5 CONCLUSIONS
The essential role of curriculum is to enable quality
learning and to provide a foundational framework for
achieving high-quality learning outcomes. The
curriculum as a complex network consists of several
types of elements and exhibits multiple relations
between them, which is emphasised by the fact that
the node objects are heterogeneous and the edge types
are diverse. Acknowledging the multivariate nature of
the network, we move from the simple monolayer
representation to a more powerful abstraction for
modelling – the bipartite network model. Hence, we
extract entities from the curriculum knowledge
content - concepts and LO into two sets and construct
an unweighted directed bipartite network (RQ1). To
demonstrate and apply relationships between related
subjects, learning processes should enable students to
draw meaningful connections between subjects and
integrate multiple subjects into larger learning
domains. As a result, it would also encourage the
growth of more intricate cognitive interconnections
and structures, and consequently, of competences and
skills within and across domains. Centrality analysis
has shown that achieving the learning outcomes with
large number of concepts is highly correlated with
cognitive load during learning of new and yet
strongly interwoven concepts (RQ2). Measuring the
importance of nodes in bipartite graphs could be
easily bypassed by projecting the bipartite graph onto
a unipartite network and calculating the centrality
values using, for example, the PageRank or
Eigenvector centrality algorithms, which may lead to
information loss and distortion of the network
topology, resulting in misleading results. Therefore,
in our future work, we will investigate centrality
metrics designed specifically for bipartite networks -
BiRank, HITS, CoHITS and BGRM centrality index
and their comparison with unipartite network model
for the IoT education programme. The representation
of knowledge networks as bipartite network, apart
from enabling the key entity detection, allows the
study of the effective knowledge organisation, in
terms of optimal information transfer that student can
absorb and retain effectively provided in such a way
that it does not “overload” their mental capacity.
ACKNOWLEDGEMENTS
This work has been fully supported by the University
of Rijeka under the project number uniri-drustv-18-
140.
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