Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023 |
| Editors | Maiga Chang, Nian-Shing Chen, Rita Kuo, George Rudolph, Demetrios G Sampson, Ahmed Tlili |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 36-40 |
| Number of pages | 5 |
| ISBN (electronic) | 9798350300543 |
| Publication status | Published - 2023 |
| Externally published | Yes |
| Event | 23rd IEEE International Conference on Advanced Learning Technologies, ICALT 2023 - Hybrid, Orem, United States Duration: 10 Jul 2023 → 13 Jul 2023 |
Publication series
| Name | Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023 |
|---|
Abstract
Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
Keywords
- Graph-based database models, Knowledge graphs, Learning context, Personalized learning, Text mining
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Media Technology
- Social Sciences(all)
- Education
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023. ed. / Maiga Chang; Nian-Shing Chen; Rita Kuo; George Rudolph; Demetrios G Sampson; Ahmed Tlili. Institute of Electrical and Electronics Engineers Inc., 2023. p. 36-40 (Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Building Contextual Knowledge Graphs for Personalized Learning Recommendations Using Text Mining and Semantic Graph Completion
AU - Abu-Rasheed, Hasan
AU - Dornhofer, Mareike
AU - Weber, C.
AU - Kismihok, Gabor
AU - Buchmann, Ulrike
AU - Fathi, Madjid
PY - 2023
Y1 - 2023
N2 - Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
AB - Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
KW - Graph-based database models
KW - Knowledge graphs
KW - Learning context
KW - Personalized learning
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85171999981&partnerID=8YFLogxK
U2 - 10.1109/ICALT58122.2023.00016
DO - 10.1109/ICALT58122.2023.00016
M3 - Conference contribution
AN - SCOPUS:85171999981
T3 - Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023
SP - 36
EP - 40
BT - Proceedings - 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023
A2 - Chang, Maiga
A2 - Chen, Nian-Shing
A2 - Kuo, Rita
A2 - Rudolph, George
A2 - Sampson, Demetrios G
A2 - Tlili, Ahmed
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Advanced Learning Technologies, ICALT 2023
Y2 - 10 July 2023 through 13 July 2023
ER -