Tri-Branch Convolutional Neural Networks for Top-k Focused Academic Performance Prediction

Cui, Chaoran; Zong, Jian; Ma, Yuling; Wang, Xinhua; Guo, Lei; Chen, Meng; Yin, Yilong

doi:10.1109/tnnls.2022.3175068

Cited by 7 publications

(1 citation statement)

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“…Providing appropriate guidance based on learners' individual characteristics, such as strengths and weaknesses, can also help learners understand their learning progress. Knowledge tracking (Cui et al, 2022) aims to predict future knowledge acquisition of students based on their learning history, thereby enhancing learning efficiency. The research in this paper covers the issue of knowledge tracking in the field of education.…”

Section: Introductionmentioning

confidence: 99%

DBGCN

Hu,

Li,

Zhang

et al. 2024

International Journal of Web-Based Learning and Teaching Technologies

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Given the extensive use of online learning in educational settings, Knowledge Tracing (KT) is becoming increasingly essential. KT primarily aims to predict a student's future knowledge acquisition based on their past learning activities, thus enhancing the efficiency of student learning. However, the effective acquisition of dynamic and evolving student representations from their historical records presents a formidable challenge. This paper introduces a Knowledge Tracing methodology predicated on Dynamic Broadth Graph Convolutional Networks (DBGCN). DBGCN leverages the mechanisms of breadth graph convolutional networks to proficiently acquire representations of questions and knowledge points from dynamically constructed topological graphs. It employs student state information as an attention query vector to augment student representations, thereby partially mitigating the challenge of capturing the dynamic shifts in user states. The effectiveness of our proposed DBGCN method has been demonstrated through extensive experimentation.

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Section: Introductionmentioning

confidence: 99%