The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Evaluate learner level assessment in intelligent e-learning systems using probabilistic network model
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Claims other papers make about this one
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In the citing paper
“These models predict the student's mastery by analyzing the expected number of correct responses, thereby enabling the system to make some inferences about the ability of a student in general.”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [103]
Citations
Cites
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
Chrysafiadi, K., & Virvou, M. K. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.05.020 · Cited by 58
Knowledge-based Intelligent Tutoring System for Teaching Mongo Database
Hilles, M. M., & Abu-Naser, S. S. (2017) · PhilPapers (PhilPapers Foundation) · Cited by 50
Evaluating a Bayesian Student Model of Decimal Misconceptions.
George Goguadze, A., Александрович, С. С., Isotani, S., & McLaren, B. M. (2011) · Educational Data Mining · Cited by 32
Evaluating an Intelligent Tutoring System for Personalized Math Teaching
Zhang, B., & Jia, J. (2017) · International Symposium on Educational Technology · doi:10.1109/iset.2017.37 · Cited by 32
Computer-based testing - the ideal tool to assess on the different levels of Bloom's taxonomy
Bruyn, E. D., Mostert, E.-M., & Schoor, A. N. V. (2011) · International Conference on Interactive Collaborative Learning · doi:10.1109/icl.2011.6059623 · Cited by 24
Dynamic Adaptive Mechanism in Learning Management System Based on Learning Styles
Alkhuraiji, S., Cheetham, B. M. G., & Bamasak, O. (2011) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2011.69 · Cited by 23
Academic Performance Predictors
Lei, C., & Li, K. F. (2015) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2015.114 · Cited by 21
Research on the Intelligent Information System for the Multimedia Teaching Equipment Management
Bao-xun, H., & Zhuang, K.-J. (2016) · doi:10.1109/isai.2016.0036 · Cited by 11
Contributions of Student’s Assessment of Reflections on the Prediction of Learning Performance
Nakayama, M., Mutsuura, K., & Yamamoto, H. (2018) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet.2018.8424785 · Cited by 8
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Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328
Learner models: design, components, structure, and modelling
Böck, F., Ochs, M., Henrich, A., Landes, D., Leidner, J. L., & Sedelmaier, Y. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09434-4 · Cited by 18
Bayesian Network Model Based Classifiers Are Used in an Intelligent E-learning System
Kaliwal, R. B., & Deshpande, S. L. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-56700-1_19
Self-citation
Metadata
- Authors
- Kaliwal, R. B., & Deshpande, S. L.
- Affiliations
- Visvesvaraya Technological University (IN)
- Venue
- Artificial Intelligence and Machine Learning for EDGE Computing
- Publisher
- Elsevier
- Published
- 2022
- Pages
- 255-265
- OpenAlex
- W4226396296
- Cited by
- 3
- References
- 13
- Citations by year
- 2025: 1 · 2024: 2
- FWCI
- 1.39
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Elsevier eBooks (publishedVersion)
- 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, student assessment, Bayesian networks, probabilistic networks, learning outcome evaluation, expert systems, teaching model, knowledge base model
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.
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