Knowledge space theory applies prerequisite relationships between items of knowledge within a given domain for efficient adaptive assessment and training (Doignon & Falmagne, 1999). The `kstMatrix`

package implements some basic functions for working with knowledge space. Furthermore, it provides several empirically obtained knowledge spaces in form of their bases.

There is a certain overlap in fuctionality between the `kst`

and `kstMatrix`

packages, however the former uses a set representatin and the latter a matrix representation. The packages are to be seen as complementary, not as a replacement for each other.

Knowledge spaces can easily grow very large. Therefore, their bases are often used to store the knowledge spaces with reduced space requirements. `kstmatrix`

offers two functions for computing bases from spaces and vice versa.

`kmbasis()`

The `kmbasis`

function computes the basis for a given knowledge space (actually, it can be any family of sets represented by a binary matrix).

```
kmbasis(xpl$space)
#> a b c d
#> [1,] 1 0 0 0
#> [2,] 0 1 0 0
#> [3,] 1 0 1 0
#> [4,] 0 1 1 0
#> [5,] 1 1 0 1
```

`kmunionclosure()`

The `kmunionclosure`

function computes the knowledge space for a basis (mathematically spoken it computes the closure under union of the given family of sets).

```
kmunionclosure(xpl$basis)
#> a b c d
#> [1,] 0 0 0 0
#> [2,] 1 0 0 0
#> [3,] 0 1 0 0
#> [4,] 1 1 0 0
#> [5,] 1 0 1 0
#> [6,] 1 1 1 0
#> [7,] 0 1 1 0
#> [8,] 1 1 0 1
#> [9,] 1 1 1 1
```

`kmsurmiserelation()`

The `kmsurmiserelation`

function determines the surmise relation for a quasi-ordinal knowledge space. For a more general family of sets, it computes the surmise relation for the smallest quasi-ordinal knowledge space including that family.

```
kmsurmiserelation(xpl$space)
#> a b c d
#> a 1 0 0 1
#> b 0 1 0 1
#> c 0 0 1 0
#> d 0 0 0 1
```

The surmise relation can also be used to easily close a knowledge space under intersection:

```
kmunionclosure(t(kmsurmiserelation(xpl$space)))
#> a b c d
#> [1,] 0 0 0 0
#> [2,] 1 0 0 0
#> [3,] 0 1 0 0
#> [4,] 1 1 0 0
#> [5,] 0 0 1 0
#> [6,] 1 0 1 0
#> [7,] 0 1 1 0
#> [8,] 1 1 1 0
#> [9,] 1 1 0 1
#> [10,] 1 1 1 1
```

`kmiswellgraded()`

The `kmiswellgraded`

function determines whether a knowledge structure is wellgraded.

```
kmiswellgraded(xpl$space)
#> [1] TRUE
```

`kmnotions()`

The `kmnotions`

function returns a matrix specifying the notions of a knowledge strucure, i.e. the classes of equivalent items.

```
<- matrix(c(0,0,0, 1,0,0, 1,1,1), nrow = 3, byrow = TRUE)
x kmnotions(x)
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 1
#> [3,] 0 1 1
```

For a given item number, there are two trivial knowledge spaces, the maximal knowledge space representing absolutely no prerequisite relationships (the knowledge space is the power set of the item set and the basis matrix is the diagonal matrix), and the minimal knowledge space representing equivalence of all items (the knowledge space contains just the empty set and the full item set, and the basis matrix contains one line full of ’1’s).

`kmminimalspace()`

**Example:**

```
kmminimalspace(5)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0 0 0 0
#> [2,] 1 1 1 1 1
```

`kmmaximalspace()`

**Example:**

```
kmmaximalspace(4)
#> [,1] [,2] [,3] [,4]
#> [1,] 0 0 0 0
#> [2,] 1 0 0 0
#> [3,] 0 1 0 0
#> [4,] 1 1 0 0
#> [5,] 0 0 1 0
#> [6,] 1 0 1 0
#> [7,] 0 1 1 0
#> [8,] 1 1 1 0
#> [9,] 0 0 0 1
#> [10,] 1 0 0 1
#> [11,] 0 1 0 1
#> [12,] 1 1 0 1
#> [13,] 0 0 1 1
#> [14,] 1 0 1 1
#> [15,] 0 1 1 1
#> [16,] 1 1 1 1
```

`kmdist()`

The `kmdist`

function computes a frequency distribution for the distances between a data set and a knowledge space.

```
kmdist(xpl$data, xpl$space)
#> 0 1 2 3 4
#> 5 2 0 0 0
```

`kmvalidate()`

The `kmvalidate`

function returns the distance vector, the discrimination index DI, and the distance agreement coefficient DA. The discrepancy index (DI) is the mean distance; the distance agreement coefficient is the ratio between the mean distance between data and space (ddat = DI) and the mean distance between space and power set (dpot).

```
kmvalidate(xpl$data, xpl$space)
#> $dist
#> 0 1 2 3 4
#> 5 2 0 0 0
#>
#> $DI
#> [1] 0.2857143
#>
#> $DA
#> [1] 0.5714286
```

`kmsimulate()`

The `kmsimulate`

funtion provides a generation of response patterns by applying the BLIM (Basic Local Independence Model; see Doignon & Falmagne, 1999) to a given knowledge structure. The `beta`

and `eta`

parameters of the BLIM can each be either a vector specifying different values for each item or a single numerical where `beta`

or `eta`

is assumed to be equal for all items.

```
kmsimulate(xpl$space, 10, 0.2, 0.1)
#> a b c d
#> [1,] 0 0 1 0
#> [2,] 0 1 0 0
#> [3,] 0 1 1 1
#> [4,] 0 1 0 0
#> [5,] 1 1 0 1
#> [6,] 0 1 0 1
#> [7,] 1 1 1 1
#> [8,] 1 0 1 0
#> [9,] 1 1 1 0
#> [10,] 0 0 0 0
kmsimulate(xpl$space, 10, c(0.2, 0.25, 0.15, 0.2), c(0.1, 0.15, 0.05, 0.1))
#> a b c d
#> [1,] 1 1 1 0
#> [2,] 1 1 0 1
#> [3,] 1 1 0 1
#> [4,] 0 0 0 0
#> [5,] 1 1 0 1
#> [6,] 0 1 0 0
#> [7,] 1 0 0 1
#> [8,] 1 1 0 1
#> [9,] 1 1 1 1
#> [10,] 0 0 0 0
kmsimulate(xpl$space, 10, c(0.2, 0.25, 0.15, 0.2), 0)
#> a b c d
#> [1,] 0 1 1 0
#> [2,] 0 1 0 1
#> [3,] 1 0 0 0
#> [4,] 0 0 0 0
#> [5,] 1 0 0 0
#> [6,] 1 0 0 0
#> [7,] 1 1 0 0
#> [8,] 1 1 0 0
#> [9,] 1 0 0 0
#> [10,] 1 0 0 0
```

`kmneighbourhood()`

The `kmneighbourhood`

function determines the neighbourhood of a state in a knowledge structure, i.e. the family of all states with a symmetric set diference of 1.

```
kmneighbourhood(c(1,1,0,0), xpl$space)
#> a b c d
#> [1,] 1 0 0 0
#> [2,] 0 1 0 0
#> [3,] 1 1 1 0
#> [4,] 1 1 0 1
```

`kmfringe()`

The `kmfringe`

function determines the fringe of a knowledge state, i.e. the set of thse items by which the state differs from its neighbouring states.

```
kmfringe(c(1,0,0,0), xpl$space)
#> a b c d
#> 1 1 1 0
```

`kmsymmsetdiff()`

The `kmsymmsetdiff`

function returns the symmetric set difference between two sets represented as binary vectors.

```
kmsymmsetdiff(c(1,0,0), c(1,1,0))
#> [1] 0 1 0
```

`kmsetdistance()`

The `kmsetdistance`

function returns the cardinality of the symmetric set difference between two sets represented as binary vectors.

```
kmsetdistance(c(1,0,0), c(1,1,0))
#> [1] 1
```

`kmhasse()`

and `kmcolors()`

The `kmhasse`

function draws a Hasse diagram of a knowledge structure, the `kmcolors`

function returns a color vector to be used with `kmhasse()`

.

```
<- (0:8)/8
probability_vec <- kmcolors(probability_vec, cm.colors)
colorvec kmhasse(xpl$space, horizontal = TRUE, colors = colorvec)
```

`kstMatrix`

The provided datasets were obtained by the research group around Cornelia Dowling by querying experts in the respective fields.

Six experts were queried about prerequisite relationships between 28 AutoCAD knowledge items (Dowling, 1991; 1993a). A seventh basis represents those prerequisite relationships on which the majority (4 out of 6) of the experts agree (Dowling & Hockemeyer, 1998).

```
summary(cad)
#> Length Class Mode
#> cad1 1764 -none- numeric
#> cad2 2772 -none- numeric
#> cad3 4424 -none- numeric
#> cad4 1932 -none- numeric
#> cad5 2380 -none- numeric
#> cad6 952 -none- numeric
#> cadmaj 7168 -none- numeric
```

Three experts were queried about prerequisite relationships between 48 items on reading and writing abilities (Dowling, 1991; 1993a). A fourth basis represents those prerequisite relationships on which the majority of the experts agree (Dowling & Hockemeyer, 1998).

```
summary(readwrite)
#> Length Class Mode
#> rw1 6672 -none- numeric
#> rw2 7680 -none- numeric
#> rw3 4896 -none- numeric
#> rwmaj 1440 -none- numeric
```

Three experts were queried about prerequisite relationships between 77 items on fractions (Baumunk & Dowling, 1997). A fourth basis represents those prerequisite relationships on which the majority of the experts agree (Dowling & Hockemeyer, 1998).

```
summary(fractions)
#> Length Class Mode
#> frac1 39039 -none- numeric
#> frac2 24409 -none- numeric
#> frac3 16016 -none- numeric
#> fracmaj 4235 -none- numeric
```

This is just a small fictitious 4-item-example used for the examples in the documentation.

```
summary(xpl)
#> Length Class Mode
#> basis 20 -none- numeric
#> space 36 -none- numeric
#> data 28 -none- numeric
$basis
xpl#> a b c d
#> [1,] 1 0 0 0
#> [2,] 0 1 0 0
#> [3,] 1 0 1 0
#> [4,] 0 1 1 0
#> [5,] 1 1 0 1
$space
xpl#> a b c d
#> [1,] 0 0 0 0
#> [2,] 1 0 0 0
#> [3,] 0 1 0 0
#> [4,] 1 1 0 0
#> [5,] 1 0 1 0
#> [6,] 1 1 1 0
#> [7,] 0 1 1 0
#> [8,] 1 1 0 1
#> [9,] 1 1 1 1
$data
xpl#> a b c d
#> [1,] 0 0 1 0
#> [2,] 1 0 0 0
#> [3,] 0 0 0 1
#> [4,] 1 1 0 0
#> [5,] 1 1 1 0
#> [6,] 1 1 1 1
#> [7,] 1 1 0 0
```

- Baumunk, K. & Dowling, C. E. (1997). Validity of spaces for assessing knowledge about fractions.
*Journal of Mathematical Psychology, 41,*99–105. - Doignon, J.-P. & Falmagne, J.-C. (1999).
*Knowledge Spaces.*Springer–Verlag, Berlin. - Dowling, C. E. (1991).
*Constructing Knowledge Structures from the Judgements of Experts.*Habilitationsschrift, Technische Universität Carolo-Wilhelmina, Braunschweig, Germany. - Dowling, C. E. (1993a). Applying the basis of a knowledge space for controlling the questioning of an expert.
*Journal of Mathematical Psychology, 37,*21–48. - Dowling, C. E. (1993b). On the irredundant construction of knowledge spaces. Journal of Mathematical Psychology, 37, 49–62.
- Dowling, C. E. & Hockemeyer, C. (1998). Computing the intersection of knowledge spaces using only their basis. In Cornelia E. Dowling, Fred S. Roberts, & Peter Theuns, editors,
*Recent Progress in Mathematical Psychology,*pp. 133–141. Lawrence Erlbaum Associates Ltd., Mahwah, NJ.