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Knowledge Tracing: A Survey

Paper · 2023 · Ghodai Abdelrahman, Qing Wang and Bernardo Nunes

Summary

Knowledge Tracing (KT) estimates how well a learner has mastered underlying skills from their history of answering questions, and uses this estimate to predict future answers. Reliable KT supports intelligent tutoring systems, curriculum learning and recommendation of study materials. The authors argue it also applies to machine teaching, where the learner is itself a model. The survey is built around five research questions: the history of classical methods, the deep learning techniques now in use, benchmark datasets, application areas, and open challenges. It follows the field's progression in three stages:

  • Bayesian models that needed strong simplifications.
  • Factor-analysis approaches based on logistic regression.
  • Neural architectures.

Methods are compared by model design, knowledge-state representation, assumed links between questions and skills, and handling of forgetting. Results reported across the literature are consolidated into one comparison table, which shows deep models leading on most benchmarks. The paper serves as a structured reference for building and fairly evaluating learner models.

Key points

  • KT originates with a 1986 technical report by Anderson et al., which subsequently appeared as a 1990 article in the journal Artificial Intelligence.
  • Deep Knowledge Tracing (DKT, Piech et al.) was the first deep model. It runs an RNN over interaction sequences and outperformed traditional KT models on several benchmarks.
  • Later deep KT work extends DKT along five lines:
    • key-value memory networks;
    • Transformer-style attention;
    • Graph Neural Networks over question–skill relations;
    • question text (e.g., EKT by Liu et al.);
    • forgetting features motivated by learning curve theory.
  • Core modelling difficulties include questions requiring several skills, prerequisite dependencies between skills, and decay of knowledge over time.
  • Eight future directions (RD1–RD8) are proposed:
    • richer multimodal datasets instead of ID-only encodings;
    • self-supervised pre-training for cold-start settings;
    • interactive KT with reinforcement-learning question selection;
    • explainable AI for KT predictions.

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

 Institutions

🎓Australian National UniversityCanberra · Australia27 works · 7,072 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksInformation Systems · 1,888 worksComputer Science Applications · 184 works

Cite

Cite as Ghodai Abdelrahman, Qing Wang and Bernardo Nunes. 2023. Knowledge Tracing: A Survey. ACM Computing Surveys 55(11).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1145/3569576 · arXiv 2201.06953

@inproceedings{abdelrahman2023knowledge,
  title = {{Knowledge Tracing: A Survey}},
  author = {Ghodai Abdelrahman, Qing Wang and Bernardo Nunes},
  booktitle = {ACM Computing Surveys 55(11)},
  year = {2023},
  doi = {10.1145/3569576},
  eprint = {2201.06953},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3569576}
}

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