Workflow
Figure 1 shows an overall view of the possible workflow(s) within the
CDSS package including the interfacing points to the
kstMatrix and CbKST packages.
Figure 1: CDSS workflow
Deriving skill structures
There are two ways to derive the skill structure from the skill
assignment, through an attribution function or through a skill (multi)
map. Both ways lead to the same skill structure. Further processing then
has to be done using functions from the kstMatrix and
CbKSTpackages.
Example
af <- cdss_skill_sa2af(sa) # Attribution function
af
#> Skill a b c d e f g
#> 2 a 1 0 0 0 0 0 0
#> 3 b 0 1 0 0 0 0 0
#> 4 c 1 0 1 0 0 0 0
#> 5 c 0 1 1 0 0 0 0
#> 6 d 0 1 0 1 0 0 0
#> 7 e 0 0 1 0 1 0 0
#> 8 f 0 0 0 1 1 1 1
#> 9 f 0 0 0 1 0 1 1
#> 10 g 0 0 0 1 1 1 1
#> 11 g 0 0 0 1 0 1 1
plot(kmspace(kmbasis(kmclosure(af))))FIgure 2: Derived skill space
mu <- cdss_sa2mu(sa) # Skill multi map
mu
#> LO a b c d e f g
#> 1 l1 1 0 0 0 0 0 0
#> 2 l2 0 1 0 0 0 0 0
#> 3 l3 1 0 1 0 0 0 0
#> 4 l4 0 1 1 0 0 0 0
#> 5 l5 0 1 0 1 0 0 0
#> 6 l6 1 0 1 0 1 0 0
#> 7 l6 0 1 1 0 1 0 0
#> 8 l8 0 1 0 1 0 1 1
#> 9 l7 0 1 1 1 1 1 1
plot(kmspace(kmbasis(cbkst_competencestructure(mu))))FIgure 3: Dweriving a skill space through the skill multi map
Deriving LO structures
For LO structures, things are similar to but still slightly different from skill structures. Again, we have two ways to derive an LO space but the meaning of the LO states is different and thus are the spaces. The straightforward definition of an LO state would be the set of LOs the learner has visited. The respective LO space is achieved using the attribution function way. For a skill (multi) map, however, the LO state of a learner is the set of LOs whose (required and taught) skills this learner has. One can also have a look at the possible learning paths through the LO space.
Example
laf <- cdss_lo_sa2af(sa) # Attribution function
laf
#> LO l1 l2 l3 l4 l5 l6 l7 l8
#> 2 l1 1 0 0 0 0 0 0 0
#> 3 l2 0 1 0 0 0 0 0 0
#> 4 l3 1 0 1 0 0 0 0 0
#> 5 l4 0 1 0 1 0 0 0 0
#> 6 l5 0 1 0 0 1 0 0 0
#> 7 l6 0 0 1 0 0 1 0 0
#> 8 l6 0 0 0 1 0 1 0 0
#> 9 l7 0 0 0 0 1 1 1 0
#> 10 l8 0 0 0 0 1 0 0 1
lospc <- kmspace(kmbasis(kmclosure(laf)))
plot(lospc)FIgure 4: LO space derived through the attribution function
lp <- kmlearningpaths(lospc)
length(lp)
#> [1] 340
lp[[1]]
#> [[1]]
#> [1] "∅"
#>
#> [[2]]
#> [1] "{l1}"
#>
#> [[3]]
#> [1] "{l1, l2}"
#>
#> [[4]]
#> [1] "{l1, l2, l3}"
#>
#> [[5]]
#> [1] "{l1, l2, l3, l4}"
#>
#> [[6]]
#> [1] "{l1, l2, l3, l4, l5}"
#>
#> [[7]]
#> [1] "{l1, l2, l3, l4, l5, l6}"
#>
#> [[8]]
#> [1] "{l1, l2, l3, l4, l5, l6, l8}"
#>
#> [[9]]
#> [1] "{l1, l2, l3, l4, l5, l6, l7, l8}"
#>
#> attr(,"class")
#> [1] "kmlearningpath" "list"
lp[[2]]
#> [[1]]
#> [1] "∅"
#>
#> [[2]]
#> [1] "{l1}"
#>
#> [[3]]
#> [1] "{l1, l2}"
#>
#> [[4]]
#> [1] "{l1, l2, l3}"
#>
#> [[5]]
#> [1] "{l1, l2, l3, l4}"
#>
#> [[6]]
#> [1] "{l1, l2, l3, l4, l5}"
#>
#> [[7]]
#> [1] "{l1, l2, l3, l4, l5, l6}"
#>
#> [[8]]
#> [1] "{l1, l2, l3, l4, l5, l6, l7}"
#>
#> [[9]]
#> [1] "{l1, l2, l3, l4, l5, l6, l7, l8}"
#>
#> attr(,"class")
#> [1] "kmlearningpath" "list"FIgure 5: LO space derived through the skill multi map
The latter LO space has 37 states while the former one has 42 states.