学生成绩预测的神经网络方法

Proceedings of the Seventh International Learning Analytics & Knowledge Conference Pub Date : 2017-03-13 DOI:10.1145/3027385.3029479

Fumiya Okubo, Takayoshi Yamashita, Atsushi Shimada, H. Ogata

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引用次数: 144

摘要

在本文中，我们提出了一种利用循环神经网络(RNN)从存储在教育系统中的日志数据中预测学生期末成绩的方法。我们将该方法应用于108名学生的日志数据，并检验了预测的准确性。通过与多元回归分析的对比，验证了RNN对期末成绩的早期预测是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A neural network approach for students' performance prediction

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.

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Proceedings of the Seventh International Learning Analytics & Knowledge Conference

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