The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are EMYCIN -type expert system shells. The main argument of this thesis is that these shells make knowledge-based modelling unnecessarily difficult and that tools which exploit knowledge acquisition technologies empower learners to build better models. We describe how such tools can be designed. To evaluate their usability a model-building course was conducted in five secondary schools. During the course pupils built hundreds of models in a common range of domains. Some of the models were built with an EMYCIN -type shell whilst others were built with a variety of knowledge acquisition systems. The knowledge acquisition systems emerged as superior in important respects. We offer some explanations for these results and argue that although problems remain, such as in teacher education, design of classroom practice, and assessment of learning outcomes, it is clear that knowledge acquisition systems offer considerable potential to develop improved forms of educational knowledge-based modelling.

The concept of quality of life (QOL) has been addressed for decades. It was not until recent times when geographical information systems (GIS) have become available that a locale-specific approach could be enabled. Even then, analysis to date has been conducted mostly at the resolution of city or county level. The study presented describes an innovative methodology that may appraise QOL at finer resolutions, i.e. more localespecific. The new approach is called Spatial Appraisal and Valuation of Environment and Ecosystems, or SAVEE. This thesis research is a proof-of-concept study as the first account of the SAVEE methodology. It is to set the stage for future studies toward a more comprehensive framework. In this preliminary study of locale-specific QOL, the SAVEE methodology was used to illustrate the possibility of handling QOL factors in a dynamic manner. The assessment includes three major steps: 1) data preparation, 2) data conversion and normalization, and 3) combining contributions of factors being considered. In the first step, the geospatial data layer of a factor in consideration was input into GIS to plot a proximity map of the feature, e.g. parks or fire stations. In Step Two, each factor was first assigned a range of weight according to the location of a site on a proximity map in terms of the factor’s favorability-unfavorability. In the third step, weights from each factor were combined in a pair-wise manner, e.g. park and fire station proximities, or two factors at a time. The weight combining is done by deploying map algebra formula derived from the expert system algorithm EMYCIN. The computation was done iteratively until all factors were exhausted. The final results were coded as a gradient map of an integrated and locale-specific QOL index in the range of (-1, 1). In this preliminary study, the City of College Station, Texas was used as the study site. A set of factors and their respective ranges of weight were used in the study. By adjusting the incorporation of various factors and their ranges, a series of QOL maps for the city was generated. The resulting QOL maps indicate what factors and ranges may or may not have contributions toward a holistic overall picture of the QOL of a city in the locale-specific context. The SAVEE methodology proved to be successful in handling qualitative hedonic factors in a locale-specific quantitative manner through the GIS interface.