Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

An Autonomous Intelligent Driving Simulation Tutor for Driver Training and Remediation: A Concept Paper

An intelligent tutoring model for use in a driving simulation training platform is proposed. Driving simulators by themselves cannot teach and staffing driving simulators with live trainers limits their ability to reach a wide audience. Research has shown that customized feedback, coupled with active practice in a simulator is very effective in changing a driver’s behavior for the better. A driving simulation training program which utilizes an intelligent tutoring system (ITS) can diagnose driver errors, tailor feedback to the student’s specific needs, determine when a student has mastered a specific skill set and can provide remediation as necessary. A brief discussion of basic ITS architecture is provided. An ITS model that has been successful in teaching individual skills in other domains (such as mental rotation) is applied to driving simulator instruction. The various critical components of the ITS, including the domain model, student model and tutoring model, are discussed in detail and a working example provided

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

The Development and Validation of a System for the Knowledge-Based Tutoring of Special Education Rules and Regulations

Research indicates that school officials fail to identify a relatively high proportion of school-aged children with behavioral or emotional handicaps. As a result, these children may not be receiving the special education services to which they are entitled. Multidisciplinary team members may be failing to identify these children because they lack understanding of special education rules and regulations. The purpose of this project was to combine the technologies of expert systems and mastery-based instruction to develop an inservice and preservice training program capable of producing mastery-level performance of the skills required to identify children with behavioral or emotional handicaps. Borg and Gall\u27s ( 983) research and development cycle provided the model for developing, testing, and revising the program. Prototype evaluations and large-scale field tests revealed that the program met its performance and user satisfaction objectives when administered under conditions of independent administration. However, a failure on the use and part of remote remote administrators to comply with prescribed program administration procedures allowed an unacceptable number of subjects to end training without completing all computer exercises. Attention to administration procedures contributed to the success of the project in meeting its performance and user satisfaction objectives in the final operational field test. The positive findings of the project have implications on two levels. First, the findings are important for the positive effect they may have on the lives of children. Decision-making errors on the part of multidisciplinary team members can be costly to children with behavioral or emotional handicaps, as well as to other children. The evidence obtained in this project suggests that multidisciplinary team members can be trained to accurately identify children with behavioral or emotional handicaps. On another, and perhaps more important, level, the findings have implications for the design of effective inservice and preservice training programs. The application of innovative technologies to inservice and preservice training problems does not necessarily result in the development of products capable of producing mastery-level decision-making performance. The positive results achieved in the present project suggest that those seeking to apply innovative technologies to inservice and preservice training problems take into account basic instructional design principles

Recommending learning material in Intelligent Tutoring Systems

Nowadays, intelligent e-learning systems which can adapt to learner's needs and preferences, became very popular. Many studies have demonstrated that such systems can increase the eects of learning. However, providing adaptability requires consideration of many factors. The main problems concern user modeling and personalization, collaborative learning, determining and modifying learning senarios, analyzing learner's learning styles. Determining the optimal learning scenario adapted to students' needs is very important part of an e-learning system. According to psychological research, learning path should follow the students' needs, such as (i.a.): content, level of diculty or presentation version. Optimal learning path can allow for easier and faster gaining of knowledge. In this paper an overview of methods for recommending learning material is presented. Moreover, a method for determining a learning scenario in Intelligent Tutoring Systems is proposed. For this purpose, an Analytic Hierarchy Process (AHP) method is used

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified

Mining Web-based Educational Systems to Predict Student Learning Achievements

Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases). The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered. In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models

Computer-Driven Instructional Design with INTUITEL

INTUITEL is a research project that was co-financed by the European Commission with the aim to advance state-of-the-art e-learning systems via addition of guidance and feedback for learners. Through a combination of pedagogical knowledge, measured learning progress and a broad range of environmental and background data, INTUITEL systems will provide guidance towards an optimal learning pathway. This allows INTUITEL-enabled learning management systems to offer learners automated, personalised learning support so far only provided by human tutors INTUITEL is - in the first place - a design pattern for the creation of adaptive e-learning systems. It focuses on the reusability of existing learning material and especially the annotation with semantic meta data. INTUITEL introduces a novel approach that describes learning material as well as didactic and pedagogical meta knowledge by the use of ontologies. Learning recommendations are inferred from these ontologies during runtime. This way INTUITEL solves a common problem in the field of adaptive systems: it is not restricted to a certain field. Any content from any domain can be annotated. The INTUITEL research team also developed a prototype system. Both the theoretical foundations and how to implement your own INTUITEL system are discussed in this book