Knowledge tracing aims to track students' knowledge status over time to predict students' future performance accurately. Markov chain-based knowledge tracking (MCKT) models can track knowledge concept mastery probability over time. However, as the number of tracked knowledge concepts increases, the time complexity of MCKT predicting student performance increases exponentially (also called explaining away problem. In addition, the existing MCKT models only consider the relationship between students' knowledge status and problems when modeling students' responses but ignore the relationship between knowledge concepts in the same problem. To address these challenges, we propose an inTerpretable pRobAbilistiC gEnerative moDel (TRACED), which can track students' numerous knowledge concepts mastery probabilities over time. To solve \emph{explain away problem}, we design Long and Short-Term Memory (LSTM)-based networks to approximate the posterior distribution, predict students' future performance, and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly. To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems. We conduct experiments with four real-world datasets in three knowledge-driven tasks. The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students' future performance and can learn the relationship among students, knowledge concepts, and problems from students' exercise sequences. We also conduct several case studies. The case studies show that TRACED exhibits excellent interpretability and thus has the potential for personalized automatic feedback in the real-world educational environment.
翻译:跟踪学生的知识,以便跟踪学生的知识状况和问题,从而准确预测学生的未来业绩。为了应对这些挑战,我们建议采用基于链锁的知识跟踪模型(MCKT),跟踪知识概念的掌握概率。然而,随着跟踪知识概念数量的增加,MCKT预测学生业绩的时间复杂性会急剧增加(也称为解答问题 )。此外,现有的MCKT模型在模拟学生反应时,只考虑学生知识状况和问题之间的关系,而忽视同一问题中的知识概念之间的关系。为了应对这些挑战,我们建议采用基于链锁链的知识跟踪模型(MCKT)模型(MCKT),跟踪知识概念的掌握概率概率概率。为了解决emph{解释问题,MCMT,我们设计了基于长和短期记忆(LSTM)的网络,以近距离分布,预测学生未来业绩,并提议采用超音路算法算法算出LSTMS和稳定图形模型。为了更好地模拟学生的演练,我们提议了对学生的演算方法,我们建议用三个互动的实验模型来解释学生的实验性模型,我们用3个案例的演算模型, 学习模型来展示了学生们的成绩,我们用三种实验性学模型, 学习模型来展示了三种实验性学状况, 学习模型,我们用三个的知识状况, 学习模型来展示了三种实验模型来显示了学生们的学习状态,我们的知识状况,我们用三种实验性的研究状态,我们的知识状态, 学习状态, 学习状态,我们的知识状态, 学习模式,我们用三种研究了三种研究了三种实验状态, 学习状态,我们用三种研究了三种研究了三种研究了三种实验性学状况,我们的知识状态, 学习状态,我们用三种实验性研究了三种研究了三种研究了三种实验性研究了三种实验性研究了三种实验性的研究模式,我们的知识, 学习状态, 学习模式, 学习模式, 学习状态, 学习状态, 学习学学学状况,我们用三种研究,我们用的方法,我们用的方法,我们用三种研究,我们用三种研究,我们用三种实验性的研究, 学习学学学学学学学学学状况, 学习模式, 学习模式, 学习学状态, 学习学学学学学学学状况,我们学学学学学学学