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A Meta-Analysis of the Effectiveness of Intelligent Tutoring Systems on K–12 Students’ Mathematical Learning
Resource type
Journal Article
Authors/contributors
- Steenbergen-Hu, Saiying (Author)
- Cooper, Harris (Author)
Title
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 ϭ 0.01 to g ϭ 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.
Pages
18
Date
2013
Language
en
DOI
Library Catalogue
Zotero
Citation
Steenbergen-Hu, S., & Cooper, H. (2013). A Meta-Analysis of the Effectiveness of Intelligent Tutoring Systems on K–12 Students’ Mathematical Learning. 18. https://doi.org/10.1037/a0032447
Outcome measure
Instructional domain (subject)
Education Level and Type
Groups of students
Moderating variables
Tech Hardware
Tech mechanism
Learning Approach
Research methods
Effect size/ heterogeneity
HIC/LMIC
Quality of research
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