阅读理解
计算机科学
严格标准化平均差
阅读(过程)
理解力
荟萃分析
多媒体
差异(会计)
数学教育
置信区间
统计
心理学
数学
医学
会计
法学
程序设计语言
业务
内科学
政治学
作者
Zhihong Xu,Kausalai Wijekumar,Gilbert Ramı́rez,Xueyan Hu,Robin Irey
摘要
Abstract This meta‐analysis examined the effectiveness of improving reading comprehension for students in K‐12 classrooms using intelligent tutoring systems (ITSs), a computer‐based learning environment that provides customizable and immediate feedback to the learner. Nineteen studies from 13 publications incorporating approximately 10 000 students were included in the final analysis; using robust variance estimation to account for statistical dependencies, the 19 studies yielded 88 effect size estimates. The meta‐analysis indicated that the overall random effect size of ITSs on reading comprehension was 0.60 (using a mix of standardized and researcher‐designed measures) with a 95% confidence interval 0.36 to 0.85 ( p < 0.001). This review confirms previous studies comparing ITSs to human tutoring: ITSs produced a small effect size when compared to human tutoring (0.20, 0.02–0.38, p = 0.036, n = 21). All comparisons to human tutoring used standardized measures. This review also found that ITSs produced a larger effect size on reading comprehension when compared to traditional instruction (0.86) for mixed measures and (0.26) for standardized measures. These findings may be of interest to practitioners and policy makers seeking to improve reading comprehension using consistent and accessible ITSs. Recommendations for researchers include conducting studies to understand the difference between traditional and updated versions of ITSs and employing valid and reliable standardized tests and researcher‐designed measures.
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