{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:01:22Z","timestamp":1776391282472,"version":"3.51.2"},"reference-count":44,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T00:00:00Z","timestamp":1771718400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T00:00:00Z","timestamp":1771718400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Assisted Learning"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Games are one of the most popular activities that transcend cultures and ages. Game\u2010based 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)\u2010based models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The objective of this research is to construct a deep learning framework for game\u2010based educational assessment and transform model decisions into human\u2010understandable educational insights through explainable AI technology, ultimately achieving precise prediction and intervention support for learners' learning states.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper proposes a Mamba\u2010based explainable AI framework named MambaGBA for game\u2010based education assessment. MambaGBA employs a Mamba State Space Model as backbone and integrates a cognitive science\u2010inspired 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\u2010making logic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and Conclusions<\/jats:title>\n                    <jats:p>Experimental results demonstrate that MambaGBA outperforms the baseline models in predicting student performance, including LightGBM, LSTM (Long Short\u2010Term 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\u2010performance 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcal.70203","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T02:05:41Z","timestamp":1771812341000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explainable\n                    <scp>AI<\/scp>\n                    Framework for Game\u2010Based Assessment: From Model Tuning to Human Insights"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2176-3835","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Medical Imaging Huaihe Hospital of Henan University  Kaifeng China"},{"name":"School of Computer and Information Engineering Henan University  Kaifeng China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7130-0099","authenticated-orcid":false,"given":"Ningweiyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering Henan University  Kaifeng China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2403-4777","authenticated-orcid":false,"given":"Xinhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Huaihe Hospital of Henan University  Kaifeng China"},{"name":"School of Software Henan University  Kaifeng China"}]}],"member":"311","published-online":{"date-parts":[[2026,2,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jarmac.2020.09.003"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj\u2010cs.1869"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.30868"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TALE62452.2024.10834292"},{"key":"e_1_2_11_6_1","unstructured":"Dao T. andA.Gu.2024.Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality In International Conference on Machine Learning.https:\/\/doi.org\/10.5555\/3692070.3692469."},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106071"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.47772\/IJRISS.2025.908000300"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/15485129211028651"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.70008"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2022.3226661"},{"key":"e_1_2_11_12_1","unstructured":"Gu A.2023.\u201cModeling Sequences With Structured State Spaces(PhD Dissertation Stanford University).\u201dhttps:\/\/purl.stanford.edu\/mb976vf9362."},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2021.104170"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1306039"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3463948"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42330\u2010023\u201000302\u20100"},{"key":"e_1_2_11_17_1","unstructured":"Jones K. andK. N.Jones.2024.\u201cEvery Student Deserves a Struggle: Planning for the Implementation and Differentiation of High Cognitive Tasks (Honors College Theses Murray State University).\u201dhttps:\/\/digitalcommons.murraystate.edu\/honorstheses\/194."},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2022.100074"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.32628\/CSEIT251116172"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","unstructured":"Li M.\u2010J. S.\u2010T.Li A. C. M.Yang A. Y.Huang andS. J.Yang.2024.Trustworthy and Explainable AI for Learning Analytics. In Joint Proceedings of Lak 2024 Workshops (Vol. 3667. 1\u201310). 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