Abstract
The online education is accelerating the transformation and innovation of the education industry with the rise of smart education. Learning behavior data analysis has promoted the development of education informatization. Using these learning behavior big data, a technology framework is proposed to improve teaching quality. Then, taking academic performance prediction as a case study, we provides an effective strategy to predict students’ academic performance. Through the case study, we learn more about the weak points in the students’ learning processes based on the analysis of learning behaviors.
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References
Atebekov, A.: Student research abstract: Internet of Things-based smart classroom environment. In: 31st Annual ACM Symposium on Applied Computing (SAC 2016), pp. 4–8. ACM, New York (2016)
Guo, C., Huang, Y.: Effect of mobile devices and software in collaborative learning smart classroom on students’ learning motivation. In: 2021 the 6th International Conference on Distance Education and Learning, pp. 24–28. ACM, Shanghai China (2021)
Wang, Z., Xu, Y.: Design and implementation of speciality English teaching mode based on smart classroom. In: 2021 5th International Conference on Education and Multimedia Technology, pp. 38–43. ACM, Kyoto Japan (2021)
Li, W.: Research on teaching management based on smart campus platform and smart classroom. In: 2021 4th International Conference on Information Systems and Computer Aided Education, pp. 279–283. ACM, Dalian, China (2021)
Jiang Y.: Management system of hybrid classroom teaching process oriented to smart classroom. In: 2021 4th International Conference on Information Systems and Computer Aided Education, pp. 1234–1237. ACM, Dalian, China (2021)
Li, C., Li, J.: Construction of teaching behaviors analysis model for smart classroom. In: 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 133–138. IEEE, Guiyang, China (2021)
Dekdouk, A.: Integrating mobile and ubiquitous computing in a smart classroom to increase learning effectiveness. In: International Conference on Education and e-Learning Innovations, pp. 1–5. IEEE, Sousse, Tunisia (2012)
Routray, S.K., Sharma, L., Sahoo, A., Javali, A., Sharmila, K.P., Akanksha, E.: IoT and Immersive Technology based Smart Classrooms. In: 2021 Fifth International Conference on I-SMAC, pp. 136–141. IEEE, Sousse, Tunisia (2021)
Ou L, Zhang, M.: Project teaching method design and empirical study in smart classroom environment. In: 2021 2nd International Conference on Big Data and Informatization Education, pp. 441–444. IEEE, Sousse, Tunisia (2021)
Deng, X., Zhang, R.: Smart learning environment: a case on the construction of smart classrooms in colleges and universities in Guangzhou. In: 2019 International Symposium on Educational Technology, pp. 262–264. IEEE, Hradec, Kralove (2019)
Wan, H., Liu, K., Yu, Q., Gao, X.: Pedagogical intervention practices: improving learning engagement based on early prediction. In: IEEE Transactions on Learning Technologies, pp. 278–289 (2019)
Piwek, P., Savage, S.: Challenges with learning to program and problem solve: an analysis of student online discussions. In: Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE 2020), pp. 494–499. ACM, New York (2020)
Acknowledgement
This work was supported in part by the Beijing Municipal Science and Technology Project under Grant KM201910005031, and in part by the Education and Teaching Research Fund of the Beijing University of Technology under Grant ER2020B011.
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Li, D., Ding, S., Wang, W., Su, H., Wang, Y. (2023). A Teaching Framework Based on Big Data of Students’ Learning Behaviors: A Case Study of Academic Performance Prediction. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2022. Lecture Notes in Electrical Engineering, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-99-1428-9_1
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DOI: https://doi.org/10.1007/978-981-99-1428-9_1
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