![](bg8.png)
global knowledge graph. Based on this additional in-
formation, we would like to introduce an intelligent
tutoring system in order to better recommend exer-
cises to the students. One can, for instance, imag-
ine that the application does not simply show exer-
cises based on the Leitner box system, but makes
informed suggestions based on a student’s past per-
formance for specific individual skills. It might fur-
ther be used to detect a user’s knowledge gaps. In
this domain, the EduCOR ontology
14
could be a good
fit, as it models both the education side of things but
also incorporates the mapping to labour-market skills.
Recently, GraphBRAIN has been used to power an
initial implementation of an intelligent tutoring sys-
tem (Ferilli et al., 2022). GraphBRAIN allows users
to utilise ontologies as database schema on top of a
graph database. Exploring the direction of having our
model conceptualised as an ontology (in combination
with EduCOR) is a promising direction as it means
that all generated data could easily be shared across
systems with other projects in the field of computing
education research.
6 CONCLUSION
In this position paper we presented a number of
challenges and constraints faced by small organisa-
tions offering professional training and up-skilling for
learners who are not served by traditional education.
We introduced a preliminary version of a curriculum
packager combining micromaterials, study lenses and
a companion mobile app to address some of these
challenges. It is important to note that most of the
content designed to work in the presented context and
its constraints will also be usable in general contin-
ued education. We further discussed some remaining
challenges and future research directions. The pro-
posed research is essential for reaching under-served
learners and to expand the body of knowledge in com-
puting education research.
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