Dynamic Key-Value Memory Networks With Rich Features for Knowledge Tracing.
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In this paper, the authors proposed a new exercise record representation method, which integrates the features of students' behavior with those of the learning ability, thereby improving the performance of knowledge tracing.Abstract:
Knowledge tracing is an important research topic in student modeling. The aim is to model a student's knowledge state by mining a large number of exercise records. The dynamic key-value memory network (DKVMN) proposed for processing knowledge tracing tasks is considered to be superior to other methods. However, through our research, we have noticed that the DKVMN model ignores both the students' behavior features collected by the intelligent tutoring system (ITS) and their learning abilities, which, together, can be used to help model a student's knowledge state. We believe that a student's learning ability always changes over time. Therefore, this article proposes a new exercise record representation method, which integrates the features of students' behavior with those of the learning ability, thereby improving the performance of knowledge tracing. Our experiments show that the proposed method can improve the prediction results of DKVMN.read more
Citations
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Multiple Learning Features–Enhanced Knowledge Tracing Based on Learner–Resource Response Channels
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TL;DR: In this article , the authors proposed the multiple learning features, enhanced knowledge tracing (MLFKT) framework, which constructs learner-resource response (LRR) channels based on psychometric theory, establishing stronger intrinsic connections among learning features and overcoming the limitations of the item response theory.
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Modeling knowledge proficiency using multi-hierarchical capsule graph neural network
Zeyu He,Wang Li,Yonghong Yan +2 more
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Plastic gating network: Adapting to personal development and individual differences in knowledge tracing
TL;DR: Zhang et al. as discussed by the authors adopted plastic weights in calculating gates and cell input in recurrent units, thus allowing the model to develop time and individual-specific parameters that adapt to learners' personal development and individual differences after training.
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Application of Deep Self-Attention in Knowledge Tracing.
TL;DR: Wang et al. as discussed by the authors proposed Deep Self-Attentive Knowledge Tracing (DSAKT) based on the data of PTA, an online assessment system used by students in many universities in China.
Proceedings ArticleDOI
A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges
TL;DR: In this article, the authors focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases, and selected studies have been filtered using inclusion and exclusion criteria.
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