HBC-Glicko: A Bloom-Constrained Adaptive Assessment Architecture with Uncertainty-Aware Progression

Abstract: Adaptive assessment systems typically model learner ability as a single continuous latent trait and select items accordingly. Although effective for efficient measurement, such models rarely encode hierarchical cognitive progression as a formal constraint within the learner model itself. In educational domains where higher-order performance depends on sufficiently stable prerequisite knowledge, this omission may permit pedagogically incoherent trajectories, including premature advancement or misleading readiness judgments produced by compensatory aggregation across cognitively distinct levels, where strength at one level can mask weakness at another. This study pursues three objectives: 1) to formalize an adaptive assessment architecture in which Bloom’s revised taxonomy operates as a non-compensatory structural constraint on learner progression, meaning that readiness must be established at each prerequisite level independently, without any area of strength substituting for insufficient evidence elsewhere; 2) to derive the principal theoretical properties that follow from this design; and 3) to situate the proposed architecture relative to existing rating-based learner models. To address these objectives, the study proposes Hierarchical Bloom-Constrained Glicko (HBC-Glicko), a theory-driven measurement architecture for formative adaptive assessment. Instead of representing learner state as a single scalar estimate, HBC-Glicko models it as a band-specific vector with band-level uncertainty. Progression is regulated through anchor-based readiness thresholds and confidence-bound decision rules, such that advancement depends on both estimated performance and evidential stability at prerequisite levels. The study formalizes the learner model, within-band routing logic, threshold construction, promotion and prerequisite reinforcement rules, and derives the principal architectural properties. The contribution is conceptual and architectural rather than empirical, establishing a formally specified foundation for subsequent simulation and empirical investigation.

Wissal EL Fougour and Mohamed Erradi. “HBC-Glicko: A Bloom-Constrained Adaptive Assessment Architecture with Uncertainty-Aware Progression”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170550

@article{Fougour2026,
title = {HBC-Glicko: A Bloom-Constrained Adaptive Assessment Architecture with Uncertainty-Aware Progression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170550},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170550},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Wissal EL Fougour and Mohamed Erradi}
}