Research on Student Academic Emotion Profiling and Interactive Education Models from the Perspective of Big Data
DOI:
https://doi.org/10.54097/sc9hj595Keywords:
Big Data, Academic Emotion Profiling, Interactive Education, Emotion Perception, Personalized Learning, Deep LearningAbstract
The application of big data technologies in the field of education is currently a prominent research hotspot. Artificial intelligence and big data technologies can analyze learners’ basic information, behavioral data, and learning data to intelligently generate personalized learning paths, provide individualized learning support, recommend customized learning resources, and enable intelligent assessment and feedback, ultimately facilitating the construction of academic emotion profiles and interactive education models. The New Generation Artificial Intelligence Development Plan issued by the State Council of China emphasizes the need to establish online intelligent education platforms and improve the AI education system, accelerate the reform of talent cultivation models and teaching methods through intelligent technologies, build a new educational system encompassing intelligent and interactive learning, provide precisely targeted educational services, and realize customized daily and lifelong education. Effectively leveraging big data and AI technologies to serve the entire teaching and learning process will inevitably become a key research focus in future education. In 2017, the State Council issued the *13th Five-Year Plan for National Education Development*, which proposes to actively harness the important role of big data in digital learning spaces, explore new models of future education and teaching, and support personalized learning and targeted instruction. AI and big data technologies can analyze learners’ basic information, behavioral data, and learning data to intelligently generate personalized learning paths, offer individualized learning support and interactive learning resources, and enable intelligent assessment and feedback.
Downloads
References
[1] Ma, N., Liu, C., & Guo, J. (2023). Analysis of the current status and development trend of emotion research in online education. e-Education Research, (2).
[2] Wang, Y., Liu, S., & Zheng, Y. (2021). Learner emotion perception in the intelligent era: Connotation, status and trends. Journal of Distance Education, 39(2).
[3] Liu, W., & Bi, S. (2023). Influence mechanism of students’ learning enthusiasm based on educational big data. International Journal of Emerging Technologies in Learning, 18(6), 96–109. https://doi.org/10.3991/ijet.v18i6.38764.
[4] Ba, S., & Hu, X. (2023). Measuring emotions in education using wearable devices: A systematic review. Computers & Education, 200, Article 104521. https://doi.org/10.1016/ j. compedu. 2023.104521.
[5] Pan, X., Hu, B., Zhou, Z., & Feng, X. (2023). Are students happier the more they learn? — Research on the influence of course progress on academic emotion in online learning. Interactive Learning Environments, 31(10), 6869–6889. https://doi.org/10.1080/10494820.2023.2244261.
[6] Xu, Q., Chen, S., Xu, Y., & Ma, C. (2023). Detection and analysis of graduate students’ academic emotions in the online academic forum based on text mining with a deep learning approach. Frontiers in Psychology, 14, Article 1192774. https://doi.org/10.3389/fpsyg.2023.1192774.
[7] Yang, N., & Ghislandi, P. (2024). Quality teaching and learning in a fully online large university class: A mixed methods study on students’ behavioral, emotional, and cognitive engagement. Higher Education, 88(4), 1353–1379. https://doi.org/10.1007/s10734-024-01234-5.
[8] Liu, Z., Tang, Q., Ouyang, F., Long, T., & Liu, S. (2024). Profiling students’ learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach. Computers & Education, 104, Article 105029. https://doi.org/ 10.1016/j. compedu. 2024. 10 5029.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Academic Journal of Education
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
