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copied with their values into the environment of
the user at the parent node (with maybe a change
of name, see downwards interface).
The downwards interface υ
sake of clarity). Nodes are identified with capital
letters, while conditions on the edges are named
using non-capital letters. At the top level, node B3
contains the sub-graph with nodes labeled C1,...
C5. Nodes B3 and B4 are container nodes, and
the other nodes are attached to a learning activity.
Input nodes at each level are marked with a white
incoming arrow (B1, C1, and C2), and exit edges
are marked with a cross at the bottom of the node
(B4 and C5). If the current state is B2 and only
condition b2 is true, then the transition function δ
returns either C1 or C2 as the following activity
to be visited.
The detailed algorithm to traverse a sequencing
graph is presented in Figures 2 and 3, and is ex-
ecuted every time the student finishes with a
learning activity (i.e. at a node). All outgoing
edges' conditions are evaluated against the stu-
dent's environment; those transitions where the
conditions evaluate to true are enabled. Conditions
can be viewed as prerequisites. If any of the enabled
edges is an exit edge, this process must be re-
Υ defines how
the variables exported from a child node are to
be copied into the user's environment π. It may
leave the variables' names unaltered or state a
change on the name in order to avoid collisions.
Traversal Algorithm
In a sequencing graph, transitions are defined as a
function of the current node and the conditions on
the edges whose source node is the current node,
evaluated against the environment of each student.
δ: N × C × π →N
The output of this function is a sequence of
learning activities adapted to each student.
An example of a simple sequencing graph has
been given in Figure 1. The graph has two hier-
archical levels (the root node is omitted for the
Figure 2. Traversal algorithm in SG
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