From RBMs to BN2A Models: Parameter Transformation for Interpretable Educational Diagnostics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10509003" target="_blank" >RIV/00216208:11320/25:10509003 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-032-05134-9_8" target="_blank" >https://doi.org/10.1007/978-3-032-05134-9_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-05134-9_8" target="_blank" >10.1007/978-3-032-05134-9_8</a>

Result language

angličtina

Original language name

From RBMs to BN2A Models: Parameter Transformation for Interpretable Educational Diagnostics

Original language description

Restricted Boltzmann Machines (RBMs) are bipartite graphical models with binary latent and observed variables that have shown promise for representation learning. However, their lack of interpretable parameters limits their utility in domains requiring explainability, like educational assessment. Despite extensive RBM research, non-negativity constraints on weights-essential for monotonicity in educational contexts-remain largely unexplored. To address this, we propose a method to translate RBMs into a specialized class of bipartite Bayesian networks, which we term BN2A networks, characterized by strict 2-layer separation (hidden and observed variables), Noisy-AND conditional probability tables, and directly interpretable parameters for educational models. Our work establishes a mathematical transformation from RBM weights to BN2A&apos;s interpretable parameters (leak and penalty probabilities), theoretical analysis showing BN2A&apos;s constrained connectivity is a subset of RBM architectures, and empirical evidence that the transformation preserves model fidelity under realistic conditions. By bridging these paradigms, our method leverages RBM&apos;s representational power while achieving BN2A&apos;s interpretability, opening new possibilities for adaptive learning systems and diagnostic tools.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10102 - Applied mathematics

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-032-05134-9

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

14

Pages from-to

104-117

Publisher name

Springer Internat. Publ.

Place of publication

Cham

Event location

Hagen, Germany

Event date

Sep 23, 2025

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

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