Research on Entrepreneurship Course Knowledge Recommendation System Combining Knowledge Graph and Clustering Technology

This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part of the student profiles and advanced learning paths modules that aim to adapt to a certain student's learning style, tastes, and level of mastery of the subject. Additionally, the system creates feedback paths to hammer the recommendation algorithm in constant improvement and evolution as well. Upon the conclusion of tests and discussion, the effectiveness and utility factors put in place in our proposed KRS are assured. Moreover, the quick and efficient responsiveness of the system to adapt to students' changing needs and peculiar demands for individual learning support further improves learning outcomes and the satisfaction level of the course participants.

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Publication Details

Journal
International Journal of Web-Based Learning and Teaching Technologies
Published
2025-06-11
DOI
https://doi.org/10.4018/ijwltt.380658
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
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article

Research on Entrepreneurship Course Knowledge Recommendation System Combining Knowledge Graph and Clustering Technology

Jing Guo, Xiaorui Wang, Chao Liu
International Journal of Web-Based Learning and Teaching Technologies
Online Learning and Analytics
article

Research on Entrepreneurship Course Knowledge Recommendation System Combining Knowledge Graph and Clustering Technology

Jing Guo, Xiaorui Wang, Chao Liu
article en

Abstract

This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part of the student profiles and advanced learning paths modules that aim to adapt to a certain student's learning style, tastes, and level of mastery of the subject. Additionally, the system creates feedback paths to hammer the recommendation algorithm in constant improvement and evolution as well. Upon the conclusion of tests and discussion, the effectiveness and utility factors put in place in our proposed KRS are assured. Moreover, the quick and efficient responsiveness of the system to adapt to students' changing needs and peculiar demands for individual learning support further improves learning outcomes and the satisfaction level of the course participants.

International Journal of Web-Based Learning and Teaching TechnologiesVol. 20(1)
First Affiliated Hospital of Henan University (CN)
Openalex Percentile: Top 87%
Online Learning and Analytics
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