Developmental patterns of individiuals within a sensory-based learning environment in art education : insights emerging from a case study /

My research permitted me to reexamine my recent evaluations of the Leaf Project given to the Foundation Year students during the fall semester of 1997. My personal description of the drawing curriculum formed part of the matrix of the Foundation Core Studies at the Ontario College of Art and Design. Research was based on the random selection of 1 8 students distributed over six of my teaching groups. The entire process included a representation of all grade levels. The intent of the research was to provide a pattern of alternative insights that could provide a more meaningful method of evaluation for visual learners in an art education setting. Visual methods of learning are indeed complex and involve the interplay of many sensory modalities of input. Using a qualitative method of research analysis, a series of queries were proposed into a structured matrix grid for seeking out possible and emerging patterns of learning. The grid provided for interrelated visual and linguistic analysis with emphasis in reflection and interconnectedness. Sensory-based modes of learning are currently being studied and discussed amongst educators as alternative approaches to learning. As patterns emerged from the research, it became apparent that a paradigm for evaluation would have to be a progressive profile of the learning that would take into account many of the different and evolving learning processes of the individual. A broader review of the student's entire development within the Foundation Year Program would have to have a shared evaluation through a cross section of representative faculty in the program. The results from the research were never intended to be conclusive. We realized from the start that sensory-based learning is a difficult process to evaluate from traditional standards used in education. The potential of such a process of inquiry permits the researcher to ask for a set of queries that might provide for a deeper form of evaluation unique to the students and their related learning environment. Only in this context can qualitative methods be used to profile their learning experiences in an expressive and meaningful manner.

Improving the Scalability of Reduct Determination in Rough Sets

Rough Set Data Analysis (RSDA) is a non-invasive data analysis approach that solely relies on the data to find patterns and decision rules. Despite its noninvasive approach and ability to generate human readable rules, classical RSDA has not been successfully used in commercial data mining and rule generating engines. The reason is its scalability. Classical RSDA slows down a great deal with the larger data sets and takes much longer times to generate the rules. This research is aimed to address the issue of scalability in rough sets by improving the performance of the attribute reduction step of the classical RSDA - which is the root cause of its slow performance. We propose to move the entire attribute reduction process into the database. We defined a new schema to store the initial data set. We then defined SOL queries on this new schema to find the attribute reducts correctly and faster than the traditional RSDA approach. We tested our technique on two typical data sets and compared our results with the traditional RSDA approach for attribute reduction. In the end we also highlighted some of the issues with our proposed approach which could lead to future research.