论文标题
SPOC学习者的最终年级预测基于新型采样批汇总嵌入神经网络方法
SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method
论文作者
论文摘要
近年来，小型私人在线课程（SPOC）的迅速增长，能够高度定制和个性化以适应可变的教育请求，其中探索了机器学习技术以总结和预测学习者的表现，主要集中于最终成绩。但是，问题在于，在SPOC上学习者的最终成绩通常是严重的失衡，这障碍了预测模型的培训。为了解决此问题，本文开发了一种采样批汇总嵌入的深神经网络（SBNEDNN）方法。首先，定义了一个组合的指标来衡量数据的分布，然后建立规则以指导采样过程。其次，将批准（BN）修改层嵌入到完整的连接神经网络中，以解决数据不平衡问题。其他三种深度学习方法的实验结果证明了该方法的优越性。
Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.
