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

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

10 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 - 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
Country/TerritoryNew Zealand
CityChristchurch
Period12/4/1712/8/17

All Science Journal Classification (ASJC) codes

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

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