The DOI matches its Crossref record; 11 of the 14 references it cites were found in a registry or an index.
Limitations of Student Control: Do Students Know when They Need Help?
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In the citing paper
“While hints are generally effective, some students tend to immediately request bottom-out hints, resulting in less learning (Mathews et al., 2008; Aleven and Koedinger, 2000).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Aleven and Koedinger, 2000
In the citing paper
“According to the standard hints design principle in ITS (VanLehn, 2006), the hint sequence consists of three levels of increasing specificity: 1) Pointing Hint: Directs the student's attention to a specific error or location without revealing the solution (Hume et al., 1996). 2) Teaching Hint: Provides the domain principle or rule required to solve the step (Hume et al., 1996). 3) Bottom-Out Hint:”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Aleven and Koedinger, 2000
Citations
Cites
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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
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Tutoring Answer Explanation Fosters Learning with Understanding
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Self-citationAutomated advice-giving strategies for scientific inquiry
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3 of the 14 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Conati, C., and K. VanLehn, 1999. Teaching Meta-Cognitive Skills: Implementation and Evaluation of a Tutoring System to Guide Self-Explanation While Learning from Examples. In Artificial Intelligence in Education, Proceedings of AIED-99, edited by S. P. Lajoie and M. Vivet, 297–304. Amsterdam: IOS Press.
- Ritter, S., 1997. Communication, Cooperation and Competition among Multiple Tutor Agents. In Artificial Intelligence in Education, Proceedings of AI-ED 97 World Conference, edited by B. du Boulay and R. Mizoguchi, 31–38. Amsterdam: IOS Press.
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Silva, P., Costa, E. D. B., & Fonseca, B. (2013) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/cbie.sbie.2013.386 · Cited by 1
Reflections on Andes’ Goal-free User Interface
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Assistance Dilemma : Giving and Withholding Learning Support
Miwa, K., Terai, H., Matsumuro, M., & Maehigashi, A. (2012) · The Japanese Journal of Educational Psychology · doi:10.5926/jjep.62.156 · Cited by 1
Artificial Intelligence Assisted Learning (AIAL) Blueprint
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Data Analysis and Knowledge Extraction in Educational Data Mining (EDM)
(2015) · International Journal of Science and Research (IJSR) · doi:10.21275/v4i11.sub158841 · Cited by 1
An Adaptive Hint System for Puzzle Games: A Multimodal-based Approach
He, H., Zhu, Y., & Cai, W. (2022) · IEEE Games, Entertainment, Media Conference (GEM) · doi:10.1109/gem56474.2022.10017301 · Cited by 1
Lee, G., Ha, J., & Cha, H. (2024) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2024.27.8.001 · Cited by 1
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Vandewaetere, M., & Wauters, K. (2010) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2010.117 · Cited by 1
Stoic Behavior in Hint Seeking when Learning using an Intelligent Tutoring System
Miwa, K., Terai, H., Kanzaki, N., & Nakaike, R. (2013) · eScholarship (California Digital Library) · Cited by 1
Rough Sets Approximations for Learning Outcomes
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Cao, J., Zhao, C. Q., Schunn, C., McLaughlin, E. A., Lin, J., & Koedinger, K. R. (2026) · arXiv · doi:10.48550/arxiv.2604.07469
Self-citationEmpirical Investigation on Self Fading as Adaptive Behavior of Hint Seeking
Miwa, K., Terai, H., Kanzaki, N., & Nakaike, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30950-2_102
Diagnostic Tests Based on Knowledge States
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Learn with Me: Collaborative Virtual Learning for the Special Children
Valeria, N., & Theng, L. B. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22170-5_42
Automated decision making based on weak orderings
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Metadata
- Authors
- Aleven, V., & Koedinger, K. R.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2000
- Pages
- 292-303
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W2139891935
- Semantic Scholar
- 097fcee1a910a91549bd22fecd14b684e24776e5
- Cited by
- 236
- References
- 14
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 15 · 2023: 12 · 2022: 12 · 2021: 8 · 2020: 8 · 2019: 8 · 2018: 11 · 2017: 12 · 2016: 12 · 2015: 9 · 2014: 10 · 2013: 15 · 2012: 10
- FWCI
- 3.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- intelligent tutoring systems, learner control, metacognition, help-seeking strategies, cognitive tutors, production rules, metacognitive feedback
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