The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Evaluation module based on Bayesian networks to Intelligent Tutoring Systems
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Specific use of this term is relatively recent, with papers such as Zhao, Tang, Darlington, Austin, and Culley (2008) on evaluating information in engineering documents and Ramírez-Noriega, Juárez-Ramírez, and Martínez-Ramírez (2017) using a Bayesian network as the design for an intelligent tutoring system.”
From Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda (2019) · cites this paper as Ramírez-Noriega et al., 2017
Citations
Cites
Artificial intelligence: a modern approach
(1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 21,974
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,583
AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction
Carbonell, J. (1970) · IEEE Transactions on Man Machine Systems · doi:10.1109/tmms.1970.299942 · Cited by 955
Proceedings of the 7th International Conference on Educational Data Mining.
Stamper, J. C., Pardos, Z. A., Mavrikis, M., & McLaren, B. M. (2014) · Educational Data Mining · Cited by 667
Brown, S. A. (2005) · Research Repository (University of Gloucestershire) · Cited by 578
Using Assessment to Improve the Quality of Education
Kellaghan, T., & Greaney, V. (2001) · Medical Entomology and Zoology · Cited by 174
Bayesian networks for student model engineering
Millán, E., Loboda, T. D., & Pérez-de-la-Cruz, J.-L. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.07.010 · Cited by 134
Procesos cognitivos y aprendizaje significativo
Navarro, M. R. (2008) · MINISTERIO DE EDUCACION · Cited by 79
Using Bayesian networks to improve knowledge assessment
Millán, E., Descalço, L., Castillo, G., Oliveira, P., & Diogo, S. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.06.012 · Cited by 60
Bayesian Networks For Evidence-Based Decision-Making in Software Engineering
Tosun, A., & Bener, A. (2014) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2321179 · Cited by 60
Research works printed but not linked: 2
- Cataldi, 2010, Modelado del Estudiante en Sistemas Tutores Inteligentes, Revista Iberoamericana de Tecnologia en Educación y Educación enTecnología, 5, 29
- Millán, 2013, Using Bayesian networks to improve knowledge assessment, Computers & Education, 60, 436, 10.1016/j.compedu.2012.06.012 · DOI
Cited by
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.01.021 · Cited by 2,628
Guerrero-Roldán, A.-E., Rodríguez-González, M. E., Bañeres, D., Elasri-Ejjaberi, A., & Cortadas, P. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00271-0 · Cited by 50
Metadata
- Authors
- Ramírez-Noriega, A., Juárez-Ramírez, R., & Martínez-Ramírez, Y.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 37
- Issue
- 1
- Pages
- 1488-1498
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2404383624
- Cited by
- 61
- References
- 36
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 8 · 2017: 3
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- International Journal of Information Management (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- intelligent tutoring systems, Bayesian networks, knowledge assessment, knowledge acquisition, adaptive testing, adaptive question selection, probabilistic inference, conceptual networks, knowledge diagnosis, personalized learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
