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Cited by 124Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in EducationOnline Learning and Analytics

The DOI matches its Crossref record; 123 of the 132 references it cites were found in a registry or an index.

Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

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Guo, Lu, Wang, Dong, Gu, Fei, Li, Yazheng, Wang, Yezhu, Zhou, Rongting·2021·Asia Pacific Education Review
DOI 10.1007/s12564-021-09697-7PMC PMC8095475
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  1. Conati, C., Gertner, A., & Vanlehn, K. (2002a). Using Bayesian networks to manage uncertainty in student modeling. User Modeling and User-Adapted Interaction, 12, 371–417 · DOI · Paper page
  2. Desmarais, M. C., & d Baker, R. S. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1), 9–38 · DOI
  3. Graesser, A. C., Conley, M. W., & Olney, A. M. (2011). Intelligent tutoring systems. In S. Graham & K. Harris (Eds.), APA educational psychology handbook: Vol 3. Applications to learning and teaching.American Psychological Association.
  4. Graesser, A.C., Moreno, K., Marineau, J., Adcock, A., Olney, A., Person, N., & The Tutoring Research Group (2003). AutoTutor improves deep learning of computer literacy: Is it the dialogue or the talking head? In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Proceedings of artificial intelligence in education (pp. 47–54). Amsterdam: IOS Press.
  5. Hu, Z., & Zhou, T. (2017). New media industries from frontiers artificial intelligence and virtual reality. Social Sciences Academic Press (China), 1, 2–3
  6. Li, J., & Chen, C. (2016). CiteSpace: Text mining and visualization in scientific literature. Capital University of Economics and Business Press.
  7. Lighthill, J. (1973). Artificial Intelligence: A general survey. Artificial Intelligence: a paper symposium, Science Research Council.
  8. Liu, K., & Jing, Hu. (2018). The theory framework of AIED: The symmetric hypothesis between learner and educational resources—An interview with ITS expert professor Xiangen Hu[J]. Open Education Research, 24(06), 4–11
  9. Liu, Y. (2003). Modern educational technology and intelligent computer-aided teaching. Journal of Natural Sciences of Harbin Normal University, 2003(5), 59–61
  10. Xu, G., Zeng, W., & Huang, C. (2009). Research on intelligent teaching system. Research on Computer Applications, 2009(11), 4019–4022

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Metadata

Authors
Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., & Zhou, R.
Affiliations
  • University of Science and Technology of China (CN)
  • Anhui University (CN)
Corresponding author
  • Wang, Dong
  • Zhou, Rongting
ORCID
Venue
Asia Pacific Education Review
Publisher
Springer Science and Business Media LLC
Published
2021-05-04
Volume
22
Issue
3
Pages
441-461
ISSN
  • 1598-1037 (ISSN-L)
  • 1876-407X
DOI
10.1007/s12564-021-09697-7
PMC
PMC8095475
OpenAlex
W3157169558
Cited by
124
References
132
Citations by year
2026: 28 · 2025: 32 · 2024: 40 · 2023: 11 · 2022: 11 · 2021: 2
FWCI
5.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Asia Pacific Education Review (publishedVersion)
  • PubMed Central (publishedVersion)
Funders
Anhui Provincial Quality Constructing Teaching Research Project (No: 2018jwxm0899)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
intelligent tutoring systems, research trends, research evolution, multidisciplinary research, interactive learning environment, student modeling, teaching and learning strategies, machine learning in education, Coh-Metrix, STEM education

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