Government-Sponsored Research and Development Efforts in the Area of Intelligent Tutoring Systems. Summary Report.

1995 ◽  
Author(s):  
Christine Youngblut
Keyword(s):  
Research And Development ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Summary Report ◽  
Sponsored Research ◽  
Tutoring Systems

Using Effect Size for Group Modeling in E-Learning Systems

2011 ◽  
pp. 237-257
Author(s):  
Divna Krpan ◽  
Suzana Tomaš ◽  
Roko Vladušic
Keyword(s):  
Research And Development ◽  
Future Development ◽  
Effect Size ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Tutoring Systems ◽  
Group Modeling ◽  
Modeling Group ◽  
E Learning

There is great need for collaboration in education and e-learning systems which imply the necessity for group modeling. Since Bloom’s experiment, which produced effect size of 2-sigma, there were many attempts to repeat those results with intelligent tutoring systems. Our experiments show effectiveness of xTEx-Sys in measure of effect size. The goal of our research and development is to get as close as possible to effect size of 2-sigma. There is greater need for collaboration in e-learning systems and there are some indications that collaboration could increase effectiveness. Since collaboration is closely coupled with groups, directions for future development and exploration of e-learning systems lay in field of group modeling. Group modeling also implies creation of stereotype models.

Intelligent Tutoring Systems for Distributed Learning

2011 ◽  
pp. 162-183 ◽  
Author(s):  
Mohamed Ally
Keyword(s):  
Research And Development ◽  
Multidisciplinary Team ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Distributed Learning ◽  
Future Research ◽  
Student Model ◽  
Tutoring Systems ◽  
Individual Learner ◽  
Teaching Situation

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

Intelligent Tutoring Systems for Distributed Learning

2008 ◽  
pp. 292-306 ◽  
Author(s):  
Mohamed Ally
Keyword(s):  
Research And Development ◽  
Multidisciplinary Team ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Distributed Learning ◽  
Future Research ◽  
Student Model ◽  
Tutoring Systems ◽  
Individual Learner ◽  
Teaching Situation

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

Intelligent tutoring systems for aviation automation

2000 ◽  
Author(s):  
Christine Mitchel ◽  
Alan Chappell ◽  
W. Gray ◽  
Alex Quinn ◽  
David Thurman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

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