Abstract
Temporal information plays an important role in student performance prediction, so two interpretable feature factors, namely enthusiasm and stability, are extracted from online and offline blended temporal learning data, which can characterize students’ learning attitudes. A new transfer predicting model called LGES is proposed. The contrast experiments with and without the feature factors, and the experiments with and without transfer learning are carried out. The results show that the proposed model significantly improves the accuracy and the discrimination ability. Furthermore, it confirms that both transfer learning and the two feature factors can effectively improve prediction performance. The ablation experiment of datasets is conducted. It proves that the model trained on the blended data achieved the best performance, and points that the importance of offline data in identifying truly failing students.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H., Shang, X.: Educational data mining techniques for student performance prediction: method review and comparison analysis. Front. Psychol. 12, 698490 (2021)
Nearly a third of university courses still have blended teaching. https://www.bbc.com/news/education-64130367. Accessed 28 Mar 2024
EDUCAUSE.2021 EDUCAUSE horizon report | Teaching and learning edition [EB/OL], 26 April 2021. https://library.educause.edu/resources/2021/4/2021-educause-horizon-report-teaching-and-learning-edition. Accessed 23 May 2024
EDUCAUSE.2021 EDUCAUSE horizon report | Teaching and learning edition [EB/OL], 26 April 2021. https://library.educause.edu/resources/2022/4/2022-educause-horizon-report-teaching-and-learning-edition. Accessed 23 May 2024
EDUCAUSE. 2021 EDUCAUSE horizon report | Teaching and learning edition [EB/OL], 26 April 2021. https://library.educause.edu/resources/2023/5/2023-educause-horizon-report-teaching-and-learning-edition. Accessed 23 May 2024
Long, Z., Mu, X., Song, C., Tian, D.: Research on the learning behavior of students in blended learning mode. In: 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA), pp. 210–213. IEEE, Xiamen, China (2021)
Romero, C., Ventura, S.: Educational data mining and learning analytics: an updated survey. WIREs Data Min. Knowl. 10, e1355 (2020)
Xiao, W., Ji, P., Hu, J.: A survey on educational data mining methods used for predicting students’ performance. Eng. Rep. 4, e12482 (2022)
Chen, J., et al.: Student behavior analysis and performance prediction based on blended learning data. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds.) KSEM 2022. LNCS, vol. 13369, pp. 59–609. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10986-7_48
Lagus, J., Longi, K., Klami, A., Hellas, A.: Transfer-learning methods in programming course outcome prediction. ACM Trans. Comput. Educ. 18, 1–18 (2018)
Baker, R.S.: Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes (2019)
Bai, H., Yu, H., Bantsimba N., R., Luo, L.: How college experiences impact student learning outcomes: insights from Chinese undergraduate students. Front. Psychol. 13, 1021591 (2022)
Chiu, M.-C., Moss, E., Richards, T.: Effect of deadlines on student submission timelines and success in a fully-online self-paced course. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education, vol. 1, pp. 207–213. ACM, Portland, OR, USA (2024)
Deeva, G., De Smedt, J., Saint-Pierre, C., Weber, R., De Weerdt, J.: Predicting student performance using sequence classification with time-based windows. Expert Syst. Appl. 209, 118182 (2022)
Du, J.Y., Chen, Y.M.: Applications and research of data mining in teaching. AMM 58–60, 2659–2663 (2011)
Kovacs, G.: Effects of in-video quizzes on MOOC lecture viewing. In: Proceedings of the Third (2016) ACM Conference on Learning @ Scale, pp. 31–40. ACM, Edinburgh, Scotland, UK (2016)
Qiu, J., et al.: Modeling and predicting learning behavior in MOOCs. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 93–102. ACM, San Francisco California USA (2016)
Sunar, A.S., Abbasi, R.A., Davis, H.C., White, S., Aljohani, N.R.: Modelling MOOC learners’ social behaviours. Comput. Hum. Behav. 107, 105835 (2020)
Xie, S.-T., He, Z.-B., Chen, Q., Chen, R.-X., Kong, Q.-Z., Song, C.-Y.: Predicting learning behavior using log data in blended teaching. Sci. Program. 2021, 1–14 (2021)
Wan, H., Liu, K., Yu, Q., Gao, X.: Pedagogical intervention practices: improving learning engagement based on early prediction. IEEE Trans. Learn. Technol. 12, 278–289 (2019)
Xing, W., Du, D., Bakhshi, A., Chiu, K.-C., Du, H.: Designing a transferable predictive model for online learning using a Bayesian updating approach. IEEE Trans. Learn. Technol. 14, 474–485 (2021)
Acknowledgments
This paper is supported by National Natural Science Foundation of China under Grant No. 61502198, 61472161, 61402195, 61103091, U19A2061; the Science and Technology Development Plan of Jilin Province under Grant No. 20210101414JC, 20160520099JH, 20190302117GX, 20180101334JC, 2019C053-3; Research Topic of Higher Education Teaching Reform in Jilin Province under Grant No. 20213F2QZ6100FV, JLJY202262573244; Jilin University Undergraduate Teaching Reform Research Project under Grant No. 2021XYB125.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, J. et al. (2024). A Student Performance Prediction Model Based on Feature Factor Transfer. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14885. Springer, Singapore. https://doi.org/10.1007/978-981-97-5495-3_29
Download citation
DOI: https://doi.org/10.1007/978-981-97-5495-3_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5494-6
Online ISBN: 978-981-97-5495-3
eBook Packages: Computer ScienceComputer Science (R0)