Deep Reinforcement Learning for Adaptive Learning Systems

Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua‐Hua

doi:10.3102/10769986221129847

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…Recently, with the advancements in deep learning, an increasing number of studies are leveraging Deep Reinforcement Learning (DRL) to tackle the MDP problems in CAT. Li et al [97] utilize the Deep Q-Network (DQN) to represent the action-value function 𝑄 𝑤 (𝑠, 𝑞), representing the value of choosing question 𝑞 in state 𝑠, and 𝑤 denotes its parameter of the network's fully connected layer. The most suitable question is selected according to the policy:…”

Section: Reinforcement Learning Methodsmentioning

confidence: 99%

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%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…Li et al [35] proposed an adaptive learning system using a continuous latent trait model and a deep Q-learning algorithm. The system aims to improve learners' abilities to reach target levels.…”

Section: Rl Techniques For the Teacher-student Frameworkmentioning

confidence: 99%

Reinforcement Learning in Education: A Literature Review

Fahad Mon,

Wasfi,

Hayajneh

et al. 2023

Informatics

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The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.

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“…This method of modal decomposition is characterized by the instantaneous identification of features but requires a large amount of data to produce effective results [4][5][6]. With the development of artificial intelligence, deep learning methods have become the main research direction of current fault diagnosis due to the advantages of efficient and powerful feature extraction, and methods of deep learning mainly include support vector machines and neural network algorithms [7][8][9][10]. The support vector machine still remains effective when the data dimension is larger than the number of samples, and it excels at solving nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Prediction Method for Electric Multiple Unit Gearbox Based on Gated Recurrent Unit–Hidden Markov Model

Liu,

Zhang,

Wang

et al. 2024

Applied Sciences

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Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid–flexible coupling dynamics. Vibration acceleration data from the simulation were utilized to create a six-dimensional hybrid feature domain representing the degradation process. By leveraging the capabilities of the Hidden Markov Model (HMM) for handling hidden transitive probabilities in temporal data and Gated Recurrent Unit (GRU) for addressing long-distance and high-dependence temporal data, a GRU-HMM fault prediction model was developed. This model was validated using monitoring data and the six-dimensional hybrid feature domain from the CRH5 gearbox and compared against actual maintenance records. The findings indicated that the GRU-HMM fault prediction model can effectively recognize the degradation patterns of multiple components, offering higher accuracy in fault prediction compared to traditional models. These research outcomes are expected to optimize EMU maintenance schedules based on usage conditions, enhance EMU utilization rates, and reduce operational and maintenance costs, thereby providing valuable theoretical support.

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Deep Reinforcement Learning for Adaptive Learning Systems

Cited by 11 publications

References 34 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Reinforcement Learning in Education: A Literature Review

Research on Fault Prediction Method for Electric Multiple Unit Gearbox Based on Gated Recurrent Unit–Hidden Markov Model

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