Students' Performance Prediction Using Data of Multiple Courses by Recurrent Neural Network

被引:0
作者
Okubo, Fumiya [1 ]
Yamashita, Takayoshi [2 ]
Shimada, Atsushi [3 ]
Konomi, Shin'ichi [1 ]
机构
[1] Kyushu Univ, Fac Arts & Sci, Fukuoka, Japan
[2] Chubu Univ, Dept Comp Sci, Kasugai, Aichi, Japan
[3] Kyushu Univ, Fac Informat Sci & Elect Engn, Fukuoka, Japan
来源
25TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2017): TECHNOLOGY AND INNOVATION: COMPUTER-BASED EDUCATIONAL SYSTEMS FOR THE 21ST CENTURY | 2017年
关键词
learning analytics; recurrent neural network; learning log; prediction of student's performance;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:439 / 444
页数:6
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