Visual Knowledge Graph Construction of Self-directed Learning Ability Driven by Interdisciplinary Projects


  • Xiangying Kou Xi’an Traffic Engineering Institute



INTRODUCTION: The application of interdisciplinary information technology is becoming more and more widespread, and the application of visual knowledge mapping in the process of students' independent learning is also becoming more and more important; therefore, in this context, takes the history discipline as a starting point to study the construction of visual knowledge mapping of students' independent learning ability under the drive of interdisciplinary projects.

OBJECTIVES: To enrich the means of student independent learning aids in China's history discipline and enhance the modernization level of China's history discipline construction; to solve the problem that student independent learning ability under the drive of China's interdisciplinary projects can not be visualized and observed; to further improve China's distance education environment and to enhance the educational capacity of the history discipline.

METHODS: Firstly, the relevant modeling uses a visual knowledge map. Secondly, the neural network model assesses students' independent learning ability in history learning. Finally, the convolutional neural network model is used to assess the efficiency of the knowledge map.

RESULTS: The Sig and Tanh function models have better robustness, and the ReLU and PReLU functions have weaker interdisciplinary driving performance. However, the iterative Knownledge1 and Knownledge2 models have better robustness of the visualized knowledge graph.

CONCLUSION: In studying history, the interdisciplinary, project-driven, and independent learning ability of students could be more vital, and our country should vigorously develop new information network technology to improve the status quo of history discipline education in China.


Ahmadi, A., Darni, D., & Yulianto, B. (2021). The Techniques of Qualitative Data Collection in Mapping Indonesian Litterateurs in East Java: An Initial Design. International Journal of Multicultural and Multireligious Understanding (IJMMU), 8, 67–99.

Calligaro, Thomas., Banas, Agnieszka., Banas, Krzysztof., Radovi, I. Bogdanovi., Brajkovi, Marko., Chiari, Massimo., Forss, A. Maija., Hajdas, Irka., Krmpoti, Matea., & Mazzinghi, Anna. (2022). Emerging nuclear methods for historical painting authentication: AMS-. Forensic Science International, 336, 111327.

Campos, F. O., Orini, M., Arnold, R., Whitaker, J., & Bishop, M. J. (2021). Using computational modeling to assess the ability of substrate mapping techniques to guide ventricular tachycardia ablation. Computers in Biology and Medicine, 77(78), 104214.

Chen, Y., Liu, L., Liu, H., & Sun, Y. (2021). Study on the Formalized Development of the Street Stall Economy based on Domestic and International Experiences and Perspectives. Economic reserch, 4(4), 326a.

Dinar, A., Bradford, J., Castelan, E., Gavino, J., González, J., Jantz, A., Li, Y., Iii, F. M., & Parmer, M. (2022). Domestic Interests and International Negotiations. World Scientific Book Chapters, 66(66), 66–88.

FreiburgFreiburgGermany, S. straub@unifr chUniversität. (2021). Literary Reviewing and the Velocity of Book Histories in Times of Digitization. Anglia, 139(1), 224–241.

Fujitani, R., Hattori, M., Yasuda, Y., & Hoshi, T. (2023). Domestic and international effects of economic policy uncertainty on corporate investment and strategic cash holdings: Evidence from Japan. Social Science Electronic Publishing, 55(44), 234–259.

Hallinger, P., & Kovaevi, J. (2022). Mapping the intellectual lineage of educational management, administration and leadership, 1972–2020: Educational Management Administration & Leadership, 50(2), 192–216.

Jafari, M., Fathian, M., Akhavan, P., & Fesharaki, M. N. (2022). Mapping Network Warfare Techniques to KM: Applying Lessons and Theories from the Most Demanding Arena of All. 455(445), 66–97.

Junk, J., & Blatter, J. (2022). Transnational attention, domestic agenda-setting and international agreement. 33, 33(45), 44.

Kim, J. C., Marilyn Lee. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 77–99.

Kim, P., & Yoo, Y. (2021). Domestic and international competitiveness of Korea’s information security industry. Journal of Korea Technology Innovation Society, 77(77), 25–76.

Lapworth, E. (2021). Assessing large-scale digitization using Web analytics. Digital Library Perspectives, ahead-of-print(ahead-of-print), 12–65.

Liu, Q., Li, T., Zang, Q., & Hao, X. (2021). Research on key technology of 3D digitization for a secondary power grid system based on model application analysis. Journal of Physics: Conference Series, 34(34), 66–99.

Meadow, C. M. (2022). Correspondences and Contradictions in International and Domestic Conflict Resolution: Lessons From General Theory and Varied Contexts. 56(56), 24336.

Pantiukhin, D., Piepenburg, D., Hansen, M. L. S., & Kraan, C. (2021). Data-driven bioregionalization: A seascape-scale study of macrobenthic communities in the Eurasian Arctic. Journal of Biogeography, 45(34), 223–258.

Prokop, V., Stejskal, J., Merickova, B. M., & Odei, S. A. (2023). Revealing the importance of international and domestic cooperation using artificial neural networks: Case of European radical and incremental innovators. European Journal of Innovation Management, 45(45), 56–88.

Pez Ubieta, I. de L., Velasco, E. P., Puente Mndez, S. T., & CandelasHeras, F. A. (2023). Detection and depth estimation for domestic waste in outdoor environments by sensor fusion. 5(5), 43.

Rodríguez, A., Hernández, V., & Nieto, M. J. (2022). International and domestic external knowledge in firms' innovation performance from transition economies: The role of institutions. Technological Forecasting and Social Change, 176, 67–99.

Siad, F. M., & Rabi, D. M. (2021). Harassment in Medicine: Cultural barriers to psychological safety. 33(33), 777–787.

Szombara, S. (2021). Using different mapping techniques and GIS programs to analyze and visualize mental maps. Polish Cartographical Review, 53, 91–104.




How to Cite

Kou X. Visual Knowledge Graph Construction of Self-directed Learning Ability Driven by Interdisciplinary Projects. EAI Endorsed Scal Inf Syst [Internet]. 2024 Mar. 1 [cited 2024 Apr. 20];. Available from:



Research articles