Students' performance prediction using data of multiple courses by recurrent neural network

Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, Shinichi Konomi

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

5 Citations (Scopus)

Abstract

In this paper, we show a method to predict students' final grades using a recurrent neural network (RNN). An RNN is a variant of a neural network that handles time series data. For this purpose, the learning logs from 937 students who attended one of six courses by two teachers were collected. Nine kinds of learning logs are selected as the input of the RNN. We examine the prediction of final grades, where the training data and test data are the logs of courses conducted in 2015 and in 2016, respectively. We also show a way to identify the important learning activities for obtaining a specific final grade by observing the values of weight of the trained RNN.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
EditorsAhmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen
PublisherAsia-Pacific Society for Computers in Education
Pages439-444
Number of pages6
ISBN (Print)9789869401265
Publication statusPublished - Jan 1 2017
Event25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
Duration: Dec 4 2017Dec 8 2017

Publication series

NameProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings

Other

Other25th International Conference on Computers in Education, ICCE 2017
CountryNew Zealand
CityChristchurch
Period12/4/1712/8/17

Fingerprint

Recurrent neural networks
neural network
Students
performance
student
learning
Time series
time series
Neural networks
teacher
Values

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Information Systems
  • Hardware and Architecture
  • Education

Cite this

Okubo, F., Yamashita, T., Shimada, A., & Konomi, S. (2017). Students' performance prediction using data of multiple courses by recurrent neural network. In A. F. Mohd Ayub, A. Mitrovic, J-C. Yang, S. L. Wong, & W. Chen (Eds.), Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings (pp. 439-444). (Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings). Asia-Pacific Society for Computers in Education.

Students' performance prediction using data of multiple courses by recurrent neural network. / Okubo, Fumiya; Yamashita, Takayoshi; Shimada, Atsushi; Konomi, Shinichi.

Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings. ed. / Ahmad Fauzi Mohd Ayub; Antonija Mitrovic; Jie-Chi Yang; Su Luan Wong; Wenli Chen. Asia-Pacific Society for Computers in Education, 2017. p. 439-444 (Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings).

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

Okubo, F, Yamashita, T, Shimada, A & Konomi, S 2017, Students' performance prediction using data of multiple courses by recurrent neural network. in AF Mohd Ayub, A Mitrovic, J-C Yang, SL Wong & W Chen (eds), Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings. Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings, Asia-Pacific Society for Computers in Education, pp. 439-444, 25th International Conference on Computers in Education, ICCE 2017, Christchurch, New Zealand, 12/4/17.
Okubo F, Yamashita T, Shimada A, Konomi S. Students' performance prediction using data of multiple courses by recurrent neural network. In Mohd Ayub AF, Mitrovic A, Yang J-C, Wong SL, Chen W, editors, Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings. Asia-Pacific Society for Computers in Education. 2017. p. 439-444. (Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings).
Okubo, Fumiya ; Yamashita, Takayoshi ; Shimada, Atsushi ; Konomi, Shinichi. / Students' performance prediction using data of multiple courses by recurrent neural network. Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings. editor / Ahmad Fauzi Mohd Ayub ; Antonija Mitrovic ; Jie-Chi Yang ; Su Luan Wong ; Wenli Chen. Asia-Pacific Society for Computers in Education, 2017. pp. 439-444 (Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings).
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