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Quiz-based Knowledge Tracing by Shen, Shuanghong; Chen, Enhong; Xu, Bihan; Liu, Qi; Huang, Zhenya; Zhu, Linbo; Su, Yu is a scholarly article available to read on EtoBox.

What is Quiz-based Knowledge Tracing about?

Knowledge tracing (KT) aims to assess individuals' evolving knowledge states according to their learning interactions with different exercises in online learning systems (OIS), which is critical in supporting decision-making for subsequent intelligent services, such as personalized learning source recommendation. Existing researchers have broadly studied KT and developed many effective methods. However, most of them assume that students' historical interactions are uniformly distributed in a continuous sequence, ignoring the fact that actual interaction sequences are organized based on a series of quizzes with clear boundaries, where interactions within a quiz are consecutively completed, but interactions across different quizzes are discrete and may be spaced over days. In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions. Specifically, as students' interactions within a quiz are continuous and have the same or similar knowledge concepts, we design the adjacent gate followed by a global average pooling layer to capture the intra-quiz short-term knowledge influence. Then, as

Author

Shen, Shuanghong; Chen, Enhong; Xu, Bihan; Liu, Qi; Huang, Zhenya; Zhu, Linbo; Su, Yu

Published

2023

Language

EN