The DOI matches its Crossref record.
ASSISTments Dataset from Multiple Randomized Controlled Experiments
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DKT has historically been effective on other datasets, which are both larger and in other domains, such as EdNet, Assistments, and KhanAcademy.
“By contrast, DKT has historically been effective on other datasets, which are both larger and in other domains, such as EdNet [13], Assistments [41] and KhanAcademy [34].”
From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [41]
The Contributions section identifies the paper as presenting an ASSISTments dataset, and the ASSISTments and Data Description sections describe its mathematics-learning domain and size (8,297 students and 102,252 attempted problems); page numbers are unavailable in our copy. The paper does not report that DKT was effective on the dataset or establish that it was larger than the citing paper’s dataset. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 10
- ASSISTmentsTestBed (2015) The ASSISTmentsTestBed. A website that supports researchers funded via the Heffernan (2014) grant. Retrieved from www.assistmentstestbed.org
- Heffernan (2014) "SI2-SSE: Adding Research Accounts to the ASSISTments' Platform: Helping Researchers Do Randomized Controlled Studies with Thousands of Students." An NSF proposal abstract
- Lang, C., Heffernan, N., Ostrow, K., & Wang, Y. (2015). The Impact of Incorporating Student Confidence Items into an Intelligent Tutor: A Randomized Controlled Trial. In Santos, et al. (eds.) Proceedings of the 8th Int Conf on EDM. pp. 144--149.
- Ostrow, K. S. (2015). A Multifaceted Consideration of Motivation and Learning within ASSISTments (Master's Thesis, Worcester Polytechnic Institute).
- Ostrow, K. S., & Heffernan, N. T. (2015, June). The Role of Student Choice Within Adaptive Tutoring. In Artificial Intelligence in Education (pp. 752--755). Springer International Publishing.
- 10.1145/2883851.2883872 · DOI
- 10.1109/TLT.2009.23 · DOI
- Selent, D., & Heffernan, N. (2015, June). When More Intelligent Tutoring in the Form of Buggy Messages Does not Help. In Artificial Intelligence in Education (pp. 768--771). Springer International Publishing.
- Thanaporn, P., Selent, D., & Heffernan, N., Beck, J., Zou, J. Using a Single Model across Multiple Interventions to Improve the Detection of Treatment Effects. The 9th International Conference on Educational Data Mining on Educational Data Mining (in submission)
- Xiong, X., Li, S., & Beck, J. E. (2013, May). Will You Get It Right Next Week: Predict Delayed Performance in Enhanced ITS Mastery Cycle. In FLAIRS Conference
Cited by
Sorted by citations
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Shi, Y., Chi, M., Barnes, T., & Price, T. (2022) · Zenodo · doi:10.5281/zenodo.6853105 · Cited by 7
Metadata
- Authors
- Selent, D., Patikorn, T., & Heffernan, N.
- Affiliations
- Worcester Polytechnic Institute, Worcester, MA, USA
- Venue
- Proceedings of the Third (2016) ACM Conference on Learning @ Scale
- Publisher
- ACM
- Published
- 2016-04-25
- Pages
- 181-184
- Cited by
- 29
- References
- 0
- Funders
- National Science Foundation (ACI-1440753, DRL-1252297, DRL-1109483, DRL-1316736, DGE-1535428 & DRL-1031398)
- Institute of Education Sciences (R305A120125 & R305C100024)
- Office of Naval Research
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