CoChat
Sign Up
Cited by 55

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics

Nye, Benjamin D., Pavlik, Philip I., Windsor, Alistair, Olney, Andrew M., Hajeer, Mustafa, Hu, Xiangen·2018·International Journal of STEM Education
DOI 10.1186/s40594-018-0109-4
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40594-018-0109-4

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 35

  1. Adcock, AB, & Eck, RNV (2005). Reliability and factor structure of the attitude toward tutoring agent scale (attas). Journal of Interactive Learning Research, 16(2), 195.
  2. Aleven, V, & Koedinger, K (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147–179. · DOI · Paper page
  3. Aleven, V, Ogan, A, Popescu, O (2004). Evaluating the effectiveness of a tutorial dialogue system for self-explanation. In Intelligent Tutoring Systems (ITS) 2004. Springer, New York, (pp. 443–454).
  4. Aleven, V, McLaren, BM, Sewall, J, Koedinger, KR (2009). A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105–154.
  5. Atkinson, RK, Renkl, A, Merrill, MM (2003). Transitioning from studying examples to solving problems: effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774. · DOI · Paper page
  6. Beck, JE, & Gong, Y (2013). Wheel-spinning: students who fail to master a skill. In International Conference on Artificial Intelligence in Education. Springer, Heidelberg, (pp. 431–440). · DOI
  7. Bloom, BS. (1956). Taxonomy of educational objectives: the classification of educational goals. Harlow: Longman Group.
  8. Chi, M, & VanLehn, K (2010). Meta-cognitive strategy instruction in intelligent tutoring systems: how, when, and why. Educational Technology and Society, 13(1), 25–39.
  9. Chi, MT (2009). Active-constructive-interactive: a conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105. · DOI
  10. Craig, SD, Driscoll, DM, Gholson, B (2004). Constructing knowledge from dialog in an intelligent tutoring system: interactive learning, vicarious learning, and pedagogical agents. Journal of Educational Multimedia and Hypermedia, 13(2), 163.
  11. Craig, SD, Hu, X, Graesser, AC, Bargagliotti, AE, Sterbinsky, A, Cheney, KR, Okwumabua, T (2013). The impact of a technology-based mathematics after-school program using aleks on student’s knowledge and behaviors. Computers & Education, 68, 495–504. · DOI
  12. Falmagne, JC, Albert, D, Doble, C, Eppstein, D, Hu, X, (Eds.) (2013). Knowledge spaces. Berlin, Germany: Springer. · DOI
  13. Graesser, AC, Langston, MC, Lang, KL (1992). Designing educational software around questioning. Journal of Interactive Learning Research, 3(2), 235.
  14. Graesser, AC, D’Mello, SK, Hu, X, Cai, Z, Olney, A, Morgan, B (2012). AutoTutor. In: McCarthy, PM, & Boonthum, C (Eds.) In Applied natural language processing and content analysis: identification, investigation and resolution. IGI Global, Hershey, PA, (pp. 169–187). · DOI
  15. Heidig, S, & Clarebout, G (2011). Do pedagogical agents make a difference to student motivation and learning. Educational Research Review, 6(1), 27–54. · DOI
  16. Huang, X, Craig, SD, Xie, J, Graesser, A, Hu, X (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265. · DOI
  17. Landauer, TK, Foltz, PW, Laham, D (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284. · DOI · Paper page
  18. Lehman, B, D’Mello, S, Graesser, A (2012). Interventions to regulate confusion during learning. In Proceedings of the 11th international conference on Intelligent Tutoring Systems. Springer-Verlag, Berlin, Heidelberg, ITS’12, (pp. 576–578). · DOI
  19. Nye, BD (2016). Its, the end of the world as we know it: transitioning aied into a service-oriented ecosystem. International Journal of Artificial Intelligence in Education, 26(2), 756–770. · DOI
  20. Nye, BD, Graesser, AC, Hu, X (2013). Multimedia learning in intelligent tutoring systems. In: Mayer, RE (Ed.) In Multimedia Learning. 3rd Ed. Cambridge University Press, New York.
  21. Nye, BD, Graesser, AC, Hu, X (2014a). AutoTutor and family: a review of 17 years of science and math tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. · DOI
  22. Nye, BD, Graesser, AC, Hu, X, Cai, Z (2014b). AutoTutor in the cloud: a service-oriented paradigm for an interoperable natural-language its. Journal of Advanced Distributed Learning Technology, 2(6), 35–48.
  23. Renkl, A (2005). The worked-out-example principle in multimedia learning. In: Mayer, RE (Ed.) In The Cambridge handbook of multimedia learning. Cambridge University Press, New York, (pp. 229–245). · DOI
  24. Roschelle, J, & Kaput, J (1996). Educational software architecture and systemic impact: the promise of component software. Journal of Educational Computing Research, 14(3), 217–228. · DOI
  25. Sabo, KE, Atkinson, RK, Barrus, AL, Joseph, SS, Perez, RS (2013). Searching for the two sigma advantage: evaluating algebra intelligent tutors. Computers in Human Behavior, 29(4), 1833–1840. · DOI
  26. Schommer-Aikins, M, Duell, OK, Hutter, R (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289–304. · DOI
  27. Schroeder, NL, Adesope, OO, Gilbert, RB (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39. · DOI
  28. Schwonke, R, Renkl, A, Krieg, C, Wittwer, J, Aleven, V, Salden, R (2009). The worked-example effect: not an artefact of lousy control conditions. Computers in Human Behavior, 25(2, SI), 258–266. · DOI
  29. Schworm, S, & Renkl, A (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285. · DOI
  30. Sullins, J, Meister, R, Craig, S, Wilson, W, Bargagliotti, A, Hu, X (2013). Is there a relationship between interacting with a mathematical intelligent tutoring system and students performance on standardized high-stake tests. Knowledge Spaces: Applications to education, 69–78. · DOI
  31. VanLehn, K (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265.
  32. VanLehn, K, Siler, S, Murray, C, Yamauchi, T, Baggett, WB (2003). Why do only some events cause learning during human tutoring?Cognition and Instruction, 21(3), 209–249. · DOI
  33. Vanlehn, K, Graesser, AC, Jackson, GT, Jordan, P, Olney, A, Rosé, CP (2007). When are tutorial dialogues more effective than reading?Cognitive Science, 31(1), 3–62. http://www.ncbi.nlm.nih.gov/pubmed/21635287 . · DOI · Paper page
  34. Venkatesh, V, Morris, MG, Davis, GB, Davis, FD (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. · DOI · Paper page
  35. Willingham, DT. (2009). Why don’t students like school?: a cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco: Wiley.

Cited by

Sorted by citations

See all 55

Metadata

Authors
Nye, B. D., Pavlik, P. I., Windsor, A., Olney, A. M., Hajeer, M., & Hu, X.
Venue
International Journal of STEM Education
Publisher
Springer Science and Business Media LLC
Published
2018-04-13
Volume
5
Issue
1
ISSN
2196-7822
DOI
10.1186/s40594-018-0109-4
Cited by
55
References
5
Language
en
Licence
Funders
Office of Naval Research (N00014-12-C-0643)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. https://app.cochat.ai/papers/10.1186/s40594-018-0109-4
The paper
Nye, B. D., Pavlik, P. I., Windsor, A., Olney, A. M., Hajeer, M., & Hu, X. (2018). SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. International Journal of STEM Education. https://doi.org/10.1186/s40594-018-0109-4