基于表示学习的在线学习交互质量评价方法

Evaluation of Quality of Interaction in Online Learning Based on Representation Learning

当今的教育模式发生着非常重大的变革,教育正在向泛在化、智能化、个性化的方向发展。以Massive Open Online Courses(MOOCs)为代表的在线教育逐渐进入大众视野,在线教育中的交互性成为了决定在线学习质量的关键。研究表明,学习过程中的交互为学习者提供了有效且高效的帮助和支持,对学习过程的评价反馈可以有效地提高学习效果。在教育领域,对学习者和学习资源之间的交互进行建模至关重要,表示学习技术为学习者和学习资源之间的顺序交互建模提供了具体方案。文中首先建立在线学习的交互网络模型,然后使用两个循环神经网络将网络中的学习者和学习资源节点嵌入到一个欧氏空间中,并提出交互质量评价指标,以判断学习者的学习效果是否达到预期。在实际数据集上的实验证明了所提方法的有效性。

The model of education today has undergone a very significant change,and education is developing in the direction of ubiquity,intelligence and individuation.Online education,represented by MOOCs,is gradually coming into the public field of vision,and the interactivity in online education has become the key to determine the quality of online learning.Some researches show that the interaction in the learning process provides efficient help and effective support for learners,and the feedback of learning process evaluation can effectively improve the interaction effect of learning.Modeling interactions between learners and learning resources is crucial in domains such as e-commerce.Representation learning presents a method to model the sequential interactions between learners and learning resources.Firstly,an interactive network of online learning is established.And then,the learners and learning resources can be embedded into a Euclidean space by using two recurrent neural networks.The evaluation index of the quality of interaction is proposed,which can judge whether the learner’s learning effect is up to the expectation.The experiments on real datasets reveal the effectiveness of the proposed method.