The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

Adaptive Intelligent Tutoring System for learning Computer Theory

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned

The Tutor's Role

This chapter addresses three questions about being an effective online tutor: 1. Why do we still think that online tutoring can principally draw its basis from face-to-face group processes and dynamics or traditional pedagogy? 2. Does the literature tell us anything more than we would make as an intelligent guess? 3. Do we really know what an ‘effective’ online tutor would be doing? The OTiS participants have gone some way to answering these questions, through the presentation and discussion of their own online tutoring experiences. Literature in this area is still limited, and suffers from the need for timeliness of publication to be useful. Intelligent guesses are all very well, but much better as a source of information for online tutors are the reflections and documented experiences of practitioners. These experiences reveal that face-to-face pedagogy has some elements to offer the online tutor, but that there are key differences and there is a need to examine the processes and dynamics of online learning to inform online tutoring