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home / papers / 2022 / 深度知识追踪模型综述和性能比较

深度知识追踪模型综述和性能比较

Summary

Knowledge tracing (KT) infers how well a learner has mastered each knowledge concept from their record of exercise attempts, then uses that estimate to forecast their next answers. Classical approaches such as IRT, BKT and factor-analysis models (LFA, PFA, IFA) depend on hand-crafted features and fixed theoretical assumptions. As a result, they overlook links between concepts and the effects of forgetting. This Chinese-language survey from East China Normal University reviews the deep models that have largely replaced them since DKT (Piech et al., 2015).

The authors sort the models into four families by backbone: recurrent networks, memory networks, self-attention networks, and other designs such as CNN, GNN, federated and transfer learning. Within the first three families, they further group work by the kind of improvement: changing the architecture, adding extra interaction features, or building in educational theory. They then benchmark eight representative models on five public datasets. The paper closes with real teaching use cases and open research questions. It offers a structured map and an empirical baseline for anyone building learner models for adaptive tutoring.

Key points

  • Bibliographic note: the paper appeared in 软件学报 (Journal of Software) 34(3):1365–1395 in 2023, with DOI 10.13328/j.cnki.jos.006715. It was published online on 2022-07-22, which explains the "2022" date.
  • Recurrent-network models reviewed: stacked LSTM (Sha et al.), EKT, KQN, tree-based DKT, EERNN, DKTS, qDKT, GIKT, DKT-DSC, DKT-F, KPT and IEKT. Memory-network models trace back to Zhang et al., and self-attention models to Pandey et al.
  • Examples of added features:
    • EERNN encodes exercise text with a Bi-LSTM.
    • Tong et al. use BERT and TextCNN to derive difficulty and concept features.
    • DKT-F adds repeat-interval, sequence-interval and past-attempt-count inputs to model forgetting.
    • DKT-DSC periodically clusters students by ability with K-means.
  • Benchmark scope: 8 models are compared on 5 public datasets. The experimental section was missing from the supplied text, so dataset names and per-model scores are not recorded here.
  • Original DKT limitation: it treats each exercise as covering a single concept. It therefore cannot tell apart different exercises on the same concept, which motivated many of the later variants.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.13328/j.cnki.jos.006715

@article{2022,
  title = {{深度知识追踪模型综述和性能比较}},
  author = {王宇 and 朱梦霞 and 杨尚辉 and 陆雪松 and 周傲英},
  journal = {软件学报 (Journal of Software)},
  year = {2022},
  doi = {10.13328/j.cnki.jos.006715},
  url = {https://doi.org/10.13328/j.cnki.jos.006715}
}

References

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