Research on information construction of knowledge graph based on literature retrieval in english learning
Palabras clave:
Controlled vocabulary search, Document visibility, Information technologiesResumen
This study aimed to explore the construction of an English language knowledge graph based on literature retrieval to support intelligent education. A questionnaire was administered to collect data on students’ experiences with traditional and technology-enhanced learning approaches. Literature was also retrieved and analyzed to populate the knowledge graph domains. The results showed that implementing a knowledge graph significantly improved learning personalization and fostered greater student engagement compared to conventional
teaching methods. Real-time analytics and continuous feedback further optimized the
learning process. Post-implementation assessments found notable gains in students’
academic performance and inclination toward English learning. The personalized, adaptive learning environment facilitated by the nowledge graph more effectively sustained interest and promoted achievement. In conclusion, knowledge graphs constructed through literature analysis hold promising potential for advancing English education when incorporated into intelligent tutoring systems. By mapping interconnections within the subject domain visually and computationally, they can power highly customized instruction tailored to individual needs.
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Aouichaoui, A. R. N. et al. G.Uncertainty estimation in deep learning-based property models: Graph-based on literature retrieval neural networks applied to the critical properties. AIChE Journal, v. 68, n. 6, e17696, 2022. Doi : https://doi.org/10.1002/aic.17696
Charifi, R. et al. A. Sedimentary phosphate classification based on spectral analysis and machine learning. Computers & Geosciences, v. 150, 2021.
Chiu, C. K.; Tseng, J. C. R. A Bayesian Classification Network-Based Learning Status Management System in an Intelligent Classroom. Educational Technology & Society, v. 24, n. 24, p. 67-78, 2021.
Cui, Q. Multimedia Teaching for Applied Linguistic Smart Education System. International Journal of Human-Computer Interaction, v. 56, n. 56, p. 89-101, 2023. Doi: https://doi.org/10.1080/10447318.2022.2122111
Eng, K.; Wolock, D. M.; Wieczorek, M. Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach. Advances in Water Resources, v. 11, n. 15, p. 45-58, 2023. Doi: https://doi.org/10.1016/j.advwatres.2023.104440
Geisler, E. L. et al. A Role for Artificial Intelligence in the Classification of Craniofacial Anomalies. Journal of Craniofacial Surgery, v. 32, n. 3, p. 967-969, 2021. Doi: https://doi.org/10.1097/SCS.0000000000007369
Huang, W. et al. A generic intelligent routing method using deep reinforcement learning with graphs based on literature retrieval neural networks. IET Communications, v. 69, p. 69-77, 2022. Doi https://doi.org/10.1049/cmu2.12487
Jiang, D. et al. Multiview feature augmented neural network for knowledge graph based on literature retrieval embedding. Knowledge-Based Systems, v. 56, n. 56, p. 65-77, 2022. Doi: https://doi.org/10.1016/j.knosys.2022.109721
Liu, Y. F.; Hwang, W. Y.; Liu, Z. Y. Effects of Mobile Drama With Authentic Contexts on English Learning based on Literature Retrieval: Journal of Educational Computing Research, v. 59, n. 7, p. 1294-1318, 2021. Doi: https://doi.org/10.1177/0735633121994289
Matthews, S.; Proctor, M. D. Can Public Health Workforce Competency and Capacity be Built through an Agent-based Online, Personalized Intelligent Tutoring System? Educational Technology & Society, v. 24, n. 1, p. 77-86, 2021.
Menekse, M. Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges. Journal of Engineering Education, v. 233, n. 233, p. 45-59, 2023. Doi: https://doi.org/10.1002/jee.20539
Moore, S. K.; Schneider, D.; Strickland, E. How Deep Learning Works: Inside the Neural Networks that Power Today’s AI. IEEE Spectrum, v. 10, n. 58, 2021. Doi: https://doi.org/10.1109/MSPEC.2021.9563965
Roberts, G. J. Tall trees; weak roots? A model of barriers to English language proficiency confronting displaced medical healthcare professionals: Language Teaching Research, v. 27, n. 4, p. 820-836, 2023. Doi: https://doi.org/10.1177/1362168820968366
Ruiz, S.; Rebuschat, P.; Meurers, D. The effects of working and declarative memory on instructed second language vocabulary learning: Insights from intelligent CALL: Language Teaching Research, v. 25, n. 4, p. 510-539, 2021. Doi: https://doi.org/10.1177/1362168819872859
Shiranirad, M.; Burnham, C. J.; English, N. J. Machine-learning-based many-body energy analysis of argon clusters: Fit for size? Chemical Physics, v. 552, e111347, 2021.
Xin, D. Application Value of Multimedia Artificial Intelligence Technology in English learning Based on literature retrieval Practice. Hindawi Limited, v. 45, n. 45, p. 23-40, 2021.
Xu, Z. et al. The effects of web-based text structure strategy instruction on adult Chinese ELLs’ reading comprehension and reading strategy use. Language Teaching Research, v. 28, n. 4, p. 1288-1310, 2024. Doi: https://doi.org/10.1177/13621688211022308
Yang, C. et al. Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems: Journal of Educational Computing Research, v. 59, n. 6, p. 1015-1035, 2021. Doi: https://doi.org/10.1177/0735633120986256
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