Embedded Training Intelligent Tutoring Systems (ITS) for the Future Combat Systems (FCS) Command and Control (C2) Vehicle

2005 ◽  
Author(s):  
George M. Burmester ◽  
Dick Stottler ◽  
John L. Hart
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Command And Control ◽  
Tutoring Systems ◽  
The Future ◽  
Future Combat Systems ◽  
And Control

scholarly journals “How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

2008 ◽  
Vol 24 (4) ◽  
pp. 1342-1363 ◽  
Author(s):  
Abdolhossein Sarrafzadeh ◽  
Samuel Alexander ◽  
Farhad Dadgostar ◽  
Chao Fan ◽  
Abbas Bigdeli
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
The Future

scholarly journals THE FUTURE DIRECTION OF CYBEROLOGY

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
AHAMAD TARMIZI Azizan ◽  
Tse Guan Tan ◽  
Ahmad Rasdan Ismail ◽  
Nik Zulkarnaen Khidzir
Keyword(s):  
Future Development ◽  
Intelligent Tutoring Systems ◽  
Review Paper ◽  
Intelligent Tutoring ◽  
Mobile Games ◽  
Tutoring Systems ◽  
The Future ◽  
Future Potential ◽  
Computer Generated Imagery ◽  
Future Direction

AbstrakObjektif untuk kertas kajian ini ialah untuk membincangkan hala tuju masa depan dunia siberologi.Ianya terdiri daripada tujuh kunci utama iaitu: 1) memfokuskan pada hala tuju masa depan infografik.2) menerangkan kajian masa hadapan tentang kejuruteraan perisian. 3) Menerokai “storytelling” digitaluntuk masa akan datang. 4) Menerangkan trend masa hadapan dalam pengimejan berkomputer. 5)membongkarkan potensi permainan mobil di masa hadapan. 6) mendedahkan pembangunan masadepan sistem tutor pintar. Akhir sekali, 7) membincangkan tentang kajian masa depan tentang interaksicomputer insan. Abstract The objective of this review paper is to discuss the future direction of cyberology. It consists of seven mainparts. Part 1 of this review focused on future direction of infographics. Part 2 of this review presents thefuture study of software engineering. Part 3 explores the future of digital storytelling. Part 4 shows thefuture trends in computer generated imagery. Part 5 discovers future potential of mobile games. Part 6reveals the future development of intelligent tutoring systems. Lastly, Part 7 discusses the future researchof human computer interaction.

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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