Early Warning Model for Learning based on Bidirectional LSTM

Yufan Li, Huifu Zhang
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Abstract

In this paper, we proposed a methodology and a model for identifying at-risk students. Our model is based on a deep bidirectional long short-term memory network(deep BiLSTM) and we applied it to the data from 2032 students. We carried out 2 experiments to predict students achievement at different steps of the semester, to test three data balancing techniques and to compare our model versus two classical classification algorithms. Results showed that our model was capable of identifying at-risk students at the middle of the semester and trustworthy to be an early warning model.
基于双向LSTM的学习预警模型
在本文中,我们提出了一种方法和模型来识别有风险的学生。我们的模型基于深度双向长短期记忆网络(deep BiLSTM),并将其应用于2032名学生的数据。我们进行了两个实验来预测学生在学期不同阶段的成绩,测试了三种数据平衡技术,并将我们的模型与两种经典分类算法进行了比较。结果表明,我们的模型能够在学期中期识别出有风险的学生,并且值得信赖,是一个早期预警模型。
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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