Abstract
Periodic testing (PT) is part and parcel of instructional process, which targets at measuring student proficiency level on specific stage. In general, most previous PTs follow an inflexible offline-policy method, which can hardly adjust testing procedure using the online feedback instantly. In this paper, we develop a dynamic and executed online periodic testing framework called O\(^3\)GPT, which selects the most suitable questions step by step, depending on student’s previous timestep’s real-time feedback. To begin with, we employ a stacked GRU to update student’s state representation instantly, which could well capture the long-term dynamic nature from their past learning trajectories, leading to the testing agent perform effective periodic testing. Subsequently, in Stage2, O\(^3\)GPT incorporates a flexible testing-specific reward function into the soft actor-critic algorithm (SAC) to guarantee the rationality of all selected questions. Finally, to set up the online feedback, we test O\(^3\)GPT on an on-line simulated environment which can model qualitative development of knowledge proficiency. The results of our experiment conducted on two well-established student response datasets indicate that O\(^3\)GPT outperforms state-of-the-art baselines in PT task.
This work is supported by the Natural Science Foundation of Tianjin City (Grant No. 19JCYBJC15300), Tianjin Science and Technology planning project China (Grant No. 21ZYQCSY00050), Tianjin Higher Education Institute Undergraduate Teaching Quality and Teaching Reform Research Project (Grant No. B201005706), National Natural Science Foundation of China (Grant No. 61807024), and the Science and Technology Program of Tianjin (Grant No. 22YDTPJC00940).
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Ren, Y., Shang, Y., Liang, K., Zhang, X., Zhang, Y. (2023). O\(^3\)GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_11
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