Abstract
Learning early-warning is one of the important ways to realize adaptive learning. Aiming at the problem of too large prediction granularity in learning early-warning, we divide student’s characters into three dimensions (knowledge, behavior and emotion). Secondly, we predict the student’s master degree of knowledge, based on the knowledge point. And then we realized learning early-warning model. In the model, we take 60 points as the learning early-warning standard, and take RF and GDBT as base classifiers, and give the strategy of selecting the basic model. The experiment shows that the prediction of knowledge mastery of the model and the real data Pearson correlation coefficient can reach 0.904279, and the prediction accuracy of the model below the early-warning line can reach 76%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, J., Yang, Z., Wang, X., Zhang, X., Feng, J.: An early-warning method on e-learning. In: Liu, S., Glowatz, M., Zappatore, M., Gao, H., Jia, B., Bucciero, A. (eds.) eLEOT 2018. LNICST, vol. 243, pp. 62–72. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93719-9_9
Cheng, X., et al.: A novel learning early-warning model based on random forest algorithm. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds.) ITS 2018. LNCS, vol. 10858, pp. 306–312. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91464-0_32
Sisovic, S., Matetic, M., Bakaric, M.B.: Clustering of imbalanced moodle data for early alert of student failure. In: 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 165–170. IEEE Press, Herlany (2016). https://doi.org/10.1109/sami.2016.7423001
Purdue Information Technology. http://www.itap.purdue.edu/learning/tools/signals/
Acknowledgments
This paper was supported by National Key Research and Development Program of China (Grant No. 2017YFB1402400), Ministry of Education “Tiancheng Huizhi” Innovation Promotes Education Fund (Grant No. 2018B01004), National Natural Science Foundation of China (Grant No. 61402020), and CERNET Innovation Project (Grant No. NGII20170501).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhai, J., Zhu, Z., Li, D., Huang, N., Zhang, K., Huang, Y. (2019). A Learning Early-Warning Model Based on Knowledge Points. In: Coy, A., Hayashi, Y., Chang, M. (eds) Intelligent Tutoring Systems. ITS 2019. Lecture Notes in Computer Science(), vol 11528. Springer, Cham. https://doi.org/10.1007/978-3-030-22244-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-22244-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22243-7
Online ISBN: 978-3-030-22244-4
eBook Packages: Computer ScienceComputer Science (R0)