As simulator-based training systems become more complex, the
amount of effort required to generate, monitor, and maintain training
exercises multiplies greatly. This has significantly increased the
burden on the instructors, potentially making the training experience
less efficient as well as less effective. Research on intelligent
tutoring systems (ITS) has largely addressed this issue by replacing the
instructor with a computer model of the appropriate pedagogical concepts
and the domain expertise. While this approach is highly desirable, the
effort required to develop and maintain an ITS can be quite significant.
A more modest as well as practical alternative to an ITS is the
development of intelligent computer-based tools that can support the
instructors in their tasks. The advantage of this approach is that
various tools can be developed to address the different aspects of the
instructor's duties. Moreover, without the burden of having to replace
the instructor, these tools are more easily developed and fielded in
existing trainers. One aspect of an instructor's task is to assess the
students' performance after each training exercise and select the next
exercise based on their previous performances. It would clearly be
advantageous if this exercise selection process were to be automated,
thus relieving the instructor of a significant burden and allowing him
to concentrate on other tasks. Therefore, the focus of this paper is the
development of a stand-alone system capable of determining exercise
progression and remediation automatically during a training session in a
simulator-based trainer, on the basis of the students's past
performance. Instructional heuristics were developed to carry out the
exercise progression process. A prototype was developed and applied to
gunnery training on the Army M1 main battle tank
"In recent years, researchers have attempted to develop computer-assisted learning systems that are more intelligent and individualized. For example, Gonzalez and Ingraham (1994) developed a tutoring system that is capable of automatically determining exercise progression and remediation during a training session, relative to the students' past performance; Harp, Samad, Villano et al. (1995) employed the technique of neural networks to model the behavior of students in the context of an intelligent tutoring system. They used self-organizing feature maps to capture the possible states of student knowledge from an existing item bank; Rowe and Galvin (1998) employed planning methods, consistency enforcement, objects, and structured menu tools to construct intelligent simulationbased tutors for procedural skills. "
[Show abstract][Hide abstract] ABSTRACT: The popularity of web-based learning systems has encouraged researchers to pay attention to several new issues. One of the most important issues is the development of new techniques to provide personalized teaching materials. Although several frameworks or methods have been proposed, it remains a challenging issue to design an easy-to-realize framework for developing adaptive learning systems that benefit student learning performance. In this paper, we propose a modular framework that can segment and transform teaching materials into modular learning objects based on the SCORM standard such that subject contents can be composed dynamically according to the profile and portfolio of individual students. An adaptive learning system has been developed based on this innovative approach. Based on the experimental results of a college computer course, we conclude that the proposed framework can be used to develop adaptive learning systems that benefit the students' learning achievements.
Educational Technology & Society 04/2008; 11(2):171-191. · 1.01 Impact Factor
"Rapid advances in computer and communication technologies have encouraged researchers to apply the technologies to the development of computer-aided tutoring and testing systems (Antao et al. 2000; Chou 2000). For example, Vasandani et al. proposed a system which could assist in organising system knowledge and operational information to enhance operation performance (Vasandani and Govindaraj 1991, 1995; Vasandani et al. 1989); moreover, Gonzalez and Ingraham (1994) presented a system that automatically determined exercise progression and remediation during a training session based on past student performance. Meanwhile, various techniques and tools for developing intelligent tutoring systems were also being proposed. "
[Show abstract][Hide abstract] ABSTRACT: In the past decade, researchers have attempted to develop computer‐assisted learning and testing systems to help students improve their learning performance. Conventional testing systems simply provide students with a score, and do not offer sufficient information in order to improve their learning performance. It would be of more benefit to students if the test results could be critically analysed and hence learning suggestions could be offered accordingly. This study proposes an algorithm for diagnosing students’ learning problems and provides personalised learning suggestions for Science and Mathematics courses. An intelligent tutoring, evaluation and diagnosis system has been implemented based on the novel approach. Experimental results on a Mathematics course have demonstrated the feasibility of this approach in enhancing students’ learning performance, making it highly promising for further study.
Innovations in Education and Teaching International 02/2008; 45(1-1):77-89. DOI:10.1080/14703290701757476 · 0.79 Impact Factor
"For example, Vasandani and Govindaraj ,  proposed an intelligent tutoring system that can assist operators in organizing their system knowledge and operational information to enhance operation performance. Gonzalez and Ingraham  developed an intelligent tutoring system which is capable of determining exercise progression and remediation automatically during a training session according to the students' past performance. Harp et al. employed the technique of neural networks to model the behavior of students in the context of an intelligent tutoring system, and they used self-organizing feature maps to capture the possible states of student knowledge from an existing test database . "
[Show abstract][Hide abstract] ABSTRACT: In recent years, researchers have attempted to apply network techniques to the development of computer-assisted learning systems. They have also attempted to develop more effective programs to test and improve the learning performance of students. However, the most conventional testing systems present only a score as a test result, which is not capable of providing advice to the students. To cope with this problem, we propose a gray forecast approach for modeling the relationships among the subject concepts and the test items. These relationships are then employed to diagnose the student learning problems. A testing and diagnostic system based on our approach has, therefore, been implemented. Some experimental results have demonstrated the feasibility of this approach in enhancing the students' learning performance, making it highly promising for further study
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 02/2007; 37(1-37):98 - 108. DOI:10.1109/TSMCC.2006.876062 · 2.17 Impact Factor
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