Effectiveness Of Intelligent Tutoring Systems Research Articles

Understanding secondary students' continuance intention to adopt AI-powered intelligent tutoring system for English learning.

Previous studies have demonstrated the effectiveness of intelligent tutoring systems (ITS) in facilitating English learning. However, no empirical research has been conducted on secondary students’ intention to use ITSs in the language domain. This study proposes an extended technology acceptance model (TAM) to predict secondary students’ continuance intention to use and actual use of ITSs for English learning. The model included fifteen hypotheses that were tested with 528 senior secondary students in China. The results of structural equation modeling showed that (1) perceived usefulness and price value had direct positive impacts on continuance intention; (2) perceived ease of use was not directly associated with students’ intention but indirectly influenced intention via perceived usefulness; (3) through the mediation of perceptions, learning goal orientation and facilitating conditions were positively associated with continuance intention; (4) perceived enjoyment positively predicted and anxiety negatively predicted students’ intention to use ITSs; and (5) students’ continuance intention to use ITSs was significantly positively associated with their actual use of ITSs for English learning. The model showed strong explanatory power and might be implemented in future research. This study contributes to the theory and practice of ITSs in K-12 education.

The effectiveness of intelligent tutoring systems on K‐12 students' reading comprehension: A meta‐analysis

AbstractThis 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.

A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning.

This meta-analysis synthesizes research on the effectiveness of intelligent tutoring systems (ITS) for college students. Thirty-five reports were found containing 39 studies assessing the effectiveness of 22 types of ITS in higher education settings. Most frequently studied were AutoTutor, Assessment and Learning in Knowledge Spaces, eXtended Tutor-Expert System, and Web Interface for Statistics Education. Major findings include (a) Overall, ITS had a moderate positive effect on college students’ academic learning (g = .32 to g = .37); (b) ITS were less effective than human tutoring, but they outperformed all other instruction methods and learning activities, including traditional classroom instruction, reading printed text or computerized materials, computer-assisted instruction, laboratory or homework assignments, and no-treatment control; (c) ITS’s effectiveness did not significantly differ by different ITS, subject domain, or the manner or degree of their involvement in instruction and learning; and (d) effectiveness in earlier studies appeared to be significantly greater than that in more recent studies. In addition, there is some evidence suggesting the importance of teachers and pedagogy in ITS-assisted learning.

Supplemental Material for A Meta-Analysis of the Effectiveness of Intelligent Tutoring Systems on College Students’ Academic Learning

Supplemental Material for A Meta-Analysis of the Effectiveness of Intelligent Tutoring Systems on College Students’ Academic Learning

A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning.

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.