A neural network approach for students' performance prediction

F. Okubo, Atsushi Shimada, T. Yamashita, H. Ogata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

In this paper, we propose a method for predicting final grades of students by a Recurrent Neural Network (RNN) from the log data stored in the educational systems. We applied this method to the log data from 108 students and examined the accuracy of prediction. From the experimental results, comparing with multiple regression analysis, it is confirmed that an RNN is effective to early prediction of final grades.

Original languageEnglish
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages598-599
Number of pages2
ISBN (Electronic)9781450348706
DOIs
Publication statusPublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Other

Other7th International Conference on Learning Analytics and Knowledge, LAK 2017
CountryCanada
CityVancouver
Period3/13/173/17/17

Fingerprint

Recurrent neural networks
Students
Neural networks
Regression analysis

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Okubo, F., Shimada, A., Yamashita, T., & Ogata, H. (2017). A neural network approach for students' performance prediction. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 598-599). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029479

A neural network approach for students' performance prediction. / Okubo, F.; Shimada, Atsushi; Yamashita, T.; Ogata, H.

LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. p. 598-599.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Okubo, F, Shimada, A, Yamashita, T & Ogata, H 2017, A neural network approach for students' performance prediction. in LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, pp. 598-599, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 3/13/17. https://doi.org/10.1145/3027385.3029479
Okubo F, Shimada A, Yamashita T, Ogata H. A neural network approach for students' performance prediction. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery. 2017. p. 598-599 https://doi.org/10.1145/3027385.3029479
Okubo, F. ; Shimada, Atsushi ; Yamashita, T. ; Ogata, H. / A neural network approach for students' performance prediction. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. pp. 598-599
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