论文标题
基于行为的学习者建模的深度学习方法
A Deep Learning Approach to Behavior-Based Learner Modeling
论文作者
论文摘要
电子学习的日益普及，通过诸如预测分析和内容建议等技术来改善在线教育的需求。在本文中，我们研究了学习者成果预测，即对他们在课程结束时的表现的预测。我们提出了一个新颖的两个分支决策网络，用于绩效预测，其中包含了两个重要因素：学习者如何在课程中进步以及内容如何在课程中进行。我们结合了点击式功能，这些功能记录了学习者在学习时采取的每个动作，以及通过预训练的手套嵌入生成的文本功能。为了评估我们提出的网络的性能，我们从旨在企业培训的短暂在线课程中收集数据，并在其上评估神经网络和非神经网络的算法。我们提出的算法达到95.7％的精度和0.958 AUC得分，其表现优于所有其他模型。结果还表明，行为特征和文本特征的组合比行为特征更具预测性，并且神经网络模型在捕获用户行为和课程内容之间的联合关系方面具有强大的功能。
The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.
