Abstract
Distance learning through the Internet is changing educational paradigms. Learning approaches, teaching methods, students’ expectations with instructional activities, and financial expectations are issues that challenge professionals and educational institutions (O´Donoghue, Singh & Dorward, 2001; Parikh & Verma, 2002). The growing availability of Internet access at working places and residences, in addition to a global market where education is a competitive advantage, are reasons for keeping the growth of investments in information technology for distance education. The distance education commercial arena currently involves universities, governments, and general educational institutions. Individuals and companies keep investing in educational programs for professional qualification, or even for keeping employees up to date with new technologies and market opportunities.
TopIntroduction
Distance learning through the Internet is changing educational paradigms. Learning approaches, teaching methods, students’ expectations with instructional activities, and financial expectations are issues that challenge professionals and educational institutions (O´Donoghue, Singh & Dorward, 2001; Parikh & Verma, 2002). The growing availability of Internet access at working places and residences, in addition to a global market where education is a competitive advantage, are reasons for keeping the growth of investments in information technology for distance education. The distance education commercial arena currently involves universities, governments, and general educational institutions. Individuals and companies keep investing in educational programs for professional qualification, or even for keeping employees up to date with new technologies and market opportunities.
The Internet can offer the learning process a variety of benefits (Fuks, Gerosa & Lucena, 2002), and there are several aspects pointing to the advantages of Internet education for individuals and enterprise (Hasebrook, 1999). Wilson (2002) argues that distance learning integrates technology, connectivity, curricular content, and human resources. According to Eastmond (1998), distance education is becoming widely accepted as means for higher education to provide broader access and achieve cost efficiencies while maintaining quality programs, while the overwhelming reason cited by students is convenience (Guernsey, 1998).
However, there are also barriers to online instruction (Meyen & Yang, 2003). Barriers related to the technical aspects of online instruction may have changed, while some related to attitudes, policies, and resources may still persist. Cravotta (2003) argues that the story of distance learning is not that it can do everything or that it is problem free. In reality, it brings a new set of problems to the learning table. However, it enables us to do much more than we could before. Students also identified the lack of face-to-face interaction as a drawback to the online environment, as reported by Sullivan (2001).
The number of research projects and publications reporting experiences with distance learning and education has also been growing. It is an interdisciplinary matter coping with investigation in areas such as information technology, telecommunications for exchanging data, educational approaches, instructional techniques, and learning preferences. In this contribution, the focus is on the relationship between online learning and artificial intelligence (AI) methods and techniques. We review some of the related literature and report on our own experience in this context.
Key Terms in this Chapter
Agent: A convenient metaphor for building software to interact with the range and diversity of online resources is that of an agent. An agent is a program that performs some task on your behalf. You expect an agent to act even if all the details are not specified or if the situation changes. You expect an agent to communicate effectively with other agents. Using agents adds a layer of abstraction that localizes decisions about dealing with local peculiarities of format, knowledge conventions, and so forth, and thus helps to understand and manage complexity.
Neural Network: An empirical learning program is one capable of learning from examples by a process of generalization. Empirical learning corresponds to giving a person a lot of examples without any explanation of why the examples are members of a particular class. Empirical learning systems inductively generalize specific examples. Artificial neural networks are a particular method for empirical learning. They have proven to be equal, or superior, to other empirical learning systems over a wide range of domains, when evaluated in terms of their generalization ability.
Web Mining: Data mining is a multidisciplinary field that supports knowledge workers who try to extract information in our “data rich, information poor” environment. Its name stems from the idea of mining knowledge from large amounts of data. Any method used to extract patterns from a given data source is considered to be a data mining technique. When the data resides on the Web, the process is that of Web mining.
Artificial Intelligence (AI): Refers to the capability of a machine, and more specifically a computer or computer program, to perform functions that are normally associated with human intelligence, such as reasoning and optimization through experience. AI is the branch of computer science that attempts to approximate the results of human reasoning by organizing and manipulating factual and heuristic knowledge.
E-Learning: A valuable extension of the distance education paraphernalia, enabled by the new information and communication technologies. E-learning is often described as the use of network technology, namely the Internet, to design, deliver, select, administer, and extend learning.
Machine Learning: A sub-field of artificial intelligence. The idea is that a computing system could perhaps learn to solve problems in much the same way that humans do, that is to say, by example. A program is needed which learns the concepts of a domain under varying degrees of supervision from a human teacher. In one approach, the teacher presents the program with a set of examples of a concept, and the program’s task is to identify what collection of attributes and values defines the concept.
Multi-Agent Systems: Systems composed of multiple interacting agents, where each agent is a coarse-grained computational system in its own right. The hypothesis of multi-agent systems is creating a system that interconnects separately developed agents, thus enabling the ensemble to function beyond the capabilities of any singular agent in the set-up.