Down the rabbit hole: Professional identities, professional learning, and change in one Australian school

This study takes researcher and reader down the rabbit hole of story with its unique approach to the phenomena of professional identity, professional learning, and school change. It examines the perspectives of 14 educators: a range of teachers and leaders in one independent Australian school and in the context of a teacher growth intervention. Set against the backdrop of the global push for teacher quality, and consequent worldwide initiatives in the arenas of teacher professional learning and school change, the study generates context-specific connections between lived critical moments of identity formation, learning, and leading. A bricolaged paradigmatic stance weaves together a social constructionist, phenomenological approach to narrative inquiry. Data were generated primarily from individual narrative-eliciting interviews, of the researcher, two teachers, and 11 school leaders. Extended literary metaphor and known literary characters operate as a symbolic and structural frame. Alice, the White Rabbit, and the Cheshire Cat, from Lewis Carroll’s Alice’s Adventures in Wonderland, are analytical tools for the presentation and analysis of the perspectives of researcher, teacher, and leader participants. While the study set out to explore the ways in which educators’ experiences of professional learning (trans)form their senses of professional identity, it found that it is not just professional learning, but epiphanic life experiences, which shape professional selves and practices. School context, and the alignment of the individual with the collective, emerged as key factors for individual and school change. Transformation of educators’ identities and practices was evident in environments which were supportive, challenging, and growth focused, rather than evaluation driven. Identity formation, individual professional growth, and collective school change were revealed to be unpredictable, fluid processes in which small, unexpected moments can have far-reaching effects. The findings have implications for the theorisation of identities, and the research and implementation of professional learning and school change

Automated code compliance checking in the construction domain using semantic natural language processing and logic-based reasoning

Construction projects must comply with various regulations. The manual process of checking the compliance with regulations is costly, time consuming, and error prone. With the advancement in computing technology, there have been many research efforts in automating the compliance checking process, and many software development efforts led by industry bodies/associations, software companies, and/or government organizations to develop automated compliance checking (ACC) systems. However, two main gaps in the existing ACC efforts are: (1) manual effort is needed for extracting requirements from regulatory documents and encoding these requirements in a computer-processable rule format; and (2) there is a lack of a semantic representation for supporting automated compliance reasoning that is non-proprietary, non-hidden, and user-understandable and testable. To address these gaps, this thesis proposes a new ACC method that: (1) utilizes semantic natural language processing (NLP) techniques to automatically extract regulatory information from building codes and design information from building information models (BIMs); and (2) utilizes a semantic logic-based representation to represent and reason about the extracted regulatory information and design information for compliance checking. The proposed method is composed of four main methods/algorithms that are combined in one computational framework: (1) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically extract regulatory information from building codes and represent the extracted information into semantic tuples, (2) a semantic, rule-based method and algorithm that leverage NLP techniques to automatically transform the extracted regulatory information into logic rules to prepare for automated reasoning, (3) a semantic, rule-based information extraction and information transformation method and algorithm to automatically extract design information from BIMs and transform the extracted information into logic facts to prepare for automated reasoning, and (4) a logic-based information representation and compliance reasoning schema to represent regulatory and design information for enabling the automated compliance reasoning process. To test the proposed method, a building information model test case was developed based on the Duplex Apartment Project from buildingSMARTalliance of the National Institute of Building Sciences. The test case was checked for compliance with a randomly selected chapter, Chapter 19, of the International Building Code 2009. Comparing to a manually developed gold standard, 87.6% precision and 98.7% recall in noncompliance detection were achieved, on the testing data