@inproceedings{d21db425081340309376c59635666d63,
title = "An Approach to Generating Adaptive Feedback for Online Formative Assessment",
abstract = "In this paper, we propose a novel approach to generating adaptive feedback by identifying the chain of weakest learning objectives to a learner working in a domain. It combines the domain model based expert-driven model with question-answering based data-driven model. The domain model is an AND/OR graph of domain knowledge structure based on the revised Bloom{\textquoteright}s taxonomy, defining the learning objectives of the domain and the corresponding pre-requisite relationships. The adaptive formative assessment process uses an improved Top-Two Thompson sampling algorithm for solving the best arm identification problem in the multi-armed bandit framework. The simulation results show the feasibility and performance of the proposed approach.",
keywords = "Formative assessment, adaptive feedback generation, adaptive learning, bandit algorithms, domain modeling, online learning, simulated learners",
author = "Fuhua Lin and {De Silva}, Supun",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 19th International Conference on Augmented Intelligence and Intelligent Tutoring Systems, ITS 2023 ; Conference date: 02-06-2023 Through 05-06-2023",
year = "2023",
doi = "10.1007/978-3-031-32883-1_8",
language = "English",
isbn = "9783031328824",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "88--99",
editor = "Claude Frasson and Phivos Mylonas and Christos Troussas",
booktitle = "Augmented Intelligence and Intelligent Tutoring Systems - 19th International Conference, ITS 2023, Proceedings",
}