Effects of Web-based Intelligent Tutoring Systems on Academic Achievement and Retention (original) (raw)
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Journal of Educational Psychology, 2014
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 ϭ .32 to g ϭ .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.
Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis
Journal of Educational Psychology, 2014
Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g = .42), non-ITS computer-based instruction (g = .57), and textbooks or workbooks (g = .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g = –.11) or small-group instruction (g = .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.
Intelligent tutoring systems and learning performance
Online Information Review, 2019
PurposeIntelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.Design/methodology/approachData were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.FindingsData analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student ...
Comparing Learning Content Management System’s and Intelligent Tutoring System’s Effectiveness
2008
All instructional software should be evaluated before being used in educational process, because it is important to know whether it actually improves the student performance. Within the context of evaluating the educational influence of learning and teaching process, we measure educational influence by using the effect size as metric. In this paper, we presented the results of an experiment where we have compared one learning content management system, the Blackboard TM , with a representative of Web-based authoring shells for building intelligent tutoring systems, the xTEx-Sys. The experiment was coordinated remotely from distant location (another continent) by the developers of the xTEx-Sys, and directly conducted by the Blackboard TM using expert. The results gained through this experiment were not a total surprise, because this was the first time that the English speaking students, used to working with Blackboard TM , have used the xTEx-Sys, as well as, the first time that an experiment was conducted by a person who had no part in designing the xTEx-Sys.
Effects of Intelligent Tutoring Systems (ITS) on Personalized Learning (PL)
Creative Education, 2020
With the advancements of technological solutions and the changes in human society has brought personalized learning into the limelight. A major technological solution has stream rolled personalized learning around the world in the development and advancements of Intelligent tutoring systems. The review of previous researches and in-depth analysis of several studies have proved that an intelligent tutoring system has made a positive impact on personalized learning, bringing some visible contributions in enhancing the performance of students and providing better time management. This research explores and unveils what personalized learning is all about and the role of an intelligent tutoring system in personalized learning. The work also covers how intelligent tutoring system has enhanced the performance of students, reduced cost for training institutes and educational system. The data in this research was collected through several means ranging from Internet research, one-on-one interviews, observations, and Educational Focus groups. Through the research methods, theoretical and empirical data were gathered. For interviews, data was effectively analyzed using content analysis techniques. The research work concludes with acknowledgment of the effects of intelligent tutoring system on personalized learning.
US-China Education Review A, 2015
This study aims to determine the influence of distance asynchronous teaching of Physics-I topics via intelligent tutoring systems (ITSs) on academic achievement and permanence. A Web-based learning environment was created by use of an ITS called "Turkish Intelligent Tutoring System" (TURKZOS) for such Physics-I units as work, energy, and conservation of energy. The experimental group of the study consisted of 26 students who had computer and Internet access and participated in the study voluntarily. The achievement test developed by the researchers was used for collecting data. This test was conducted as pre-test and post-test before and after the experimental procedure respectively. The same test was administered to measure permanence 45 days later following the performance of the post-test. The obtained data were analyzed via paired t-test. Mean pre-test score was found to be 23.88, and mean post-test score was found to be 73.80. Mean permanence test, on the other hand, was found to be 71.88. When mean pre-test and post-test scores were compared, a significant difference was identified in favor of the mean post-test score (p < 0.05). In addition, a significant difference was detected between mean permanence test and pre-test scores (p < 0.005). The mean permanence test score was higher than the mean pre-test score. It was concluded that intelligent learning environments created through Web-based tutoring systems have a positive influence on academic achievement and permanence in physics teaching.
2018
With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITSs). This paper focused on the variant characteristics of ITSs developed across different educational fields. The original studies from 2007 to 2017 were extracted from the PubMed, ProQuest, Scopus, Google scholar, Embase, Cochrane, and Web of Science databases. Finally, 53 papers were included in the study based on inclusion criteria. The educational fields in the ITSs were mainly computer sciences (37.73%). Action-condition rule-based reasoning, data mining, and Bayesian network with 33.96%, 22.64%, and 20.75% frequency respectively, were the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner's model, and classify or cluster learners. Specifically, the performance of the system, learner's performance, and experiences were used for evaluation of ITSs. Most ITSs were designed for web user interfaces. Although these systems could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decision-making in physics, chemistry, and clinical fields. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, this study has recommended the development and evaluation of mobile-based ITSs. ARTICLE HISTORY