A neural network approach for students' performance prediction

F. Okubo; A. Shimada; T. Yamashita; H. Ogata

doi:10.1145/3027385.3029479

A neural network approach for students' performance prediction

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

Division for Theoretical Natural Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

112 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 language	English
Title of host publication	LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publication	Understanding, Informing and Improving Learning with Data
Publisher	Association for Computing Machinery
Pages	598-599
Number of pages	2
ISBN (Electronic)	9781450348706
DOIs	https://doi.org/10.1145/3027385.3029479
Publication status	Published - Mar 13 2017
Event	7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada Duration: Mar 13 2017 → Mar 17 2017

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	7th International Conference on Learning Analytics and Knowledge, LAK 2017
Country/Territory	Canada
City	Vancouver
Period	3/13/17 → 3/17/17

All Science Journal Classification (ASJC) codes

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

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10.1145/3027385.3029479

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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). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029479

A neural network approach for students' performance prediction. / Okubo, F.; Shimada, A.; Yamashita, T. et al.
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 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference 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. ACM International Conference Proceeding Series, 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. (ACM International Conference Proceeding Series). doi: 10.1145/3027385.3029479

Okubo, F. ; Shimada, A. ; Yamashita, T. et al. / 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 (ACM International Conference Proceeding Series).

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A neural network approach for students' performance prediction

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