Static and Dynamic Integrated Expert Systems: State of the Art, Problems and Trends

Systemized analysis of trends towards integration and hybridization in contemporary expert systems is conducted, and a particular class of applied expert systems, integrated expert systems, is considered. For this purpose, terminology, classification, and models, proposed by the author, are employed. As examples of integrated expert systems, Russian systems designed in this field and available to the majority of specialists are analyzed

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

The Effect of Aleks on Students\u27 Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessments

For many courses, mathematics included, there is an associated interactive e-learning system that provides assessment and tutoring. Some of these systems are classified as Intelligent Tutoring Systems. MyMathLab, Mathzone, and Assessment of LEarning in Knowledge Space (ALEKS) are just a few of the interactive e-learning systems in mathematics. In ALEKS, assessment and tutoring are based on the Knowledge Space Theory. Previous studies in a traditional learning environment have shown ALEKS users to perform equally or better in mathematics achievement than the group who did not use ALEKS. The purpose of this research was to investigate the effect of ALEKS on students’ achievement in mathematics in an online learning environment and to determine the cognitive complexity of mathematical tasks enacted by ALEKS’s initial (pretest) and final (posttest) assessments. The targeted population for this study was undergraduate students in College Mathematics I, in an online course at a private university in the southwestern United States. The study used a quasi-experimental One-Group non-randomized pretest and posttest design. Five methods of analysis and one model were used in analyzing data: t-test, correctional analysis, simple and multiple regression analysis, Cronbach’s Alpha reliability test and Webb’s depth of knowledge model. A t-test showed a difference between the pretest and posttest reports, meaning ALEKS had a significant effect on students’ mathematics achievement. The correlation analysis showed a significant positive linear relationship between the concept mastery reports and the formative and summative assessments reports meaning there is a direct relationship between the ALEKS concept mastery and the assessments. The regression equation showed a better model for predicting mathematics achievement with ALEKS when the time spent learning in ALEKS and the concept mastery scores are used as part of the model. According to Webb’s depth of knowledge model, the cognitive complexity of the pretest and posttest question items used by ALEKS were as follows: 50.5% required application of skills and concepts, 37.1% required recall of information, and 12.4% required strategic thinking: None of the questions items required extended thinking or complex reasoning, implying ALEKS is appropriate for skills and concepts building at this level of mathematics

Personalisation and recommender systems in digital libraries

Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field

Future challenges in intelligent tutoring systems: a framework

Intelligent Tutoring Systems (ITS) provide the benefits of one-on-one instruction in an automatic way and cost effectively, keeping in mind their multidisciplinary nature. The challenge remains on transporting to com-puters the expertise, skills and mode of action of the human tutor, overcoming space, time, socio-economical and environmental restrictions. ITS appear as a form of deployment of this issue and have been object of an increasing research. This paper aims to establish some characteristics, properties and functions that an ITS should provide, and the possible contributions that the different fields of research can make, proposing a multi-domain and multidisciplinary framework to address the research in this field. The framework incorpo-rates a knowledge base where data and knowledge related to the problem are maintained and a model base re-lated to student, teaching and environmental issues together with pedagogical perspectives

Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective

Developing the didactic operations for intelligent tutoring systems: A synthesis of artificial intelligence and hypertext.

This thesis is concerned with Intelligent Tutoring Systems. It investigates the architecture of an Intelligent Knowledge Based Tutoring System in terms of three knowledge models: that of the domain, the student and the tutor, and examines the interrelatedness and interconnectedness of these three knowledge models. Existing Knowledge Based Tutoring Systems are reviewed, and the relationship between their behaviour and architecture is analysed by evaluating them against Wenger's model of a didactic operation. Two such systems, PROUST, a tutoring system for Pascal program debugging skills, and micro-SEARCH, a tutoring system for mathematical transformations skills, are used in the study. This evaluation serves two purposes: to unravel the requirements for interrelatedness and interconnectedness between the three knowledge models in order to develop a true Knowledge Based Tutoring System with a full-scale didactic operation, and to uncover the limitations of the current generation of Knowledge Based Tutoring Systems and how they fail to fully encompass these requirements. On this basis the thesis goes on to propose a hybrid model made up of Artificial Intelligence and Hypertext concepts which helps to overcome the limitations of existing Knowledge Based Tutoring Systems. This model in particular addresses the requirements for the development of an Intelligent Tutoring Systems with a full-scale didactic operation. The model integrates Hypertext's explicit information nodes and linking properties with Artificial Intelligence's logical inferencing on knowledge representation schemes. The thesis finally shows how to use this model to design a generic Intelligent Tutoring System that supports a full-scale didactic operation

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

A Hybrid Approach for Supporting Adaptivity in E-learning Environments

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the ECA model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given Learning Management System based on learners’ learning styles. Design/methodology/approach: This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings: The system prototype supports real-time adaptivity in any given Learning Management System based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the Learning Management System. Moreover, it can support adaptivity at both course and learner levels. Research limitations/implications: Practical implications: Social implications: Originality/value: To the best of our knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity

Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective