论文标题
应用NLP的最新创新到MOOC学生课程轨迹建模
Applying Recent Innovations from NLP to MOOC Student Course Trajectory Modeling
论文作者
论文摘要
本文提出了几种策略，可以改善MOOC学生课程轨迹建模的基于神经网络的预测方法，并将以前应用的多个想法应用于解决NLP（自然语言处理）任务。特别是，本文通过两种形式的正则化研究了LSTM网络以及最近引入的变压器体系结构。
This paper presents several strategies that can improve neural network-based predictive methods for MOOC student course trajectory modeling, applying multiple ideas previously applied to tackle NLP (Natural Language Processing) tasks. In particular, this paper investigates LSTM networks enhanced with two forms of regularization, along with the more recently introduced Transformer architecture.
