Computerized Adaptive Testing: A Unified Approach Under Markov Decision Process

Gilavert, Patricia; Freire, Valdinei

doi:10.1007/978-3-031-10522-7_40

Cited by 2 publications

(2 citation statements)

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“…In CAT, the goal state typically represents the completion of the test or the attainment of a predetermined level of proficiency estimation precision. The approach by Gilavert and Freire [66] uses Linear Programming to find the optimal testing policy 𝜋 * , treating the problem like a flow network where each state must have balanced inflow and outflow, except for the start and end points. It denotes variables 𝑥 𝑠,𝑎 as the expected accumulated occurrence frequency for every pair (state 𝑠 ∈ 𝑆, action 𝑞 ∈ Q), and equalizes 𝑖𝑛(𝑠) and 𝑜𝑢𝑡 (𝑠) flow model for every state 𝑠.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

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Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Reinforcement Learning Methodsmentioning

confidence: 99%

“…Responses to a constrained set of questions may fall short of capturing the full breadth of these dimensions, potentially leading to an incomplete portrayal of an examinee's proficiency. To this end, many works [33,66,120] model CAT as a POMDP. Compared with MDP, the POMDP model has two additional elements.…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%