Author(s)

Yang Yang ¹

Affiliation(s)

¹ International Department, Guangzhou College of Commerce, Guangzhou, China

Corresponding Author

Yang Yang

ABSTRACT

This study investigates the use of knowledge graphs in blended learning, aiming to address the challenges such as fragmented self-study and lack of comprehensive understanding. Knowledge graphs help by visually representing complex knowledge structures and providing personalized learning paths tailored to individual student needs. The implementation of this approach includes several key steps: preparing multimedia resources to support diverse learning styles, guiding students in their pre-class preparation to ensure they are well-equipped for in-class activities, enhancing the in-class learning experience through interactive and engaging activities, and supporting post-class consolidation to reinforce and deepen the knowledge acquired. By integrating knowledge graphs throughout these stages, the study aims to create a more structured and coherent learning environment that can significantly improve overall learning outcomes and student engagement. The expected benefits include better organization of study materials, increased student motivation, and a more personalized and adaptive learning experience. This approach not only facilitates a deeper understanding of the subject matter but also encourages active participation and continuous learning, ultimately leading to enhanced academic performance and satisfaction.

KEYWORDS

Blended Learning, Knowledge Graphs, Personalized Learning, Educational Technology

CITE THIS PAPER

Yang Yang, Construction and Implementation of Knowledge Graph-Based Blended Learning Model. Advances in Educational Technology and Psychology (2024) Vol. 8: 131-137. DOI: http://dx.doi.org/10.23977/aetp.2024.080420.

REFERENCES

[1] Bloom, B. S. (1956). Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. New York: David McKay Co Inc.
[2] Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22, 1-18.
[3] Bonk, C. J., & Graham, C. R. (2012). The Handbook of Blended Learning: Global Perspectives, Local Designs. San Francisco, CA: Pfeiffer Publishing.
[4] Castro, R. (2019). Blended learning in higher education: Trends and capabilities. Educational Technology, 59(5), 20-27.
[5] Dietze, S., Sanchez-Alonso, S., & Ebner, H. (2013). Enhancing learning with semantic web technologies and the linked data approach. Educational Technology & Society, 16(3), 2-10.
[6] Ehrlinger, L., & Wöß, W. (2016). Towards a definition of knowledge graphs. SEMANTiCS (Posters, Demos, SuCCESS).
[7] Garrison, D. R., & Vaughan, N. D. (2008). Blended Learning in Higher Education: Framework, Principles, and Guidelines. San Francisco, CA: Jossey-Bass.
[8] Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The Handbook of Blended Learning: Global Perspectives, Local Designs (pp. 3-21). San Francisco, CA: Pfeiffer Publishing.
[9] Hattie, J. (2009). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. London: Routledge.
[10] Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, A., Giese, M., ... & Palpanas, T. (2020). Knowledge graphs. arXiv preprint arXiv:2003.02320.
[11] Mao, R. (2021). Knowledge Graphs for Enhanced Learning. Journal of Educational Technology, 42(3), 211-223.
[12] Marzano, R. J. (2007). The Art and Science of Teaching: A Comprehensive Framework for Effective Instruction. Alexandria, VA: ASCD.
[13] Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. US Department of Education.
[14] Nickel, M., Murphy, K., & Tresp, V. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11-33.  
[15] Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Communications of the ACM, 62(8), 36-43.
[16] Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3), 489-508.
[17] Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701.
[18] Suleiman, H. (2018). The Role of Knowledge Graphs in Education. International Journal of Educational Technology, 10(2), 45-59.

Sponsors, Associates, and Links

---

• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education