Early warning mechanism of interactive learning process based on temporal memory enhancement model

Xia, Xiaona; Qi, Wanxue

doi:10.1007/s10639-022-11206-1

Cited by 13 publications

(3 citation statements)

References 24 publications

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“…The other significant bottlenecks are the availability of knowledge banks and question-answer datasets to build these models (Okonkwo and Ade-Ibijola 2021). Knowledge Graphs in Education: State-of-the-art methods like knowledge graphs have been used in education to represent unstructured knowledge (Chen et al 2018;Mao 2021;Fariani, Junus, and Santoso 2023;Xia and Qi 2023) and construct knowledge graph question-answering systems (KGQA) (Agrawal, Bertsekas, and Liu 2023;Chen, Wu, and Zaki 2023;Perez-Beltrachini et al 2023). New programming questions based on knowledge graphs were generated by Chung et al (Chung, Hsiao, and Lin 2023).…”

Section: Related Workmentioning

confidence: 99%

CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs

Agrawal,

Pal,

Deng

et al. 2024

AAAI

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Building a skilled cybersecurity workforce is paramount to building a safer digital world. However, the diverse skill set, constantly emerging vulnerabilities, and deployment of new cyber threats make learning cybersecurity challenging. Traditional education methods struggle to cope with cybersecurity's rapidly evolving landscape and keep students engaged and motivated. Different studies on students' behaviors show that an interactive mode of education by engaging through a question-answering system or dialoguing is one of the most effective learning methodologies. There is a strong need to create advanced AI-enabled education tools to promote interactive learning in cybersecurity. Unfortunately, there are no publicly available standard question-answer datasets to build such systems for students and novice learners to learn cybersecurity concepts, tools, and techniques. The education course material and online question banks are unstructured and need to be validated and updated by domain experts, which is tedious when done manually. In this paper, we propose CyberGen, a novel unification of large language models (LLMs) and knowledge graphs (KG) to generate the questions and answers for cybersecurity automatically. Augmenting the structured knowledge from knowledge graphs in prompts improves factual reasoning and reduces hallucinations in LLMs. We used the knowledge triples from cybersecurity knowledge graphs (AISecKG) to design prompts for ChatGPT and generate questions and answers using different prompting techniques. Our question-answer dataset, CyberQ, contains around 4k pairs of questions and answers. The domain expert manually evaluated the random samples for consistency and correctness. We train the generative model using the CyberQ dataset for question answering task.

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Section: Related Workmentioning

confidence: 99%

CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs

Agrawal,

Pal,

Deng

et al. 2024

AAAI

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show abstract

“…Zhang et al [162] built a large-scale knowledge graph for mental and physical disorders detection through data de-identification, initially encrypting personal information with encryption isolation technology, then using content replacement for data de-identification to protect data privacy and security. Xia et al [163] created a knowledge graph by de-identifying MOOC static registration records and log information to safeguard user privacy and security. By analyzing the knowledge graph, they implemented interest dissemination on course videos to predict user preferences.…”

Section: Data Privacy and Security Issuesmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

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In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

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“…In order to study the feature clusters of learning behaviors, it is necessary to adopt appropriate methods to achieve effective clustering (Winne, 2020). Since the second aspect needs to mine learning behaviors from massive data, unsupervised learning method is needed (Lavanya et al, 2021; Xia & Qi, 2023), Data analysis is to explore the possible rules through fully learning the unlabeled samples. Generally, the samples are divided into several relatively independent subsets by feature clustering.…”

Section: Related Workmentioning

confidence: 99%

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

Wang

Xia

2023

Sage Open

Self Cite

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The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the descriptive model and learning algorithm suitable for feature clustering are designed, and sufficient experiments obtain the optimal performance indexes. The data analysis results are reliable. On this basis, the hierarchical learning behaviors based on feature clusters are visualized, the rules of different learning behaviors are summarized, then we propose the practical scheme of interactive cooperation. The hierarchical learning behaviors can be realized by feature clusters, which can effectively improve the modes of interactive cooperation, and help to improve the learning effectiveness.

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Early warning mechanism of interactive learning process based on temporal memory enhancement model

Cited by 13 publications

References 24 publications

CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs

CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs

From data to insights: the application and challenges of knowledge graphs in intelligent audit

The Study of Hierarchical Learning Behaviors and Interactive Cooperation Based on Feature Clusters

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