Integrating an Intelligent Tutoring Facility into a Gaming Simulation Environment

1997 ◽  
Vol 12 (3) ◽  
pp. 207-222 ◽  
Author(s):  
Julika Siemer ◽  
Marios C. Angelides
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Simulation Game ◽  
Simulation Environment ◽  
Business Simulation ◽  
Educational Value ◽  
Tutoring Systems ◽  
Gaming Simulation ◽  
Computer Based ◽  
And Training

Gaming simulations and intelligent tutoring systems are both substantive research and development areas within the field of computer-based education and training which have the potential for mutual enhancement. This paper argues that the pedagogical effectiveness of gaming simulations can be increased through the integration of an intelligent tutoring facility and examines possible roles for such support within a gaming simulation environment. It then commences to present INTUITION, the implementation of the Metal Box Business Simulation game, that illustrates how an intelligent tutoring facility may be integrated within a gaming simulation environment in order to increase its educational value.

An Evolution of Tutoring and Training from Humans to Intelligent Systems: Human Factors Considerations

2009 ◽  
Vol 53 (26) ◽  
pp. 1898-1902
Author(s):  
Jessica M. Ray ◽  
John S. Barnett
Keyword(s):  
Human Factors ◽  
Intelligent Tutoring Systems ◽  
Intelligent Systems ◽  
Tutoring Systems ◽  
Technological Advances ◽  
Effective Form ◽  
Computer Based ◽  
Training Research ◽  
Learning Issues ◽  
And Training

As training researchers and developers, we strive to understand and produce effective and efficient training. Research suggests the most effective form of instruction is individualized human tutoring. Yet this is rarely the most efficient form of instruction monetarily or in instructor time. Technological advances and a vision of effective, yet more efficient, computer based tutors has led to the development of sophisticated new training technologies such as Intelligent Tutoring Systems (ITSs). These systems have yet to reach their full forecast potential. In this paper we theorize that issues key to successful advancement of ITSs are human factors issues. Primary of these issues is determining how technology mediation impacts not only cognition, but also other key learning issues such as affect, emotions, motivation, and trust.

scholarly journals The Challenges of Building Intelligent Tutoring Systems for Teams

2016 ◽  
Vol 60 (1) ◽  
pp. 1981-1985 ◽  
Author(s):  
Desmond Bonner ◽  
Stephen Gilbert ◽  
Michael C. Dorneich ◽  
Eliot Winer ◽  
Anne M. Sinatra ◽  
...  
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Lessons Learned ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Team Research ◽  
Individual Instruction ◽  
Individual Task ◽  
And Training

Intelligent Tutoring Systems have been useful for individual instruction and training, but have not been widely created for teams, despite the widespread use of team training and learning in groups. This paper reviews two projects that developed team tutors: the Team Multiple Errands Task (TMET) and the Recon Task developed using the Generalized Intelligent Framework for Tutoring (GIFT). Specifically, this paper 1) analyzes why team tasks have significantly more complexity than an individual task, 2) describes the two team-based platforms for team research, and 3) explores the complexities of team tutor authoring. Results include a recommended process for authoring a team intelligent tutoring system based on our lessons learned that highlights the differences between tutors for individuals and team tutors.

An Investigation Into the Feasibility of Using Intelligent Tutoring Systems in Preparing Students for Laboratory Work

1998 ◽  
Vol 35 (2) ◽  
pp. 107-116 ◽  
Author(s):  
Barry Dwolatzky ◽  
Ashley Levin ◽  
Steven Shulman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Controlled Experiment ◽  
Laboratory Work ◽  
Tutoring Systems ◽  
Computer Based Training ◽  
Training Systems ◽  
Computer Based

Intelligent Tutoring Systems (ITS) are computer based training systems with the ability to adapt to the requirements of each student A prototype ITS was developed to assess the feasibility of using such systems to prepare students for laboratory work. This prototype was evaluated in a controlled experiment involving 252 students.

Enhancing Intelligent Tutoring Systems with the Agent Paradigm

2011 ◽  
pp. 46-69 ◽  
Author(s):  
Xin Bai ◽  
John B. Black
Keyword(s):  
Learning Environment ◽  
Intelligent Tutoring Systems ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Game Based Learning ◽  
Tutoring Systems ◽  
The Real ◽  
Future Design ◽  
Cognitive Framework ◽  
Computer Based

A cognitive framework called REflective Agent Learning environment (REAL) is developed in this study. REAL is a reusable framework that allows researchers to develop a simulation-based learning environment where users can learn through passing their thoughts to some computer-based agents and observe how the agents embodying their knowledge behave as the result of their instruction. Our research benefits from the research in Intelligent Tutoring Systems, game based learning systems, and agent technologies, stressing reflection as part of the thinking processes. It focuses on the design of the framework and the testing of its usability. The external evaluation of specific implementations serves as the guidance for the future design of the REAL applications. We hope, by grounding themselves in the needs of local practice, the REAL applications can give us opportunities to understand how theoretical claims about teaching and learning can be effectively transformed into meaningful learning.

scholarly journals Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 460 ◽  
Author(s):  
Fehaid Alqahtani ◽  
Naeem Ramzan
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Mental States ◽  
Physiological Signals ◽  
Experimental Comparison ◽  
High Precision Measurement ◽  
Tutoring Systems ◽  
Machine Learning Classification ◽  
Computer Based ◽  
Based Instruction

The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner’s emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems.

Enhancing Intelligent Tutoring Systems with the Agent Paradigm

2011 ◽  
pp. 440-463
Author(s):  
Xin Bai ◽  
John B. Black
Keyword(s):  
Learning Environment ◽  
Intelligent Tutoring Systems ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Game Based Learning ◽  
Tutoring Systems ◽  
The Real ◽  
Future Design ◽  
Cognitive Framework ◽  
Computer Based

A cognitive framework called REflective Agent Learning environment (REAL) is developed in this study. REAL is a reusable framework that allows researchers to develop a simulation-based learning environment where users can learn through passing their thoughts to some computer-based agents and observe how the agents embodying their knowledge behave as the result of their instruction. Our research benefits from the research in Intelligent Tutoring Systems, game based learning systems, and agent technologies, stressing reflection as part of the thinking processes. It focuses on the design of the framework and the testing of its usability. The external evaluation of specific implementations serves as the guidance for the future design of the REAL applications. We hope, by grounding themselves in the needs of local practice, the REAL applications can give us opportunities to understand how theoretical claims about teaching and learning can be effectively transformed into meaningful learning.

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