Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students' historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, they do not perform well for modeling complex problem solving in students.M ost importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge.However, for complex problems that involve many concepts at the same time, this assumption is deficient. In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students' performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students' discovered knowledge states and helps in discovering complex latent concepts in the problems.
翻译:旨在模拟学生知识水平和预测其表现的知识追踪(KT)是用户模型的最重要应用之一。现代KT方法模式和保持学生对一组课程概念的最新知识状态,根据学生在试图解决问题时的历史表现。然而,KT方法的设计是为了通过观察在智能教学系统中相对较小的解决问题的步骤来模拟知识。这些方法成功地应用于模拟学生知识,方法是观察学生解决简单问题的方法,但对于在学生中模拟复杂的问题解决来说效果不佳。重要的是,目前的模型假定所有问题尝试都同样有助于量化当前的学生知识。但是,对于同时涉及许多概念的复杂问题,这一假设是不足的。在本文中,我们指出并非所有尝试都同等重要,在发现学生的知识状况时,可以一起总结一些尝试来更好地代表学生业绩。我们提出了一种新颖的学生知识追踪方法,Granulal Amnk 以TEnsor 因素化为基础(GRATE), 动态选择的学生尝试在量化当前学生知识时,可以帮助量化当前概念的量化。对于同时涉及许多概念的复杂问题来说,这个假设是不足的。在本论文中,我们对学生在预测阶段的成绩分析中展示了我们所发现的成绩的预测中, 展示了我们所发现阶段的成绩的成绩的预测中, 展示的成绩的成绩的成绩分析显示的成绩。