# lpath: Learning Paths in a Knowledge Structure In kst: Knowledge Space Theory

## Description

Computes learning paths in a knowledge structure.

## Usage

 `1` ``` lpath(x) ```

## Arguments

 `x` An R object of class `kstructure`.

## Details

A learning path in a knowledge structure is a maximal sequence of knowledge states, which allows learners to gradually traverse a knowledge structure from the empty set {} (or any other bottom state) to the full set of domain problems Q. Mathematically, it is represented as a set of states.

`lpath` takes an arbitrary knowledge structure and computes all possible learning paths in the respective knowledge structure.

## Value

A list where each element represents one learing path.

## References

Doignon, J.-P., Falmagne, J.-C. (1999) Knowledge Spaces. Heidelberg: Springer Verlag.

`kstructure`

## Examples

 ```1 2 3 4``` ```kst <- kstructure(set(set(), set("a"), set("b"), set("a","b"), set("a","d"), set("b","c"), set("a","b","c"), set("a","b","d"), set("b","c","d"), set("a","b","c","d"), set("a","b","c","d","e"))) lpath(kst) ```

### Example output

```Loading required package: proxy

Attaching package: 'proxy'

The following objects are masked from 'package:stats':

as.dist, dist

The following object is masked from 'package:base':

as.matrix

[[1]]
({}, {"a"}, {"a", "b"}, {"a", "b", "c"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[2]]
({}, {"b"}, {"a", "b"}, {"a", "b", "c"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[3]]
({}, {"b"}, {"b", "c"}, {"a", "b", "c"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[4]]
({}, {"a"}, {"a", "b"}, {"a", "b", "d"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[5]]
({}, {"b"}, {"a", "b"}, {"a", "b", "d"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[6]]
({}, {"a"}, {"a", "d"}, {"a", "b", "d"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})

[[7]]
({}, {"b"}, {"b", "c"}, {"b", "c", "d"}, {"a", "b", "c", "d"}, {"a",
"b", "c", "d", "e"})
```

kst documentation built on Nov. 30, 2018, 4:18 p.m.