Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
复制标题
使用 RNN-GRU 联合神经网络进行在线高危学生识别
DOI：
10.3390/info11100474
复制
发表时间：
2020-10-01
期刊：
影响因子：
3.1
通讯作者：
Jiang, Bo
中科院分区：
文献类型：
--
作者：
He, Yanbai;Chen, Rui;Jiang, Bo
尽管在线学习平台在现代社会中逐渐变得司空见惯，但在虚拟学习环境中，学习者的高辍学率和严肃的学习成绩需要更多的关注。本研究的目的是利用VLE的统计个人传记信息和连续行为数据来预测学生在某门课程连续运行时的表现。为了实现这一目标，提出了一种新的递归神经网络(RNN)门控递归单元(GRU)联合神经网络，用于拟合静态数据和序列数据，并采用数据补全机制来填充缺失的流数据。为了考虑学习数据的时序关系，首先考虑了三种时间序列深度神经网络算法：简单RNN、GRU和LSTM作为基线模型。他们在识别高危学生方面的表现进行了比较。在开放大学学习分析数据集(OULAD)上的实验结果表明，GRU和SIMPLE RNN等简单方法比相对复杂的LSTM模型具有更好的结果。结果还表明，不同的模型有不同的峰值表现时间，这导致所提出的联合模型在学期末对高危学生的预测准确率达到80%以上。
Although online learning platforms are gradually becoming commonplace in modern society, learners' high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students' performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.