",   # ggf
                 "0111011101111101",   # ff
                 "0111011101111011",   # tf
                 "0011010100111001"),  # tff
               as.logical = TRUE)
dimnames(P) <- list("<" = colnames(chess$R), ">" = colnames(chess$R))
K <- rbind(0L, binary_closure(t(P)))
identical(sort(as.pattern(K)),
          sort(as.pattern(chess$dst1)))

blim(chess$dst1, chess$N.R)  # Tab. 1