{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T05:27:17Z","timestamp":1764912437026,"version":"3.46.0"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uygur Autonomous Region Institute of Metrology and Measurement","award":["XJGXJGZH-2024043"],"award-info":[{"award-number":["XJGXJGZH-2024043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Accurate classification of cognitive levels in instructional dialogues is essential for personalized education and intelligent teaching systems. However, most existing methods predominantly rely on static textual features and a shallow semantic analysis. They often overlook dynamic temporal interactions and struggle with class imbalance. To address these limitations, this study proposes a novel framework for cognitive-level classification. This framework integrates time entropy-enhanced dynamics with a dynamically weighted, heterogeneous ensemble strategy. Specifically, we reconstruct the original Multi-turn Classroom Dialogue (MCD) dataset by introducing time entropy to quantify teacher\u2013student speaking balance and semantic richness features based on Term Frequency-Inverse Document Frequency (TF-IDF), resulting in an enhanced MCD-temporal dataset. We then design a Dynamic Weighted Heterogeneous Ensemble (DWHE), which adjusts weights based on the class distribution. Our framework achieves a state-of-the-art macro-F1 score of 0.6236. This study validates the effectiveness of incorporating temporal dynamics and adaptive ensemble learning for robust cognitive level assessment, offering a more powerful tool for educational AI applications.<\/jats:p>","DOI":"10.3390\/informatics12040134","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:31:17Z","timestamp":1764689477000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MCD-Temporal: Constructing a New Time-Entropy Enhanced Dynamic Weighted Heterogeneous Ensemble for Cognitive Level Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0341-2707","authenticated-orcid":false,"given":"Yuhan","family":"Wu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xinjiang University, Urumqi 830000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8915-4965","authenticated-orcid":false,"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6508-5071","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7006-1245","authenticated-orcid":false,"given":"Wendong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xinjiang University, Urumqi 830000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Geethanjali, K.S., and Umashankar, N. 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