Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing
Modeling learner’s dynamic knowledge construction procedure and cognitive item difficulty for knowledge tracing
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DOI：
10.1007/s10489-020-01756-7
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发表时间：
2020-07
影响因子：
5.3
通讯作者：
Wenbin Gan;Yuan Sun;Xian Peng;Yi Sun
中科院分区：
文献类型：
--
作者：
Wenbin Gan;Yuan Sun;Xian Peng;Yi Sun
知识追踪(KT)是适应性学习中获取学习者当前知识状态以提供适应性服务的必要手段。一般来说，知识建构的过程是不断演变的，因为学生会随着时间的推移动态地学习和忘记。遗憾的是，就我们所知，现有的大多数方法只考虑了与学习或遗忘有关的一小部分信息，而在学习者的学习互动中利用丰富的信息来更准确地预测学习者在知识传授中的表现的问题仍然没有得到充分的探索。此外，现有的研究要么忽略了问题难度，要么假设问题难度是恒定的，这在实际学习过程中是不现实的，因为问题难度无疑会影响成绩，而且随着时间的推移，它给学习者带来的认知挑战也会有所不同。为此，我们提出了一种新的模型--KTM-DLF(Knowledge Tracing Machine By Modeled Accept Item Depair And Learning And Forging)，通过对学习者动态的知识构建过程和认知项目难度进行建模，来跟踪学习者在练习活动中的知识获取过程。具体地说，我们首先明确了认知项目难度的概念，并提出了一种基于学习者学习历史的自适应建模方法。然后，基于两个经典理论(学习曲线理论和艾宾豪斯遗忘曲线理论)，我们提出了一种模拟学习者随时间学习和遗忘的方法。最后，提出了KTM-DLF模型，将学习者的能力、认知项目难度和两个动态过程(学习和遗忘)结合在一起。然后，我们使用因式分解机器框架在高维中嵌入特征，并对成对交互进行建模，以提高模型的精度。在三个公开的真实世界数据集上进行了广泛的实验，结果证实了我们提出的模型的性能优于其他最先进的教育数据挖掘模型。
Knowledge tracing (KT) is essential for adaptive learning to obtain learners’ current states of knowledge for the purpose of providing adaptive service. Generally, the knowledge construction procedure is constantly evolving because students dynamically learn and forget over time. Unfortunately, to the best of our knowledge most existing approaches consider only a fragment of the information that relates to learning or forgetting, and the problem of making use of rich information during learners’ learning interactions to achieve more precise prediction of learner performance in KT remains under-explored. Moreover, existing work either neglects the problem difficulty or assumes that it is constant, and this is unrealistic in the actual learning process as problem difficulty affects performance undoubtedly and also varies overtime in terms of the cognitive challenge it presents to individual learners. To this end, we herein propose a novel model, KTM-DLF (Knowledge Tracing Machine by modeling cognitive item Difficulty and Learning and Forgetting), to trace the evolution of each learner’s knowledge acquisition during exercise activities by modeling his or her dynamic knowledge construction procedure and cognitive item difficulty. Specifically, we first specify the concept of cognitive item difficulty and propose a method to model the cognitive item difficulty adaptively based on learners’ learning histories. Then, based on two classical theories (the learning curve theory and the Ebbinghaus forgetting curve theory), we propose a method for modeling learners’ learning and forgetting over time. Finally, the KTM-DLF model is proposed to incorporate learners’ abilities, the cognitive item difficulty, and the two dynamic procedures (learning and forgetting) together. We then use the factorization machine framework to embed features in high dimensions and model pairwise interactions to increase the model’s accuracy. Extensive experiments have been conducted on three public real-world datasets, and the results confirm that our proposed model outperforms the other state-of-the-art educational data mining models.