A System Architecture for Affective Meta Intelligent Tutoring Systems

2014 ◽  
pp. 529-534 ◽  
Author(s):  
Javier Gonzalez-Sanchez ◽  
Maria Elena Chavez-Echeagaray ◽  
Kurt VanLehn ◽  
Winslow Burleson ◽  
Sylvie Girard ◽  
...  
Keyword(s):  
System Architecture ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

scholarly journals A Fuzzy Expert System Architecture for Intelligent Tutoring Systems: A Cognitive Mapping Approach

2012 ◽  
Vol 04 (01) ◽  
pp. 29-40 ◽  
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Mahdi Khademian ◽  
Behrouz Minaei-Bidgoli ◽  
Ismail Burhan Türkşen
Keyword(s):  
Expert System ◽  
System Architecture ◽  
Intelligent Tutoring Systems ◽  
Cognitive Mapping ◽  
Intelligent Tutoring ◽  
Fuzzy Expert System ◽  
Tutoring Systems ◽  
Mapping Approach

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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