Evaluating the quality of student-generated content in learnersourcing: A large language model based approach-Reference-Cited by-同舟云学术

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Evaluating the quality of student-generated content in learnersourcing: A large language model based approach

Published:2024-07-17 Issue: Volume: Page:
ISSN:1360-2357
Container-title:Education and Information Technologies
language:en
Short-container-title:Educ Inf Technol

Author:

Li Kangkang,Qian Chengyang,Yang Xianmin

Funder

National Natural Science Foundation of China

Humanities and Social Science Fund of Ministry of Education of China

Research and Innovation Projects of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10639-024-12851-4.pdf

Reference85 articles.

1. Abdi, S., Khosravi, H., Sadiq, S., & Demartini, G. (2021). Evaluating the Quality of Learning Resources: A Learnersourcing Approach. IEEE Transactions on Learning Technologies, 14(1), 81–92. https://doi.org/10.1109/TLT.2021.3058644

2. Adline, A. L. A., & Mahalakshmi, G. S. (2011). A novel framework for E-learning content evaluation. In 2011 Third International Conference on Advanced Computing (pp. 346–352). https://doi.org/10.1109/ICoAC.2011.6165200.

3. Altamimi, A. B. (2023). Effectiveness of ChatGPT in Essay Autograding. In 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 102–106). https://doi.org/10.1109/iCCECE59400.2023.10238541.

4. Amaral, G., Piscopo, A., Kaffee, L. A., Rodrigues, O., & Simperl, E. (2021). Assessing the quality of sources in Wikidata across languages: A hybrid approach. Journal of Data and Information Quality (JDIQ), 13(4), 1–35. https://doi.org/10.1145/3484828

5. Ameer, I., Bölücü, N., Siddiqui, M. H. F., Can, B., Sidorov, G., & Gelbukh, A. (2023). Multi-label emotion classification in texts using transfer learning. Expert Systems with Applications, 213, 118534. https://doi.org/10.1016/j.eswa.2022.118534

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