A perspective on learning for Intelligent Educational Systems

This paper describes the first year of operation of the CTI Centre for Psychology, which pro-motes the use of computers in psychology teaching in the U.K. We outline current use of computers in teaching and some of the obstacles to the effective development of computer-supported courses. In particular, we focus on the need for closer integration of innovative forms of teaching into courses. We describe the Centre’s strategy for supporting both conventional and innovative computer-supported teaching, and argue that effective use of software depends on both the instructional situation and the educational context.

The results of a survey of experiences, feelings and preconceptions concerning information technology (IT) that University of Oxford history and geography interns bring to the one year secondary teacher training course are reported. A notable finding was the significantly greater importance attributed to the use of computers in subject teaching by geography interns compared with historians, and the relatively large numbers of the latter ascribing little or no importance to the use of computers in the teaching of history. Analysis of the qualitative data provided by this study provided a range of insights into the ways in which geography and history interns viewed information technology. There was a marked difference between the two groups not only in their feelings about IT but in the degree of sophistication with which they articulated IT issues. The geographers were more positive and were able to support their views with specific examples of software and applications. Their level of knowledge a...

Since the early sixties the use of computers in education (computer aided instruction, CAI) has aimed to individualize instruction, i.e., tailor study procedures to the needs and characteristics of the individual student. However, although some progress has been made, CAI is still a long way off from achieving this goal. In practice, with CAI systems the student has a passive role, being exposed to a fixed set of problems that have been stored together with a corresponding set of pre-canned solutions. The tutoring path is rigid, with one-way teaching interaction only and with very little individualization. The possibility to get over the limits of the traditional CAI came from studies on cognitive psychology and artificial intelligence, with particular attention paid to the interaction between teacher and student. These researches led to the development of new educational systems, called intelligent computer aided instruction (ICAI) systems or intelligent tutoring systems (ITS), where artificial intelligence techniques are largely applied.KeywordsInference RuleTutoring SystemSequent CalculusStudent ModelReasoning ProcedureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

PurposeThe development of adaptable and intelligent educational systems is widely considered one of the great challenges in scientific research. Among key elements for building advanced training systems, an important role is played by methodologies chosen for knowledge representation. In this scenario, the introduction of ontology formalism can improve the quality of formative process, allowing the introduction of new and effective services. Ontology can lead to important improvements in the definition of courses knowledge domain, in the generation of adapted learning path and in the assessment phase. The purpose of this paper is to provide an initial discussion of the role of ontology in the context of e‐learning. It seeks to discuss the improvements related to the introduction of ontology formalism in the e‐learning field and to show a novel algorithm for ontology building through the use of Bayesian networks. Finally, it aims to illustrate its application in the assessment process and some experimental results.Design/methodology/approachA novel method for learning ontology for e‐learning is illustrated, using an approach based on Bayesian networks. Thanks to their characteristics, these networks can be used to model and evaluate the conditional dependencies among the nodes of ontology on the basis of the data obtained from student tests. An experimental evaluation of the proposed method was performed using real student data.FindingsThe proposed method was integrated in a tool for the assessment of students during a learning process. This tool is based on the use of ontology and Bayesian network. In particular through the matching between ontology and Bayesian network, it was found that our tool allows an effective tutoring and a better adaptation of learning process to demands of students. The assessment based on Bayesian approach allows a deeper analysis of student's knowledge.Research limitations/implicationsThe proposed approach needs more experimentation with other domains and with more complex ontology.Originality/valueThis paper provides an initial discussion of the role of ontology in the context of e‐learning. The improvements related to the introduction of ontology formalism in the e‐learning field are discussed and a novel algorithm for ontology building through the use of Bayesian Networks is showed. Finally, its application in the assessment process and some experimental results are illustrated.

Computer-based educational systems are becoming increasingly important in all levels and types of education. Intelligent Educational Systems (IESs) are computer-based educational systems encompassing Artificial Intelligence schemes to increase the effectiveness of the learning process. In this paper, we survey patents regarding IESs. As IESs we consider either Intelligent Tutoring Systems (ITSs) or Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent techniques. We first present indicative functions implemented in the three basic components of an IES: the domain knowledge, the user (or student) modeling unit and the pedagogical module. Afterwards, we focus on representative patents concerning IESs and present the corresponding work. At the end, we outline current and future developments regarding IESs. Keywords: Intelligent tutoring systems, adaptive educational hypermedia systems, intelligent educational systems, artificial intelligence in education, intelligent E-learning systems, intelligent computer-based training systems, artificial intelligence in Elearning

This article presents a case study of ontology-driven development of intelligent educational systems. Following a review of literature related to ontology development, ontology-driven software development, and traditional software engineering, we developed an ontology-driven software development methodology appropriate for intelligent ontology-driven systems which have ontologies as key execution components, such as e-Advisor, and which is biased toward an integration of incremental and iterative ontology development and downstream Model Driven Architecture for development of software components.

Much of the research on the use of computers in education either looks at the computer as a cognitive tool or as a catalyst for change aimed at fostering students' learning and changing teachers' actual routines. However, neither of these perspectives gives much consideration to the teaching framework in which the computer is actually used. In order to address this issue, a case study was designed to explore teachers' concepts and beliefs about computers. In this case study two teachers were involved in a software development process, and observation of their discussions during this process was used as a technique to uncover the teachers' beliefs about using computers. The case study led to a model of how teachers use computers in classroom teaching. This model conceptualizes the computer as a teaching resource that helps teachers to develop their teaching strategy, replacing the teachers in their role of managing students' rehearsal of materials and serving as a classroom management tool. This model of using computers demonstrates significant links between teachers' teaching strategies and the use of computers in education and thereby provides a support for a view of computers as professional tools for educators.

Computer science learning and use of computers in education are activities which differ in nature and tradition. Informatics involves learning informatics concepts, methodologies and the use of computers, while the use of computers in education consists of exploiting the computer at its best as an educational agent to improve the teaching-learning process in any discipline.

AAHE-ERIC/Higher Education Research ReportVolume 10, Issue 6 p. 10-14 Article The growing use of computers in education First published: June 1981 https://doi.org/10.1002/aehe.3640100606AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume10, Issue6June 1981Pages 10-14 RelatedInformation

Staff development as a condition for computer integration

In connection with the transition to a knowledge-based economy, at a time when a key factor in the development of society is the accumulated human knowledge and skills, as well as the availability of a wide range of users, intelligent systems are becoming very popular. Accordingly, the demand of the ergonomic and effective means of designing this class system is growing as well. The most time-consuming and most important stage of intelligent system development is the formation of the system knowledge base which ultimately determines the efficiency and quality of the entire intelligent system. Knowledge representation and processing models and methods as well as the intelligent system development techniques operating on the basis of these methods and models have a crucial role in relation to this. The article explores the different aspects of intelligent collaborative educational systems, describes the overall structure of an intelligent collaborative educational system and reflects the different steps of development the system.

This paper is focused on the use of Knowledge Management techniques to develop environments of Web-Based Education to leverage the individual learning process for learners. Evolving use of Knowledge Management plays an important role to enhance problem solving skills. Recently, innovative approaches for integrating Knowledge Management into practical teaching activities have been ignored. The knowledge is defined as combination and organization of data and information in given context, and Knowledge Management provides acquire, storage, sharing, application and creation of knowledge objects. A new Architecture for the development of adaptive and intelligent Web-Based Education systems is presented. This architecture is based on a Learning Model for Personalized Knowledge Management.

In the context of the development of intelligent training and educational systems, there is a need to process streaming data while meeting strict quality of service requirements, such as memory consumption, prediction accuracy, response time in the face of dynamic data changes, and others. The challenge lies in the fact that traditional data processing methods often fail to provide the required quality under limited resources. Aim of article is to develop a mathematical model for streaming data processing in an intelligent educational system and conduct an experimental evaluation of the effectiveness of the software components developed based on the model within the specified quality of service constraints. A mathematical model of data streaming processing in an intelligent educational system based on lambda calculus and corresponding formalizations is described. Based on the mathematical model, a modification of the incremental algorithm is developed.

Introduction From 1986 to 1988, Regional Centre for Education in Science and Mathematics (RECSAM) at Penang organised the Computers in Education Project with financial support from the Australian Government. The aim of the Project was to provide information on the use of computers in education among SEAMEO countries. Such information, it was thought, could be useful for educators who needed to formulate sound policies regarding the introduction, management, and use of computers in schools and the roles of computers in education in general. Six countries took part in this project including a team from Singapore.

Towards a Design of an Intelligent Educational System