The development of science concepts emergent from science museum and post-visit activity experiences : students' construction of knowledge

This research investigated students' construction of knowledge about the topics of magnetism and electricity emergent from a visit to an interactive science centre and subsequent classroom-based activities linked to the science centre exhibits. The significance of this study is that it analyses critically an aspect of school visits to informal learning centres that has been neglected by researchers in the past, namely the influence of post-visit activities in the classroom on subsequent learning and knowledge construction. Employing an interpretive methodology, the study focused on three areas of endeavour. Firstly, the establishment of a set of principles for the development of post-visit activities, from a constructivist framework, to facilitate students' learning of science. Secondly, to describe and interpret students' scientific understandings : prior t o a visit t o a science museum; following a visit t o a science museum; and following post-visit activities that were related to their museum experiences. Finally, to describe and interpret the ways in which students constructed their understandings: prior to a visit to a science museum; following a visit to a science museum; and following post-visit activities directly related to their museum experiences. The study was designed and implemented in three stages: 1) identification and establishment of the principles for design and evaluation of post-visit activities; 2) a pilot study of specific post-visit activities and data gathering strategies related to student construction of knowledge; and 3) interpretation of students' construction of knowledge from a visit to a science museum and subsequent completion of post-visit activities, which constituted the main study. Twelve students were selected from a year 7 class to participate in the study. This study provides evidence that the series of post-visit activities, related to the museum experiences, resulted in students constructing and reconstructing their personal knowledge of science concepts and principles represented in the science museum exhibits, sometimes towards the accepted scientific understanding and sometimes in different and surprising ways. Findings demonstrate the interrelationships between learning that occurs at school, at home and in informal learning settings. The study also underscores for teachers and staff of science museums and similar centres the importance of planning pre- and post-visit activities, not only to support the development of scientific conceptions, but also to detect and respond to alternative conceptions that may be produced or strengthened during a visit to an informal learning centre. Consistent with contemporary views of constructivism, the study strongly supports the views that : 1) knowledge is uniquely structured by the individual; 2) the processes of knowledge construction are gradual, incremental, and assimilative in nature; 3) changes in conceptual understanding are can be interpreted in the light of prior knowledge and understanding; and 4) knowledge and understanding develop idiosyncratically, progressing and sometimes appearing to regress when compared with contemporary science. This study has implications for teachers, students, museum educators, and the science education community given the lack of research into the processes of knowledge construction in informal contexts and the roles that post-visit activities play in the overall process of learning.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.