论文标题
上下文感知的非线性和神经专注于等级预测的知识模型
Context-aware Non-linear and Neural Attentive Knowledge-based Models for Grade Prediction
论文作者
论文摘要
学生对未来课程的年级预测很重要，因为它可以在课程选择过程中帮助他们及其顾问，以及设计个性化学位计划并根据他们的表现进行修改。准确预测未来课程中学生成绩的成功方法之一是基于知识的回归模型（CKRM）。 CKRM学习了浅线性模型，这些模型可以预测学生的成绩，因为他/她的知识状态与目标课程之间的相似性。但是，学生在估计学生的知识状态和针对每个目标课程时可以拥有\ black {不同的贡献，而线性模型无法捕获。此外，CKRM和其他年级预测方法忽略了同时采用的课程对学生在目标课程中表现的影响。在本文中，我们提出了上下文感知的非线性和神经关注模型，这些模型可以从他/她的先前课程信息中更好地估算学生的知识状态，并为目标课程与并发课程之间的相互作用建模。与竞争方法相比，我们在一个大型现实世界数据集上的实验，该数据集由$ 1.5 $ m的成绩组成，显示了拟议模型在准确预测学生成绩方面的有效性。此外，神经关注模型学到的注意力重量可能有助于更好地设计其学位计划。
Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, prior courses taken by a student can have \black{different contributions when estimating a student's knowledge state and towards each target course, which} cannot be captured by linear models. Moreover, CKRM and other grade prediction methods ignore the effect of concurrently-taken courses on a student's performance in a target course. In this paper, we propose context-aware non-linear and neural attentive models that can potentially better estimate a student's knowledge state from his/her prior course information, as well as model the interactions between a target course and concurrent courses. Compared to the competing methods, our experiments on a large real-world dataset consisting of more than $1.5$M grades show the effectiveness of the proposed models in accurately predicting students' grades. Moreover, the attention weights learned by the neural attentive model can be helpful in better designing their degree plans.
