Integration of Abductive and Deductive Inference Diagnosis Model and Its Application in Intelligent Tutoring System

This dissertation presents a diagnosis model, Integration of Abductive and Deductive Inference diagnosis model (IADI), in the light of the cognitive processes of human diagnosticians. In contrast with other diagnosis models, that are based on enumerating, tracking and classifying approaches, the IADI diagnosis model relies on different inferences to solve the diagnosis problems. Studies on a human diagnosticians\u27 process show that a diagnosis process actually is a hypothesizing process followed by a verification process. The IADI diagnosis model integrates abduction and deduction to simulate these processes. The abductive inference captures the plausible features of this hypothesizing process while the deductive inference presents the nature of the verification process. The IADI diagnosis model combines the two inference mechanisms with a structure analysis to form the three steps of diagnosis, mistake detection by structure analysis, misconception hypothesizing by abductive inference, and misconception verification by deductive inference. An intelligent tutoring system, Recursive Programming Tutor (RPT), has been designed and developed to teach students the basic concepts of recursive programming. The RPT prototype illustrates the basic features of the IADI diagnosis approach, and also shows a hypertext-based tutoring environment and the tutoring strategies, such as concentrating diagnosis on the key steps of problem solving, organizing explanations by design plans and incorporating the process of tutoring into diagnosis

Flexible virtual learning environments: a schema-driven approach using sematic web concepts

Flexible e-Iearning refers to an intelligent educational mechanism that focuses on simulating and improving traditional education as far as possible on the Web by integrating various electronic approaches, technologies, and equipment. This mechanism aims to promote the personalized development and management of e-learning Web services and applications. The main value of this method is that it provides high-powered individualization in pedagogy for students and staff.Here, the thesis mainly studied three problems in meeting the practical requirements of users in education. The first question is how a range of teaching styles (e.g. command and guided discovery) can be supported. The second one is how varieties of instructional processes can be authored. The third question is how these processes can be controlled by learners and educators in terms of their personalized needs during the execution of instruction.In this research, through investigating the existing e-Iearning approaches and technologies, the main technical problems of current virtual learning environments (VLEs) were analyzed. Next, by using the Semantic Web concepts as well as relevant standards, a schema-driven approach was created. This method can support users' individualized operations in the Web-based education. Then, a flexible e-learning system based on the approach was designed and implemented to map a range of extensive didactic paradigms. Finally, a case study was completed to evaluate the research results. The main findings of the assessment were that the flexible VLE implemented a range of teaching styles and the personalized creation and control of educational processes

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers