Abstract
This paper discusses mining method based on decision tree in student resource management. With the increase of college enrollment, the number of students is also increasing. Oral teaching management produces a lot of data every day, and the existing teaching management system is becoming more and more problematic. With the wide application of database management system, the ability of data collection has been greatly improved and a large amount of data has been accumulated. Behind these data are very important and valuable information. The establishment of data mining technology is to analyze these data at a higher level and obtain the potential information of future operation and life. Data mining is a process of extracting hidden, unknown but potentially useful information and knowledge from a large number of incomplete, noisy, fuzzy and random fact data. How to transform these data information into knowledge representation, reasonably use these information to serve teaching management, scientifically guide teaching and improve teaching management level is an urgent topic for us to study. Data mining technology is a feasible and effective method to solve this problem.
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References
Letina, A., Dikovic, M.: Student teachers’ classroom management orientations and their beliefs on effective teaching behaviour, sodobna pedagogika. J. Contemp. Educ. Res. 72(138), 128–143 (2021)
Thamrin, R.M., Andriani, R.: Design web-based registration and data management of student thesis information system. SISFOTENIKA 11(1), 101 (2021)
Liu, Z., Cai, Y.: Construction management of student affairs center guided by the student-based idea: taking the business school of beijing institute of technology zhuhai institute as an example. In: 1st International Conference on Education: Current Issues and Digital Technologies (ICECIDT 2021) (2021)
Mao, L., Zhang, W.: Analysis of entrepreneurship education in colleges and based on improved decision tree algorithm and fuzzy mathematics. J. Intell. Fuzzy Syst. 40(2), 2095–2107 (2021)
Dinesh, T.: Higher classification of fake political news using decision tree algorithm over naive bayes algorithm. Revista Gestão Inovação e Tecnologias 11(2), 1084–1096 (2021)
Hardiani, T.: Comparison of Naive Bayes Method, K-NN (K-Nearest Neighbor) and Decision Tree for Predicting the Graduation of ‘Aisyiyah University Students of Yogyakarta. Int. J. Health Sci. Technol. 2(1) (2021)
Arigi, A.M., Park, G., Kim, J.: An approach to analyze diagnosis errors in advanced main control room operations using the cause-based decision tree method. Energies 14 (2021)
Rahmawati, A., Yan, R., Riana, D.: Deteksi defect coffee pada citra tunggal green beans menggunakan metode ensamble decision tree. Technol. Commun. 20(2), 198–209 (2021)
Djordjevic, D., Cockalo, D., Bogetic, S., et al.: Predicting entrepreneurial intentions among the youth in serbia with a classification decision tree model with the QUEST algorithm. Mathematics 9 (2021)
Gatwood, J., Hohmeier, K., Kocak, M., et al.: Acceptance of productivity software as a course management and collaboration tool among student pharmacists. Currents in Pharmacy Teach. Learn. 13(4) (2021)
Boomars, R., et al.: Optimizing airway management and ventilation during prehospital advanced life support in out-of-hospital cardiac arrest: a narrative review. Best Practice Res. Clin. Anaesthesiol. 35(1), 67–82 (2021)
Scott, A.T., Dpm, C., Dpm, T., et al.: Review of achilles tendon reattachment using double-row knotted and knotless techniques in the management of insertional achilles tendinopathy (2021)
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, H. (2023). Application of Classification Mining Technology Based on Decision Tree in Student Resource Management. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 465. Springer, Cham. https://doi.org/10.1007/978-3-031-23950-2_17
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DOI: https://doi.org/10.1007/978-3-031-23950-2_17
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