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Informative representations for forgetting-robust knowledge tracing

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Abstract

Tracing a student’s knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing historical records on online education platforms. Most studies have focused on a student’s skill with interactions sequence to predict the probability of correctly answering the latest question. However, they still suffer from the challenge of information sparsity and student forgetting. Specifically, the relationship between question and skill, and the features related to question texts have not been integrated to enrich information exploration. Besides, modeling forgetting behavior remains a challenge in assessing a student’s learning gains. In this paper, we present a novel model, namely Informative Representations for Forgetting-Robust Knowledge Tracing (IFKT). IFKT utilizes a light graph convolutional network to capture various relational structures via embedding propagation. Then, the embeddings are assembled with rich interaction features separately as the powerful representation. Furthermore, attention weights assignments are individualized using the relative positions, in addition to the relevance between the current question with historical interaction representations. Finally, we compare IFKT against seven knowledge tracing baselines on three real-world benchmark datasets, demonstrating the superiority of the proposed model.

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Notes

  1. https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data/skill-builder-data-2009-2010.

  2. https://sites.google.com/site/assistmentsdata/datasets/2012-13-school-data-with-affect.

  3. https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp.

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Funding

The authors thank all the participants in this study for their time and valuable inputs. This work was supported by the National Key Research and Development Program of China under grant number 2023YFC3305704 and National Natural Science Foundation of China under grant number 62377015.

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  1. School of Computer Science, South China Normal University, Guangzhou, 510631, China

    Zhiyu Chen, Zhilong Shan & Yanhua Zeng

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  1. Zhiyu Chen
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  2. Zhilong Shan
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Correspondence to Zhilong Shan.

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Chen, Z., Shan, Z. & Zeng, Y. Informative representations for forgetting-robust knowledge tracing. User Model User-Adap Inter (2024). https://doi.org/10.1007/s11257-024-09391-4

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