Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

Albreiki, Balqis; Habuza, Tetiana; Palakkal, Nishi; Zaki, Nazar

doi:10.1007/s10639-023-11938-8

Cited by 3 publications

(1 citation statement)

References 68 publications

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“…On the other hand, knowledge graph has powerful knowledge reasoning ability, which can meet the needs of deep knowledge service in various situations [6]. Knowledge graph technology can rationalize and organize massive curriculum teaching resources by standardized means and improve the efficiency of curriculum activity [7].…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic Knowledge Graph Construction Based on Deep Learning

Vladimir Y. Mariano, Mideth Abisado, Alexander A. Hernandez

2024

jes

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The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of < entity, relationship, entity > or < entity, attribute, attribute value > to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.

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Section: Introductionmentioning

confidence: 99%

Semi-automatic Knowledge Graph Construction Based on Deep Learning

Vladimir Y. Mariano, Mideth Abisado, Alexander A. Hernandez

2024

jes

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Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

Zheng,

Fan,

Gao

et al. 2024

Journal of Research on Technology in Education

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A Survey of Knowledge Graph Approaches and Applications in Education

Qu,

Li,

Wong

et al. 2024

Electronics

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This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and ProQuest for review. The findings reveal a sharp increase in recent years in the body of research into educational knowledge graphs which was mainly conducted from institutions in China. Most of the relevant research work adopted a quantitative method, such as performance evaluation, user surveys, and controlled experiments, to assess the effectiveness of knowledge graph approaches. The findings also suggest that knowledge graph approaches were primarily researched and implemented in higher education institutions, with a focus on computer science, mathematics, and engineering. The most frequently addressed objectives included enhancing knowledge representation and providing personal learning recommendations, and the most common applications were concept instruction and educational recommendations. Diverse data resources, such as course materials, student learning behaviours, and online encyclopaedia, were processed to implement knowledge graph approaches in different scenarios. Relevant technical means employed for the implementation of knowledge graphs dealt with the purposes of building knowledge ontology, achieving recommendations, and creating knowledge graphs. Various pedagogies such as personalised learning and collaborative learning are supported by the knowledge graph approaches. The findings also identified key limitations in the relevant work, including insufficient information for knowledge graph construction, difficulty in extending applications across subject areas, the restricted scale and scope of data resources, and the lack of comprehensive user feedback and evaluation processes.

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Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students

Cited by 3 publications

References 68 publications

Semi-automatic Knowledge Graph Construction Based on Deep Learning

Semi-automatic Knowledge Graph Construction Based on Deep Learning

Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

A Survey of Knowledge Graph Approaches and Applications in Education

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