Skip to main content

2019 | OriginalPaper | Buchkapitel

Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

How to effectively mine students’ behavior data is an important content to improve the level of student information management. The platform of student behavior analysis and prediction based on campus big data is established, and the value of big data produced by students’ campus behavior is analyzed. The behavior data of students’ consumption laws, living habits and learning conditions are collected, modeled, analyzed and excavated around the large data environment, and the student behavior is predicted and warned by the stratified model of students’ behavior characteristics. The experimental results verify the effectiveness of the methods used, and the behavior characteristics can be analyzed according to the behavior characteristics of the students, and the students’ behavior will be guided to the overall health direction in a timely manner.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)CrossRef Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)CrossRef
2.
Zurück zum Zitat Sun, A., Ji, T., Wang, J., et al.: Wearable mobile internet devices involved in big data solution for education. Int. J. Embed. Syst. 8(4), 293 (2016)CrossRef Sun, A., Ji, T., Wang, J., et al.: Wearable mobile internet devices involved in big data solution for education. Int. J. Embed. Syst. 8(4), 293 (2016)CrossRef
3.
Zurück zum Zitat Hasbun, T., Araya, A., Villalon, J.: Extracurricular activities as dropout prediction factors in higher education using decision trees. In: 2016 IEEE 16 International Conference on Advanced Learning Technologies (ICALT), pp. 242–244 (2016) Hasbun, T., Araya, A., Villalon, J.: Extracurricular activities as dropout prediction factors in higher education using decision trees. In: 2016 IEEE 16 International Conference on Advanced Learning Technologies (ICALT), pp. 242–244 (2016)
4.
Zurück zum Zitat Hammoud, S.: MapReduce network enabled algorithms for classification based on association rules. Brunel University School of Engineering and Design Ph.D. theses (2011) Hammoud, S.: MapReduce network enabled algorithms for classification based on association rules. Brunel University School of Engineering and Design Ph.D. theses (2011)
5.
Zurück zum Zitat Maillo, J., Triguero, I., et al.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl. Based Syst. 117, 3–15 (2017)CrossRef Maillo, J., Triguero, I., et al.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl. Based Syst. 117, 3–15 (2017)CrossRef
6.
Zurück zum Zitat Arias, J., Gamez, J.A., Puerta, J.M.: Learning distributed discrete Bayesian network classifiers under Map Reduce with Apache spark. Knowl. Based Syst. 117, 16–26 (2017)CrossRef Arias, J., Gamez, J.A., Puerta, J.M.: Learning distributed discrete Bayesian network classifiers under Map Reduce with Apache spark. Knowl. Based Syst. 117, 16–26 (2017)CrossRef
Metadaten
Titel
Analysis and Prediction Method of Student Behavior Mining Based on Campus Big Data
verfasst von
Liyan Tu
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-36405-2_36

Premium Partner