Semantically annotated learning paths
Charline Unternährer, Knut Hinkelmann, Sandra Schlick
This paper shows an application of semantic lifting in the education domain. We present a metamodel
for graphical representation of learning paths. This supports lecturers in the design of courses and
learners to navigate through learning object to achieve their learning goals. The graphical models are
semantically annotated with an ontology representing the content of the course and the learning objects.
This enables reasoning for identifying learning objects dealing with specific topics and courses dealing
with prerequisite knowledge. The approach is realized in ADOxx and validated with courses and lectures
at a university of applied sciences in Switzerland.
for graphical representation of learning paths. This supports lecturers in the design of courses and
learners to navigate through learning object to achieve their learning goals. The graphical models are
semantically annotated with an ontology representing the content of the course and the learning objects.
This enables reasoning for identifying learning objects dealing with specific topics and courses dealing
with prerequisite knowledge. The approach is realized in ADOxx and validated with courses and lectures
at a university of applied sciences in Switzerland.
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Cite as
Semantically annotated learning paths. In: BIR-WS 2023: BIR 2023 Workshops and Doctoral Consortium, 22nd International Conference on Perspectives in Business Informatics Research (BIR 2023), Ascoli Piceno, Italy, 2023.
BibTeX (Download)
@inproceedings{nokey,
title = {Semantically annotated learning paths},
author = {Charline Unternährer, Knut Hinkelmann, Sandra Schlick},
url = {https://ceur-ws.org/Vol-3514/short67.pdf},
year = {2023},
date = {2023-09-13},
urldate = {2023-09-13},
booktitle = {BIR-WS 2023: BIR 2023 Workshops and Doctoral Consortium, 22nd International Conference on Perspectives in Business Informatics Research (BIR 2023)},
address = {Ascoli Piceno, Italy},
abstract = {This paper shows an application of semantic lifting in the education domain. We present a metamodel
for graphical representation of learning paths. This supports lecturers in the design of courses and
learners to navigate through learning object to achieve their learning goals. The graphical models are
semantically annotated with an ontology representing the content of the course and the learning objects.
This enables reasoning for identifying learning objects dealing with specific topics and courses dealing
with prerequisite knowledge. The approach is realized in ADOxx and validated with courses and lectures
at a university of applied sciences in Switzerland.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}