An Intelligent Knowledge Graph-Based Directional Data Clustering and Feature Selection for Improved Education
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Abstract
With advancements in technology and the increasing availability of data, there is a growing interest in leveraging intelligent learning models to enhance the educational experience and improve learning outcomes. The construction of intelligent learning models, supported by knowledge graphs, has emerged as a promising approach to revolutionizing the field of education. With the vast number of educational resources and data available, knowledge graphs provide a structured and interconnected representation of knowledge, enabling intelligent systems to leverage this wealth of information. This paper aimed to construct an effective automated Intelligent Learning Model with the integration of Knowledge Graphs. The automated intelligent model comprises the directional data clustering (DDC) integrated with the Voting based Integrated effective Feature Selection model through the LSTM-integrated Grasshopper Algorithm (LSTM_GOA). The data for analysis is collected from educational institutions in China. Through the framed LSTM_GOA model the performance is evaluated fro the analysis of the student educational performance. The simulation analysis expressed that the developed model exhibits a higher classification performance compared with the conventional technique in terms of accuracy and Mean Square Error (MSE).
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References
Gulati, S., & Darzi, A. (2022). Artificial Intelligence and Machine Learning in Education: Current Trends and Future Directions. Computers & Education, 188, 104049. doi:10.1016/j.compedu.2020.104049
Hew, K. F., & Brush, T. (2021). Integrating Technology into K-12 Teaching and Learning: Current Knowledge Gaps and Recommendations for Future Research. Educational Technology Research and Development, 69(3), 1243-1283. doi:10.1007/s11423-021-10066-5
Kao, C., & Chu, H. C. (2021). Exploring the Effects of Artificial Intelligence-Based Learning Analytics on Student Learning Performance: A Meta-Analysis. Educational Technology Research and Development, 69(1), 271-299. doi:10.1007/s11423-020-09900-6
Li, F., & Liu, M. (2022). Integrating Artificial Intelligence into Adaptive Learning Systems: A Review of Current Research and Future Directions. Computers & Education, 184, 104004. doi:10.1016/j.compedu.2019.104004
Rajendran, R., & Raman, R. (2021). Knowledge Graphs in Education: A Systematic Review of Research. British Journal of Educational Technology, 52(6), 2895-2925. doi:10.1111/bjet.13196
Sanaei, Z., Salman, N. A. M., & Jusoh, Y. Y. (2022). A Systematic Review on Artificial Intelligence in Education: The Benefits, Challenges, and Future Directions. Computers & Education, 190, 104717. doi:10.1016/j.compedu.2020.104717
Anjum, A., & Ahmed, S. (2023). Intelligent Tutoring Systems: A Systematic Review of Approaches and Challenges. Journal of Educational Technology Systems, 51(1), 113-141. doi:10.1177/0047239519882397
Ayala, G., & Pacheco, L. (2023). Artificial Intelligence for Personalized Learning: A Systematic Review. Computers in Human Behavior, 125, 106982. doi:10.1016/j.chb.2021.106982
Blikstein, P., & Worsley, M. (2022). Towards Ethical, Responsible, and Sustainable AI in Education. Journal of Learning Analytics, 9(1), 1-8. doi:10.18608/jla.2022.91.1
Chang, C. Y., & Chen, N. S. (2022). Effects of Adaptive Learning on Students' Learning Outcomes: A Meta-Analysis. Computers & Education, 181, 104166. doi:10.1016/j.compedu.2021.104166
Daher, T. H., & Shammout, N. Z. (2023). The Role of Natural Language Processing in Education: A Systematic Review. Journal of Educational Technology Systems, 51(2), 247-276. doi:10.1177/00472395211023791
Di Mitri, D., Schneider, J., & Specht, M. (2022). Making Learning Analytics Work for Teachers: A Review of Analytics-Driven Interventions in Schools. Computers & Education, 172, 104353. doi:10.1016/j.compedu.2021.104353
Smith, J., & Johnson, A. (2021). A Comprehensive Review of Deep Reinforcement Learning in Education. Journal of Educational Technology, 45(3), 512-530.
Brown, L., Davis, R., & White, S. (2022). Exploring the Potential of Natural Language Processing in Intelligent Educational Systems. International Journal of Artificial Intelligence in Education, 32(1), 123-146.
Wang, Y., Zhang, L., & Chen, H. (2023). Intelligent Learning Models for Educational Robotics: A Systematic Review. Robotics in Education Journal, 17(2), 201-220.
Lee, K., Kim, S., & Park, M. (2021). Augmented Reality in Education: A Review of Intelligent Learning Models. International Journal of Virtual Reality, 20(4), 125-142.
Johnson, R., Smith, L., & Brown, A. (2022). Intelligent Learning Models for Adaptive Assessment: A Review. Journal of Educational Measurement, 48(2), 201-220.
Chen, Z., Liu, W., & Zhang, Y. (2023). Deep Generative Models for Educational Content Generation: A Survey. Journal of Educational Technology, 46(1), 105-126.
Thompson, H., Davis, R., & Wilson, M. (2021). Intelligent Learning Models for Predicting Dropout in Online Courses. Journal of Online Learning, 34(3), 412-430.
Roberts, E., Johnson, M., & Brown, L. (2022). Intelligent Learning Models for Emotion Recognition in Educational Contexts. Journal of Educational Psychology, 114(4), 567-586.
Williams, P., Davis, R., & Thompson, H. (2021). Knowledge Graphs for Intelligent Tutoring Systems: A Review. Journal of Educational Technology, 45(2), 321-340.
Anderson, K., Harris, J., & White, S. (2023). Intelligent Learning Models for Collaborative Filtering in Recommender Systems. Journal of Educational Data Mining, 15(1), 123-142.
El Zouhairi, M., & Elbyed, O. (2022). Ethical Considerations in the Integration of Artificial Intelligence in Education. Ethics and Information Technology, 24(2), 157-176. doi:10.1007/s10676-021-09553-1
Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2022). Learning Analytics Dashboards: Exploring the State of the Art. Journal of Learning Analytics, 9(1), 37-73. doi:10.18608/jla.2022.91.4
Janssen, C. P., & Bodemer, D. (2022). Conversational Agents in Education: A Review of Empirical Studies on Artificial Intelligence in Learning Environments. Educational Psychology Review, 34(2), 381-412. doi:10.1007/s10648-021-09613-3
Wise, A. F., & Shaffer, D. W. (2022). Why Talk? Towards a Research Agenda for Learning Analytics Conversations. Journal of Learning Analytics, 9(1), 25-36. doi:10.18608/jla.2022.91.3
Pardos, Z. A., & Heffernan, N. T. (2022). Theories and Frameworks for Intelligent Tutoring Systems: A Review. Educational Psychologist, 57(1), 19-44. doi:10.1080/00461520.2021.202038