The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Using Bayesian Networks to Manage Uncertainty in Student Modeling
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As early as the 1970s, AI-based systems were first implemented in support of personalized and adaptive instruction, giving rise to the field of Intelligent Tutoring Systems.
“As early as the 1970s, AI-based systems were first implemented in support of personalized and adaptive instruction, giving rise to the field of ITS [17][18][19].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [17]
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Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,188
(1988) · Cited by 16,582
Encoding specificity and retrieval processes in episodic memory.
Tulving, E., & Thomson, D. M. (1973) · Psychological Review · doi:10.1037/h0020071 · Cited by 4,540
Handbook of Research on Educational Communications and Technology
(2013) · Routledge eBooks · doi:10.4324/9781410609519 · Cited by 2,744
Advances in Instructional Psychology
(2019) · Routledge eBooks · doi:10.4324/9781315864341 · Cited by 2,538
Knowledge tracing: Modeling the acquisition of procedural knowledge
Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324
Cognitive Tutors: Lessons Learned
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0402_2 · Cited by 1,982
Categorization of action slips
Norman, D. A. (1981) · Psychological Review · doi:10.1037/0033-295x.88.1.1 · Cited by 1,736
Expert systems: principles and programming
(1990) · Choice Reviews Online · doi:10.5860/choice.27-4542 · Cited by 1,306
The transfer of cognitive skill
(1990) · Choice Reviews Online · doi:10.5860/choice.27-2989 · Cited by 1,250
Intelligent Tutoring Goes To School in the Big City
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (2018) · doi:10.1184/r1/6470153 · Cited by 1,105
The psychology of reading and language comprehension
Just, M. A., & Carpenter, P. A. (1986) · Medical Entomology and Zoology · Cited by 1,056
A model for reasoning about persistence and causation
Dean, T., & Kanazawa, K. (1989) · Computational Intelligence · doi:10.1111/j.1467-8640.1989.tb00324.x · Cited by 999
(2013) · Routledge eBooks · doi:10.4324/9780203052594 · Cited by 761
Learning from worked-out examples: A study on individual differences
RENKL, A. (1997) · Cognitive Science · doi:10.1016/s0364-0213(99)80017-2 · Cited by 679
The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software UsersPreprint
Horvitz, E., Breese, J. S., Heckerman, D. E., Hovel, D., & Rommelse, K. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7385 · Cited by 655
Foundations of Intelligent Tutoring Systems
Polson, M. C., & Richardson, J. M. (2013) · Psychology Press eBooks · doi:10.4324/9780203761557 · Cited by 615
Cognitively Diagnostic Assessment
Nichols, P. D., Chipman, S. F., & Brennan, R. L. (2012) · Routledge eBooks · doi:10.4324/9780203052969 · Cited by 491
A Bayesian model of plan recognition
Charniak, E., & Goldman, R. P. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90060-o · Cited by 469
Diagnostic Monitoring of Skill and Knowledge Acquisition
(2013) · Routledge eBooks · doi:10.4324/9780203056899 · Cited by 468
Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995) · Cognition and Instruction · doi:10.1207/s1532690xci1302_3 · Cited by 422
Intelligent Tutoring Systems: Past, Present, and Future.
Shute, V. J., & Psotka, J. (1994) · doi:10.21236/ada280011 · Cited by 335
A model of the self-explanation effect.
VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0201_1 · Cited by 305
Self-citationPredictive Statistical Models for User Modeling
Ingrid Zukerman, A., & Albrecht, D. W. (2001) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1011175525451 · Cited by 283
A Large‐Scale Evaluation of an Intelligent Discovery World: Smithtown
Shute, V. J., & Glaser, R. A. (1990) · Interactive Learning Environments · doi:10.1080/1049482900010104 · Cited by 279
Toward Computer-Based Support of Meta-Cognitive Skills: a . . .
Conati, C., & VanLehn, K. A. (2000) · Cited by 256
Self-citationSome Practical Issues in Constructing Belief Networks.
Henrion, M. (1987) · Uncertainty in Artificial Intelligence · Cited by 250
Techniques for Plan Recognition
Carberry, S. (2001) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1011118925938 · Cited by 249
Numerical uncertainty management in user and student modeling: An overview of systems and issues
Jameson, A. (1996) · User Modeling and User-Adapted Interaction · doi:10.1007/bf01126111 · Cited by 228
Journal of Artificial Intelligence in Education
(1991) · Intelligent Tutoring Media · doi:10.1080/14626269109408288 · Cited by 223
Andes: A Coached Problem Solving Environment for Physics
Gertner, A. S., & VanLehn, K. A. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_17 · Cited by 218
Self-citationBayesian Models for Keyhole Plan Recognition in an Adventure Game
Albrecht, D. W., Zukerman, I., & Nicholson, A. E. (1998) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1008238218679 · Cited by 203
Gray, W. D., & Orasanu, J. M. (1987) · Elsevier eBooks · doi:10.1016/b978-0-12-188950-0.50013-8 · Cited by 195
The role of probability-based inference in an intelligent tutoring system
Mislevy, R. J., & Gitomer, D. H. (1996) · User Modeling and User-Adapted Interaction · doi:10.1007/bf01126112 · Cited by 168
Predicting Users’ Requests on the WWW
Zukerman, I., Albrecht, D. W., & Nicholson, A. E. (1999) · Courses and lectures · doi:10.1007/978-3-7091-2490-1_27 · Cited by 165
Self-explaining : The dual processes of generating inference and repairing mental models
Michelene, T. H. (2000) · Medical Entomology and Zoology · Cited by 158
Display of Information for Time-Critical Decision MakingPreprint
Horvitz, E., & Barry, M. R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4959 · Cited by 156
Student assessment using Bayesian nets
Martin, J. J., & VanLehn, K. A. (1995) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1995.1025 · Cited by 152
Self-citationProcedural help in Andes: generating hints using a Bayesian network student model
Gertner, A. S., Conati, C., & VanLehn, K. A. (1998) · Cited by 139
Self-citationBundy, A., Giunchiglia, F., Sebastiani, R., & Walsh, T. (1996) · Cited by 127
Modeling Student Knowledge: Cognitive Tutors in High School and College
Corbett, A. T., McLaughlin, M., & Scarpinatto, K. C. (2000) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1026505626690 · Cited by 100
PROBABILITY‐BASED INFERENCE IN COGNITIVE DIAGNOSIS
Mislevy, R. J. (1994) · ETS Research Report Series · doi:10.1002/j.2333-8504.1994.tb01576.x · Cited by 84
SMART: Student modeling approach for responsive tutoring
Shute, V. (1995) · User modeling and user-adapted interaction · doi:10.1007/bf01101800 · Cited by 81
Incorporating default inferences into plan recognition
Carberry, S. (1990) · National Conference on Artificial Intelligence · Cited by 80
Corbett, A. T., & Bhatnagar, A. (1997) · Springer eBooks · doi:10.1007/978-3-7091-2670-7_25 · Cited by 80
Conceptual and meta learning during coached problem solving
VanLehn, K. A. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61327-7_99 · Cited by 75
Self-citationHuber, M. J., Durfee, E. H., & Wellman, M. P. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-332-5.50048-1 · Cited by 73
Student Modeling from Conventional Test Data: A Bayesian Approach without Priors
VanLehn, K. A., Niu, Z., Siler, S. A., & Gertner, A. S. (1998) · Lecture notes in computer science · doi:10.1007/3-540-68716-5_49 · Cited by 72
Self-citationDT Tutor: A Decision-TheoreticDynamic Approach for Optimal Selection of Tutorial Actions
Murray, R. C., & VanLehn, K. A. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_19 · Cited by 71
Self-citationJ. Evaluation on an assessment system based on Bayesian student modeling
VanLehn, K. A., & Martin, J. J. (1997) · NPARC · Cited by 55
Self-citationFading and Deepening: The Next Steps for Andes and Other Model-Tracing Tutors
VanLehn, K. A., Freedman, R., Jordan, P., Murray, C., Osan, R. M., Ringenberg, M. A., Rosé, C. P., Schulze, K. G., Shelby, R., Treacy, D. J., et al. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_51 · Cited by 54
Self-citationA dempster-shafer approach to modeling agent preferences for plan recognition
Bauer, M. (1996) · User Modeling and User-Adapted Interaction · doi:10.1007/bf01126114 · Cited by 52
Andes: An Intelligent Tutor for Classical Physics
Schulze, K. G., Shelby, R., Treacy, D. J., Wintersgill, M. C., VanLehn, K. A., & Gertner, A. S. (2000) · Journal of Electronic Publishing · doi:10.3998/3336451.0006.110 · Cited by 51
Self-citationConstructing Situation Specific Belief NetworksPreprint
Mahoney, S. M., & Laskey, K. B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7399 · Cited by 46
The Conceptual Helper: An Intelligent Tutoring System for Teaching Fundamental Physics Concepts
Albacete, P., & VanLehn, K. A. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_60 · Cited by 44
Self-citationTwo-Phase Updating of Student Models Based on Dynamic Belief Networks
Reye, J. (1998) · Lecture notes in computer science · doi:10.1007/3-540-68716-5_33 · Cited by 38
Conati, C., & VanLehn, K. A. (2001) · Cited by 37
Self-citationBunt, A., Conati, C., Huggett, M., & Müldner, K. (2001) · Cited by 32
Self-citationA tutorial introduction to stochastic simulation algorithms for belief networks
Cousins, S., Chen, W. C., & Frisse, M. E. (1993) · Artificial Intelligence in Medicine · doi:10.1016/0933-3657(93)90020-4 · Cited by 23
A computer framework to support self-explanation*
Conati, C., & Larkin, J. H. (1998) · Cited by 20
Self-citationProbabilistic Plan Recognition for Cognitive Apprenticeship
Conati, C. (2001) · Cited by 13
Self-citationGenerating tailored examples to support learning via self-explanation
Conati, C., & Carenini, G. (2001) · Cited by 12
Self-citationApplications of Learning and Planning Methods
Bourbakis, N. (1991) · WORLD SCIENTIFIC eBooks · doi:10.1142/1317 · Cited by 10
ASPM2: Progress Toward the Analysis of Symbolic Parameter Models
Polk, T. A., VanLehn, K. A., & Kalp, D. (1994) · doi:10.21236/ada284437 · Cited by 4
Self-citationAn A* Approach to Robust Plan Recognition for Intelligent Interfaces
Calistri–Yeh, R. J. (1991) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812812414_0010 · Cited by 3
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Research works printed but not linked: 7
- Breese, J., Goldman, R. and Wellman, P.: 1994, Introduction to the special section on knowledge-based construction of probabilistic and decision models. IEEE Transactions on Systems, Man, and Cybernetics, 24, pp. 1577-1579.
- Huber, M. J., Durfee, E. H. and Wellman, M. P.: 1994, The automated mapping of plans for plan recognition. Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, pp. 344-351.
- Martin, J. and VanLehn, K.: 1993, OLAE: Progress toward a multi-activity, Bayesian student modeller. Artificial Intelligence in Education, 1993: Proceedings of AI-ED 93, Charlottesville, VA, Association for the Advancement of Computing in Education, pp. 410-417.
- Petrushin, V. A., Sinitsa, K.M. and Zherdienko, V.: 1995, Probabilistic approach to adaptive student knowledge assessment: methodology and experiment. Artificial Intelligence in Education: Proceedings of AI-ED ‘95, Washington, DC, U.S.A., pp. 51-58.
- Shelby, R. N., Schulze, K. G., Treacy, D. J., Wintersgill, M. C. and VanLehn, K.: 2001, The Andes Intelligent Tutor: an Evaluation. Proceedings of the Physics Education Research Conference, Rochester, NY.
- Van Mulken, S.: 1996, Reasoning about the user’s decoding of presentations in an intelligent multimedia presentation system. Proceedings of UM ‘96, 5th International Conference on User Modeling, Kailua Kona, HW, U.S.A., pp. 67-74.
- VanLehn, K. and Niu, Z. 2001, Bayesian student modeling, useri nterfaces and feedback: A sensitivity analysis. International Journal of Artificial Intelligence in Education, 12, 154-184.
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Ivon, A., Cooper David, G., Winslow, B., Park, W. B., Kasia, M., & Robert, C. (2009) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/978-1-60750-028-5-17 · Cited by 382
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Haghshenas, E., Mazaheri, A., Gholipour, A., Tavakoli, M., Zandi, N., Narimani, H., Rahimi, F., & Nouri, S. (2010) · The Second International Conference on E-Learning and E-Teaching (ICELET 2010) · doi:10.1109/icelet.2010.5708382 · Cited by 14
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Metadata
- Authors
- Conati, C., Gertner, A., & VanLehn, K.
- Affiliations
- University of British Columbia (CA)
- Mitre (United States) (US)
- University of Pittsburgh (US)
- Corresponding author
- Conati, Cristina
- Gertner, Abigail
- VanLehn, Kurt
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2002-11
- Volume
- 12
- Issue
- 4
- Pages
- 371-417
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W1529098247
- Cited by
- 493
- References
- 65
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 10 · 2023: 8 · 2022: 12 · 2021: 9 · 2020: 15 · 2019: 14 · 2018: 20 · 2017: 26 · 2016: 19 · 2015: 21 · 2014: 26 · 2013: 31 · 2012: 27
- FWCI
- 18.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- Bayesian networks, student modeling, uncertainty management, adaptive tutoring systems, knowledge assessment, plan recognition, model scalability
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