The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
Preparing students for future learning with Teachable Agents
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
“Another AI system used path traversal algorithms to establish causal chains, by which students were provided with elaborated feedback and hints rather than the correct answers.e learning-by-teaching context was constructed by learners' self-organization of interactions and their interpretation of feedback [38].”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [38]
In the citing paper
“Although AI techniques displayed and predicted smart computation in the education domain, they generally fail to bring”
From A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021) · cites this paper as [38]
Citations
Cites
(2015) · Routledge eBooks · doi:10.4324/9781315712383-22 · Cited by 10,894
What Video Games Have to Teach Us about Learning and Literacy
(2004) · Education + Training · doi:10.1108/et.2004.00446dae.002 · Cited by 6,674
What video games have to teach us about learning and literacy
Gee, J. P. (2003) · Computers in Entertainment · doi:10.1145/950566.950595 · Cited by 5,735
Novak, J. D., Gowin, D. B., & Kahle, J. B. (1984) · doi:10.1017/cbo9781139173469 · Cited by 5,199
Reciprocal Teaching of Comprehension-Fostering and Comprehension-Monitoring Activities
Palinscar, A. S., & Brown, A. L. (1984) · Cognition and Instruction · doi:10.1207/s1532690xci0102_1 · Cited by 5,186
Forbus, K. D. (1984) · Artificial Intelligence · doi:10.1016/0004-3702(84)90038-9 · Cited by 2,491
COGNITIVE APPRENTICESHIP: MAKING THINKING VISIBLE
Collins, A., Brown, J. S., & Holum, A. (1991) · Cited by 1,516
Handbook of Metacognition in Education
Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009) · doi:10.4324/9780203876428 · Cited by 1,509
Kuhl, P. K., Tsao, F.-M., & Liu, H.-M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1532872100 · Cited by 1,237 · cited 1×
“For example, one outstanding question involves the value of videogames and other interactive media frequently found outside of school (e.g., Barron et al. 2009; Gee 2003; Kuhl et al. 2003; Ito 2009; Stevens et al. 2008).”
BackgroundLearning With Concept and Knowledge Maps: A Meta-Analysis
Nesbit, J. C., & Adesope, O. O. (2006) · Review of Educational Research · doi:10.3102/00346543076003413 · Cited by 932 · cited 1×
“Concept maps have proven to be a useful paper-and-pencil technology for improving knowledge retention and integration (for reviews, see Hilbert and Renkl 2008; Horton et al. 1993; Nesbit and Adesope 2006; O’Donnell et al. 2002).”
Background
Research works printed but not linked: 2
- Gopnik, A., & Schulz, L. (Eds.). (2007). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press.
- Ito, M. (2009). Hanging out, messing around, geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press. · DOI
Cited by
Sorted by citations
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364
“Although AI techniques displayed and predicted smart computation in the education domain, they generally fail to bring “added-value” to large-scale students because of the concern of costs, and the mainstream is still occupied by “basic value” [38].”
BackgroundMethodAI technologies for education: Recent research & future directions
Zhang, K., & Aslan, A. B. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100025 · Cited by 923
Intelligent tutoring systems and learning outcomes: A meta-analysis
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014) · Journal of Educational Psychology · doi:10.1037/a0037123 · Cited by 840
Dai, C., & Ke, F. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100087 · Cited by 357
García‐Martínez, I., Fernández‐Batanero, J. M., Cerero, J. F., & León, S. P. (2023) · Journal of New Approaches in Educational Research · doi:10.7821/naer.2023.1.1240 · Cited by 386
Metadata
- Authors
- Chin, D. B., Dohmen, I. M., Cheng, B. H., Oppezzo, M. A., Chase, C. C., & Schwartz, D. L.
- Affiliations
- Stanford University (US)
- SRI International (US)
- Corresponding author
- Chin, Doris B.
- ORCID
- Venue
- Educational Technology Research and Development
- Published
- 2010-03-05
- Volume
- 58
- Issue
- 6
- Pages
- 649-669
- ISSN
- 1042-1629 (ISSN-L)
- 1556-6501
- OpenAlex
- W2093782067
- Cited by
- 119
- References
- 45
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 7 · 2018: 5 · 2017: 7 · 2016: 4 · 2015: 9 · 2014: 12 · 2013: 12 · 2012: 7
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Educational Technology Research and Development (publishedVersion)
- Society for Research on Educational Effectiveness
- 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 › Education › Artificial Intelligence in Education
- Keywords
- teachable agents, concept maps, artificial intelligence in education, elementary school science, transfer of 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
