PyNeuraLogic framework for lifted relational neural-symbolic learning¶
Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
PyNeuraLogic is an open-source framework developed by Hooshyar et al. for building differentiable relational neural-symbolic models from logical facts and weighted rule templates, including learner-modelling applications.
Components¶
Claims¶
- Responsible-DKT outperforms a neural-symbolic baseline without knowledge injection and a fully data-driven PyTorch DKT in predictive accuracy across training ratios and sequence lengths [+W]
- Symbolic knowledge injection improves temporal reliability of predictions, yielding lower early- and mid-sequence errors and the lowest prediction inconsistency rates across sequence lengths [+W]
- The neural-symbolic architecture enables empirical evaluation of embedded pedagogical rules, showing that repeated-incorrect (non-mastery) patterns play a stronger role in prediction updates [+W]
Related Products and Programmes¶
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Key Sources¶
- Hooshyar, D.; Šír, G.; Yang, Y.; Kärkkäinen, T.; Hämäläinen, R.; Krivich, E.; Cukurova, M.; Gašević, D.; Azevedo, R. (2026). Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling. arXiv preprint. https://arxiv.org/abs/2604.08263. doi:10.48550/arxiv.2604.08263
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
pyneuralogic-framework-for-lifted-relational-neural-symbolic-learning |
title |
PyNeuraLogic framework for lifted relational neural-symbolic learning |
description |
PyNeuraLogic is an open-source framework developed by Hooshyar et al. |
status |
review |
product_kind |
software |
generated |
by process:settle-candidates on 2026-10-11 |
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unverified — no one has confirmed this page's content |
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