| Sumario: | Background: Games are one of the most popular activities that transcend cultures and ages. Game‐based assessment (GBA) integrates game elements into the assessment of abilities, skills, or knowledge and has already been applied in education. However, the complex behavioural sequence data of GBA poses a challenge to the explainability of artificial intelligence (AI)‐based models. Objectives: The objective of this research is to construct a deep learning framework for game‐based educational assessment and transform model decisions into human‐understandable educational insights through explainable AI technology, ultimately achieving precise prediction and intervention support for learners' learning states. Methods: This paper proposes a Mamba‐based explainable AI framework named MambaGBA for game‐based education assessment. MambaGBA employs a Mamba State Space Model as backbone and integrates a cognitive science‐inspired Episodic Memory Module to capture key behavioural patterns, aiming to predict learners' performance in GBA. Furthermore, an eXplainable Artificial Intelligence (XAI) analysis reveals MambaGBA model's decision‐making logic. Results and Conclusions: Experimental results demonstrate that MambaGBA outperforms the baseline models in predicting student performance, including LightGBM, LSTM (Long Short‐Term Memory), and transformer. XAI helps to distill the complex knowledge learned by models into insights and tools that human experts can understand. This study also develops a lightweight detector for identifying learners' struggling states based on MambaGBAs' interpretable insights. This study not only provides a high‐performance and highly interpretable GBA framework but also offers a new theoretical perspective and practical evidence on how to apply XAI technology more meaningfully in education. Summary: What is currently known about this topic? ○GBA empresses non‐invasive assessment into games without interfering with learners' participation.○Sequence modelling in GBA data analysis is very important.○Deep learning models achieve high prediction accuracy, but lack transparency.What does this paper add? ○This paper presents a MambaGBA framework which integrates the Mamba model and an episodic memory module.○MambaGBA framework simulates how human experts combine long‐term observation with real‐time performance.○A XAI analysis connects model tuning with human insights.Implications for practice and/or policy ○MambaGBA can help educators identify students who are making slow progress and provide personalised intervention.○Educational technology developers can adopt this framework to build GBA tools.○Education policymakers can request the use of explainable artificial intelligence tools.
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