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Teaching is a communication process in which a body of knowledge is delivered from an instructor to students (Gagne, 1985). This communication traditionally takes place in a classroom. With the proliferation of computer software and hardware at low costs and the ease of access to the World Wide Web, one should expect that the use of Internet and related information technologies will foster an alternative teaching environment. Previous works have reported on various Internet-based teaching aids such as using e-mail, posting information on a Web page and using the Internet to search for additional class materials. Integrating these teaching media in a larger framework of Distance Learning and Virtual Class (Hiltz, 1994), they would provide a synergistic effect in helping students acquire the desired body of knowledge efficiently. From this perspective, we discuss a framework for summative and formative evaluations of Internet-based teaching in higher education. The timely evaluation is necessary for the development and implementation of a new teaching/learning environment. It will assure that the technology meets the intended pedagogic goal of teaching by taking into account feedback from student-users.

One of the limitations of conventional expert systems and traditional machine induction methods in capturing human expertise is in their requirement of a large pool of structured samples from a multi-criteria decision problem domain. Then the experts may have difficulty in expressing explicitly the rules on how each decision was reached. To overcome these shortcomings, this paper reports on the design of an optimal knowledge base for machine induction with the integration of Artificial Neural Network (ANN) and Expert Systems (ES). In this framework, an orthogonal plan is used to define an optimal set of examples to be taken. Then holistic judgments of experts on these examples will provide a training set for an ANN to serve as an initial knowledge base for the integrated system. Any counter-examples in generalization over new cases will be added to the training set to retrain the network to enlarge its knowledge base.

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This study investigates to what extent student attitudes toward acceptance of online instruction and Distance Learning are affected by determinants such as demographics, learning environment, learning domains, delivery methods, and web-based instructional technology. Logistic Regression and Discriminant Analysis use statistically significant determinants to predict student preference on future online classes. Factor Analysis provides an exploratory model of online learning acceptance having three factors; namely, Communication/Feedback, Course Outcome, and Effort Required. Practical implications of findings and insights on field observations are offered. Overall, students agreed that they had learned sufficient knowledge from an online course. Students satisfied with their recent learning outcome tend to take more online courses in the future.