A Course Agnostic Approach to Predicting Student Success from VLE Log Data Using Recurrent Neural Networks

被引：19

作者：

Corrigan, Owen ^{[1
]}

Smeaton, Alan F. ^{[1
]}

机构：

[1] Dublin City Univ, Insight Ctr Data Analyt, Dublin 9, Ireland

来源：

DATA DRIVEN APPROACHES IN DIGITAL EDUCATION | 2017年 / 10474卷

基金：

爱尔兰科学基金会;

关键词：

Learning analytics; Student intervention; Machine learning; LEARNING ANALYTICS;

D O I：

10.1007/978-3-319-66610-5_59

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We describe a method of improving the accuracy of a learning analytics system through the application of a Recurrent Neural Network over all students in a University, regardless of course. Our target is to discover how well a student will do in a class given their interaction with a virtual learning environment. We show how this method performs well when we want to predict how well students will do, even if we do not have a model trained based on their specific course.

引用

页码：545 / 548

页数：4

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