The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning.
Abstract
In this study, we meta-analyzed empirical research of the effectiveness of intelligent tutoring systems (ITS) on K–12 students’ mathematical learning. A total of 26 reports containing 34 independent samples met study inclusion criteria. The reports appeared between 1997 and 2010. The majority of included studies compared the effectiveness of ITS with that of regular classroom instruction. A few studies compared ITS with human tutoring or homework practices. Among the major findings are (a) overall, ITS had no negative and perhaps a small positive effect on K–12 students’ mathematical learning, as indicated by the average effect sizes ranging from g = 0.01 to g = 0.09, and (b) on the basis of the few studies that compared ITS with homework or human tutoring, the effectiveness of ITS appeared to be small to modest. Moderator analyses revealed 2 findings of practical importance. First, the effects of ITS appeared to be greater when the interventions lasted for less than a school year than when they lasted for 1 school year or longer. Second, the effectiveness of ITS for helping students drawn from the general population was greater than for helping low achievers. This finding draws attentions to the issue of whether computerized learning might contribute to the achievement gap between students with different achievement levels and aptitudes.
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“Steenbergen-Hu and Cooper (2013) found no difference in the effect of intelligent tutoring systems based on whether the assessments were course-related data, researcher-made tests, or standardized tests.”
From Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education (2017) · cites this paper as Steenbergen-Hu and Cooper (2013)
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Cites
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 2×
“Although not as effective as what Bloom (1984) found, a recent meta-review by VanLehn (2011) found that human tutoring had a positive impact of d 0.79 on students’ learning.”
InfluentialVanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323
Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011) · Review of Educational Research · doi:10.3102/0034654310393361 · Cited by 1,029
“Intelligent tutoring systems,”
Graesser, A. C., Hu, X., & Sottilare, R. (2018) · International Handbook of the Learning Sciences · doi:10.4324/9781315617572-24 · Cited by 182
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Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review
Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research · doi:10.3102/0034654315581420 · Cited by 930
Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863
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
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103897 · Cited by 574
Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425
Metadata
- Authors
- Steenbergen-Hu, S., & Cooper, H.
- Venue
- Journal of Educational Psychology
- Published
- 2013-11
- Volume
- 105
- Issue
- 4
- Pages
- 970-987
- ISSN
- 1939-2176
- 0022-0663
- Cited by
- 175
- References
- 4
- Language
- en
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