Abstract
With the development of the internet, campus networks and various information systems in colleges have been constructed, and the data generated by accessing the internet can reflect students’ learning and daily behaviors. Traditional methods have difficulty effectively and accurately analyzing the students’ behavior. In this paper, a method is designed to mine students’ behavior patterns. The main contributions of this article include the following: (1) We extract student behavior features from network log data, a total of 44 students’ behavior features were selected in the experiment. (2) We gain 11 online behavior patterns with association rules from students’ daily behaviors. (3) We build a student prediction model based on students’ behavior patterns and the final model predicts the students’ grades with an accuracy of 87.62%. This paper presents a method to analyze students’ behavior patterns. Moreover, reasonable suggestions and effective and credible data support for precise and dynamic decision-making in colleges and universities are provided.
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
Livari, N., Sharma, S., Venta-Olkkonen, L.: Digital transformation of everyday life-how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care?. Int. J. Inf. Manag. 55, 102183 (2020)
Deliens, T., Defbrche, B., De Bourdeaudhuij, L., et al.: Determinants of physical activity and sedentary behaviour in university students: a qualitative study using focus group discussions. BMC Public Health 15(1), 1–9 (2015)
Aman, F., Rauf, A., Ali, R., et al.: A predictive model for predicting students academic performance. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (USA), pp. 1–4 (2019)
Haefner, N., Wincent, J., Panda, V., et al.: Artificial intelligence and innovation management: a review, framework, and research agenda. Technol. Forecast. Soc. Change 162, 120392 (2021)
Arifin, M., Widowati, F., et al.: Comparative analysis on educational data mining algorithm to predict academic performance. In: 2021 International Seminar on Application for Technology of Information and Communication (iSemantic) (2021)
Bonde, S.N., Kirange, D.K.: Survey on evaluation of student’s performance in educational data mining. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (2018)
Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. (2019)
Kausar, S., Huahu, X., Hussain, I., et al.: Integration of data mining clustering approach in the personalized E-learning system. IEEE Access, 1 (2018)
Doi, S.M.: Use of classification technique in educational data mining. In: 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE) (2021)
Manzanares, M., Hemanz, R., Yaez, M., et al.: Eye-tracking technology and data-mining techniques used for a behavioral analysis of adults engaged in learning processes. J. Vis. Exp. (172) (2021)
Hidalgo, N.C., Ger, P.M., Valentin, L.: Using meta-learning to predict student performance in virtual learning environments. Appl. Intell., 1–14 (2021)
Iqbal, N., Jamil, F., Ahmad, S., et al.: Toward effective planning and management using predictive analytics based on rental book data of academic libraries. IEEE Access 8, 81978–81996 (2020)
Mou, C., Zhou, Q., Zou, X.: Understanding and predicting poor performance of computer science students from short time series test results. Int. J. Eng. Educ. 33(6App.), 1803–1814 (2017)
Chen, P., Lu, Y., Zheng, V.W., et al.: KnowEdu: a system to construct knowledge graph for education. IEEE Access 6, 31553–31563 (2018)
Luo, M.: Research on students? Mental health based on data mining algorithms. J. Healthcare Eng. 2021 (2021)
Costa, J.D.J., Bernardini, F., Artigas, D., et al.: Mining direct acyclic graphs to find frequent substructures-an experimental analysis on educational data. Inf. Sci. 482, 266–278 (2019)
Prabowo, H., Hidayat, A.A., Cenggoro, T.W., et al.: Aggregating time series and tabular data in deep learning model for university students * GPA prediction. IEEE Access 9, 87370–87377 (2021)
Zhou, Q., Quan, W., Zhong, Y., et al.: Predicting high-risk students using Internet access logs. Knowl. Inf. Syst. 55(2), 393–413 (2018)
Yang, K.: The construction of sports culture industry growth forecast model based on big data. Pers. Ubiquit. Comput. 24(1), 5–17 (2020)
Ni, L., Li, C., Wang, X., et al.: DP-MCDBSCAN: differential privacy preserving multi-core DBSCAN clustering for network user data. IEEE Access 6, 21053–21063 (2018)
Wu, D., Shen, Y.: English feature recognition based on GA-BP neural network algorithm and data mining. Comput. Intell. Neurosci. 2021 (2021)
Dong, Y., Yang, C., Zhang, Y.: Deep metric learning with online hard mining for hyperspectral classification. Remote Sens. 13(7), 1368 (2021)
Ahmed, S., Lee, Y., Hyun, S.H., et al.: Feature selection-based detection of covert cyber deception assaults in smart grid communications networks using machine learning. IEEE Access 6, 27518–27529 (2018)
Acknowledgments
Thanks for the acknowledgement provided by China Academy of Information and Communications Technology and Industry-University-Research Innovation Fund for Chinese Universities: 2021KSA01002.
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
Qu, S., Li, D., Cao, F. (2024). Research on College Students’ Behavioral Patterns Based on Big Data. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Educational Digitalization. ICCSE 2023. Communications in Computer and Information Science, vol 2025. Springer, Singapore. https://doi.org/10.1007/978-981-97-0737-9_17
Download citation
DOI: https://doi.org/10.1007/978-981-97-0737-9_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0736-2
Online ISBN: 978-981-97-0737-9
eBook Packages: Computer ScienceComputer Science (R0)