{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T01:39:58Z","timestamp":1772674798359,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031992605","type":"print"},{"value":"9783031992612","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-99261-2_20","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T04:13:29Z","timestamp":1753244009000},"page":"221-229","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Online Mathematics Test Efficiency Based on Stacked Integrated Models: A Case Study of NAEP Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2208-3508","authenticated-orcid":false,"given":"Chengliang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-1628","authenticated-orcid":false,"given":"Haoming","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5754-5123","authenticated-orcid":false,"given":"Yutong","family":"Lai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9059-0255","authenticated-orcid":false,"given":"Zihan","family":"Xiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0736-7932","authenticated-orcid":false,"given":"Xianlong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Bi, H., et al.: Quality meets diversity: a model-agnostic framework for computerized adaptive testing. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 42\u201351. IEEE, New York (2020). https:\/\/doi.org\/10.1109\/ICDM50108.2020.00013","DOI":"10.1109\/ICDM50108.2020.00013"},{"key":"20_CR2","unstructured":"Chen, Y., Li, X., Liu, J., Ying, Z.: Item response theory\u2014a statistical framework for educational and psychological measurement. arXiv preprint arXiv:2108.08604 (2021)"},{"key":"20_CR3","unstructured":"Deshkar, P.A., et al.: Data pre-processing solution using statistical and data mining techniques. In: Garg, L., et al. (eds.) AI Technologies for Information Systems and Management Science. ISMS 2023. Lecture Notes in Networks and Systems, vol. 1136, pp. 109\u2013120. Springer, Cham (2024)"},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"106071","DOI":"10.1016\/j.engappai.2023.106071","volume":"122","author":"SM Dol","year":"2023","unstructured":"Dol, S.M., Jawandhiya, P.M.: Classification technique and its combination with clustering and association rule mining in educational data mining\u2014a survey. Eng. Appl. Artif. Intell. 122, 106071 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106071","journal-title":"Eng. Appl. Artif. Intell."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Fang, T., Huang, S., Zhou, Y., Zhang, H.: Multi-model stacking ensemble learning for student achievement prediction. In: 2021 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp. 136\u2013140. IEEE, New York (2021)","DOI":"10.1109\/PAAP54281.2021.9720454"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.jbusres.2018.02.012","volume":"94","author":"E Fernandes","year":"2019","unstructured":"Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., Van Erven, G.: Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. J. Bus. Res. 94, 335\u2013343 (2019). https:\/\/doi.org\/10.1016\/j.jbusres.2018.02.012","journal-title":"J. Bus. Res."},{"key":"20_CR7","doi-asserted-by":"publisher","unstructured":"Huang, Y., Zhou, Y., Chen, J., Wu, D.: Applying machine learning and SHAP method to identify key influences on middle-school students\u2019 mathematics literacy performance. J. Intell. 12(10), Article no. 10 (2024). https:\/\/doi.org\/10.3390\/jintelligence12100093","DOI":"10.3390\/jintelligence12100093"},{"key":"20_CR8","unstructured":"Ji, C.S., Yee, D.S.-W., Rahman, T.: Mapping state proficiency standards onto the NAEP scales: results from the 2019 NAEP reading and mathematics assessments. NCES 2021\u2013036. National Center for Education Statistics, Washington, DC (2021)"},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Lan, A.S., Studer, C., Baraniuk, R.G.: Time-varying learning and content analytics via sparse factor analysis. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 452\u2013461. ACM, New York (2014). https:\/\/doi.org\/10.1145\/2623330.2623631","DOI":"10.1145\/2623330.2623631"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"e1699","DOI":"10.7717\/peerj-cs.1699","volume":"9","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Huang, Z., Wang, G.: Student learning performance prediction based on online behavior: an empirical study during the COVID-19 pandemic. PeerJ Comput. Sci. 9, e1699 (2023). https:\/\/doi.org\/10.7717\/peerj-cs.1699","journal-title":"PeerJ Comput. Sci."},{"key":"20_CR11","unstructured":"Lundberg, S.: A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017)"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"21833","DOI":"10.1007\/s10639-024-12654-7","volume":"29","author":"J Dai","year":"2024","unstructured":"Dai, J., Zhang, X., Wang, C.L.: A meta-analysis of learners\u2019 continuance intention toward online education platforms. Educ. Inf. Technol. 29, 21833\u201321868 (2024). https:\/\/doi.org\/10.1007\/s10639-024-12654-7","journal-title":"Educ. Inf. Technol."},{"issue":"1","key":"20_CR13","first-page":"42","volume":"5","author":"J Moneva","year":"2020","unstructured":"Moneva, J., Tribunalo, S.M.: Students\u2019 level of self-confidence and performance tasks. Asia Pac. J. Acad. Res. Soc. Sci. 5(1), 42\u201348 (2020)","journal-title":"Asia Pac. J. Acad. Res. Soc. Sci."},{"key":"20_CR14","unstructured":"National Center for Education Statistics: NAEP Nations Report Card - National Assessment of Educational Progress - NAEP. National Center for Education Statistics, Washington, DC (2019)"},{"key":"20_CR15","unstructured":"Ning, R., Waters, A.E., Studer, C., Baraniuk, R.G.: SPRITE: a response model for multiple choice testing. arXiv preprint arXiv:1501.02844 (2015)"},{"issue":"5","key":"20_CR16","doi-asserted-by":"publisher","first-page":"5447","DOI":"10.1007\/s10639-023-12007-w","volume":"29","author":"H Sahlaoui","year":"2024","unstructured":"Sahlaoui, H., Alaoui, E.A.A., Agoujil, S., Nayyar, A.: An empirical assessment of SMOTE variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance models. Educ. Inf. Technol. 29(5), 5447\u20135483 (2024). https:\/\/doi.org\/10.1007\/s10639-023-12007-w","journal-title":"Educ. Inf. Technol."},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"2400304","DOI":"10.1002\/aisy.202400304","volume":"2024","author":"AM Salih","year":"2024","unstructured":"Salih, A.M., et al.: A perspective on explainable artificial intelligence methods: SHAP and LIME. Adv. Intell. Syst. 2024, 2400304 (2024). https:\/\/doi.org\/10.1002\/aisy.202400304","journal-title":"Adv. Intell. Syst."},{"key":"20_CR18","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s40561-024-00343-4","volume":"11","author":"M Saqr","year":"2024","unstructured":"Saqr, M., L\u00f3pez-Pernas, S.: Why explainable AI may not be enough: predictions and mispredictions in decision making in education. Smart Learn. Environ. 11, 52 (2024). https:\/\/doi.org\/10.1186\/s40561-024-00343-4","journal-title":"Smart Learn. Environ."},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1057\/s41599-024-02717-y","volume":"11","author":"CL Wang","year":"2024","unstructured":"Wang, C.L., Chen, X.J., Yu, T., Liu, Y.D., Jing, Y.H.: Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit. Soc. Sci. Commun. 11, 256 (2024). https:\/\/doi.org\/10.1057\/s41599-024-02717-y","journal-title":"Humanit. Soc. Sci. Commun."},{"issue":"24","key":"20_CR20","doi-asserted-by":"publisher","first-page":"8419","DOI":"10.1080\/10447318.2023.2291609","volume":"40","author":"CL Wang","year":"2023","unstructured":"Wang, C.L., Dai, J., Zhu, K.K., Yu, T., Gu, X.Q.: Understanding the continuance intention of college students toward new e-learning spaces based on an integrated model of the TAM and TTF. Int. J. Hum.-Comput. Interact. 40(24), 8419\u20138432 (2023). https:\/\/doi.org\/10.1080\/10447318.2023.2291609","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"20_CR21","doi-asserted-by":"publisher","first-page":"e13117","DOI":"10.1111\/jcal.13117","volume":"41","author":"CL Wang","year":"2025","unstructured":"Wang, C.L., Chen, X.J., Hu, Z.B., Jin, S., Gu, X.Q.: Deconstructing university learners\u2019 adoption intention towards AIGC technology: a mixed-methods study using ChatGPT as an example. J. Comput. Assist. Learn. 41, e13117 (2025). https:\/\/doi.org\/10.1111\/jcal.13117","journal-title":"J. Comput. Assist. Learn."},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"132435","DOI":"10.1109\/ACCESS.2023.3334915","volume":"11","author":"W Xiao","year":"2023","unstructured":"Xiao, W., Hu, J.: Analyzing effective factors of online learning performance by interpreting machine learning models. IEEE Access 11, 132435\u2013132447 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3334915","journal-title":"IEEE Access"},{"key":"20_CR23","doi-asserted-by":"publisher","unstructured":"Zhou, X., Ding, P.L.K., Li, B.: Improving robustness of random forest under label noise. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 950\u2013958. IEEE, New York (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00106","DOI":"10.1109\/WACV.2019.00106"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"7753","DOI":"10.1007\/s10639-024-13110-2","volume":"30","author":"E \u00d6z","year":"2025","unstructured":"\u00d6z, E., Bulut, O., Cellat, Z.F., Y\u00fcrekli, H.: Stacking: an ensemble learning approach to predict student performance in PISA 2022. Educ. Inf. Technol. 30, 7753\u20137779 (2025). https:\/\/doi.org\/10.1007\/s10639-024-13110-2","journal-title":"Educ. Inf. Technol."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-99261-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T04:13:35Z","timestamp":1753244015000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-99261-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031992605","9783031992612"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-99261-2_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palermo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2025.itd.cnr.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}