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The design and development of the dragoon intelligent tutoring system for model construction: lessons learned.

, , , , , , , , , , , , and . Interact. Learn. Environ., 25 (3): 361-381 (2017)

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How Should Knowledge Composed of Schemas be Represented in Order to Optimize Student Model Accuracy?, , and . AIED (1), volume 10947 of Lecture Notes in Computer Science, page 127-139. Springer, (2018)Model Elicitation through Direct Questioning., , and . CoRR, (2020)Learning How to Construct Models of Dynamic Systems: An Initial Evaluation of the Dragoon Intelligent Tutoring System., , , and . IEEE Trans. Learn. Technol., 10 (2): 154-167 (2017)Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations., , and . BioNLP@ACL, page 275-284. Association for Computational Linguistics, (2022)Plan Explanations as Model Reconciliation., , , and . HRI, page 258-266. IEEE, (2019)Evaluating Deterioration Prediction, Usability, and Impact of a Clinical Artificial Intelligence System: Real-time Intensive Care Warning INdex System (I-WIN) Using EHR and Bedside Monitor Data., , , , , and . AMIA, AMIA, (2022)The Long Tail: Understanding the Discoverability of API Functionality., , , , , , , , , and . VL/HCC, page 157-161. IEEE Computer Society, (2019)The design and development of the dragoon intelligent tutoring system for model construction: lessons learned., , , , , , , , , and 3 other author(s). Interact. Learn. Environ., 25 (3): 361-381 (2017)Plan Explanations as Model Reconciliation - An Empirical Study., , , and . CoRR, (2018)Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+., , , , and . ICAPS, page 518-526. AAAI Press, (2023)