Intelligent Question Sequencing via Concept-Graph-Aware Reinforcement Learning for Personalized Assessment

Eoin  Byrne; Fiona  Murphy; Patrick  Keane

doi:10.54691/4dpe7270

Authors

Eoin Byrne
Fiona Murphy
Patrick Keane

DOI:

https://doi.org/10.54691/4dpe7270

Keywords:

Reinforcement Learning, Concept Graphs, Question Sequencing, Personalized Assessment, Adaptive Testing, Graph Neural Networks, Educational Data Mining, Intelligent Tutoring Systems, Knowledge State Estimation.

Abstract

Personalized assessment systems require sophisticated question sequencing strategies that can adapt to individual student knowledge states while efficiently evaluating learning outcomes across complex domain structures. Traditional assessment approaches rely on static question ordering or simple adaptive algorithms that fail to leverage the intricate relationships between learning concepts and cannot optimize question sequences for both assessment efficiency and learning reinforcement. The challenge lies in developing intelligent systems that can dynamically select and sequence questions based on real-time student performance while considering conceptual dependencies and individual learning characteristics. This study proposes a novel framework that integrates concept-graph-aware structures with Reinforcement Learning (RL) techniques to enable intelligent question sequencing for personalized assessment systems. The framework employs graph neural networks to model domain knowledge relationships while utilizing deep RL agents to learn optimal question selection policies that maximize assessment accuracy and educational value. The concept-graph representation captures prerequisite dependencies, difficulty progressions, and semantic relationships between assessment items, enabling more informed sequencing decisions that align with pedagogical principles and individual learning pathways. Experimental evaluation using comprehensive educational datasets demonstrates that the proposed framework achieves 39% improvement in assessment efficiency compared to traditional adaptive testing methods. The concept-graph-aware approach results in 45% better knowledge state estimation accuracy and 33% reduction in assessment duration while maintaining equivalent measurement precision. The framework successfully balances assessment objectives with learning reinforcement, resulting in 28% improvement in student engagement and 31% better learning outcome prediction compared to conventional question sequencing approaches.

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