In recent decades, more teachers are using question generators to provide students with online homework. Learning-to-rank (LTR) methods can partially rank questions to address the needs of individual students and reduce their study burden. Unfortunately, ranking questions for students is not trivial because of three main challenges: (1) discovering students’ latent knowledge and cognitive level is difficult, (2) the content of quizzes can be totally different but the knowledge points of these quizzes may be inherently related, and (3) ranking models based on supervised, semisupervised, or reinforcement learning focus on the current assignment without considering past performance. In this work, we propose KFRank, a knowledge-fusion ranking model based on reinforcement learning, which considers both a student’s assignment history and the relevance of quizzes with their knowledge points. First, we load students’ assignment history, reorganize it using knowledge points, and calculate the effective features for ranking in terms of the relation between a student’s knowledge cognitive and the question. Then, a similarity estimator is built to choose historical questions, and an attention neural network is used to calculate the attention value and update the current study state with knowledge fusion. Finally, a rank algorithm based on a Markov decision process is used to optimize the parameters. Extensive experiments were conducted on a real-life dataset spanning a year and we compared our model with the state-of-the-art ranking models (e.g., ListNET and LambdaMART) and reinforcement-learning methods (such as MDPRank). Based on top- nDCG values, our model outperforms other methods for groups of average and weak students, whose study abilities are relatively poor and thus their behaviors are more difficult to predict.

中文翻译：

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具有注意网络的知识融合排名系统，用于提出作业建议

在最近的几十年中，越来越多的教师使用问题生成器为学生提供在线作业。等级学习（LTR）方法可以对问题进行部分排名，以满足单个学生的需求并减轻他们的学习负担。不幸的是，由于三个主要挑战，给学生排列问题的难度并不小：（1）发现学生的潜在知识和认知水平很困难；（2）测验的内容可能完全不同，但这些测验的知识点可能是固有的相关；以及（3）基于监督学习，半监督学习或强化学习的排名模型将重点放在当前作业上，而不考虑过去的表现。在这项工作中，我们提出了KFRank，这是一种基于强化学习的知识融合排名模型，它既考虑了学生的作业历史，又考虑了测验与他们的知识点的相关性。首先，我们加载学生的作业历史记录，使用知识点对其进行重组，并根据学生的知识认知与问题之间的关系来计算排名的有效特征。然后，建立相似度估计器以选择历史问题，并使用注意力神经网络计算注意力值并通过知识融合更新当前学习状态。最后，基于马尔可夫决策过程的秩算法用于优化参数。在过去一年的真实数据集上进行了广泛的实验，我们将我们的模型与最新的排名模型（例如ListNET和LambdaMART）和强化学习方法（例如MDPRank）进行了比较。 nDCG值，对于学习能力相对较弱且行为难以预测的中等和弱势学生群体，我们的模型优于其他方法。