title: Developing an individual and collective self-efficacy scale for the teaching of writing in high schools

authors: Locke, Terry

abstract: The study reported on here focuses on self-efficacy in relation to high-school teachers’ teaching of writing. 140 New Zealand teachers from four schools completed a teacher-of- writing self-efficacy scale (TWSES) based on a rhetorical model of the writing process and incorporating five hypothesized dimensions. An initial principal components analysis was undertaken on 25 individual self-efficacy items to investigate the dimensionality of the data and the extent to which it reflected the dimensions hypothesized. A two-component solution emerged, termed “pre-writing instructional strategies” (accounting for 52% of total variance) and “compositional strategy demonstration” (7% of variance). Further principal components analyses conducted on groups of items deemed to be thematically coherent, that loaded on each component, confirmed that the data set for each group, treated sepa- rately to any other items, was approximately uni-dimensional. Measurement scales were calibrated to each group of items, and served as the dependent variables for comparisons of teachers’ self-efficacy in different subjects. Statistically significant variations occurred in the resultant scale locations for teachers of English, the humanities, science and mathe- matics. The study findings have implications for the teaching of writing as conceptualized in the secondary school, and indicate a value in viewing disciplinary literacies in rhetorical terms.

text: Developing an individual and collective self-efficacy scale for the teaching of writing in high schools The study reported on here focuses on self-efficacy in relation to high-school teachers’ teaching of writing. 140 New Zealand teachers from four schools completed a teacher-of- writing self-efficacy scale (TWSES) based on a rhetorical model of the writing process and incorporating five hypothesized dimensions. An initial principal components analysis was undertaken on 25 individual self-efficacy items to investigate the dimensionality of the data and the extent to which it reflected the dimensions hypothesized. A two-component solution emerged, termed “pre-writing instructional strategies” (accounting for 52% of total variance) and “compositional strategy demonstration” (7% of variance). Further principal components analyses conducted on groups of items deemed to be thematically coherent, that loaded on each component, confirmed that the data set for each group, treated sepa- rately to any other items, was approximately uni-dimensional. Measurement scales were calibrated to each group of items, and served as the dependent variables for comparisons of teachers’ self-efficacy in different subjects. Statistically significant variations occurred in the resultant scale locations for teachers of English, the humanities, science and mathe- matics. The study findings have implications for the teaching of writing as conceptualized in the secondary school, and indicate a value in viewing disciplinary literacies in rhetorical terms.

year: 2016

title: Embedding Java classes with code2vec: improvements from variable obfuscation

authors: Compton, Rhys

abstract: Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code.

text: Embedding Java classes with code2vec: improvements from variable obfuscation Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code.

year: 2020

title: Sustainable management, a sustainable ethic?

authors: Daya-Winterbottom, Trevor

abstract: New Zealand legislated for sustainability by enacting the Resource Management Act 1991 (RMA). The RMA received the Royal Assent on 22 July 1991 and came into force as law on 1 October 1991. It restated and reformed “the law relating to the use of land, air, and water”, and is the principal statute governing the New Zealand environment. 
Despite the rhetoric of sustainable management, environmental law and governance in New Zealand continue to grapple with persistent challenges in relation to protecting biodiversity on private land, maintaining freshwater quantity and quality, and recognising Maori interests in resource management. New strategies are however emerging for promoting environmental justice and using the law to advance sustainability. My paper will therefore explore these aspects of the New Zealand experiment in legislating for sustainable management.

text: Sustainable management, a sustainable ethic? New Zealand legislated for sustainability by enacting the Resource Management Act 1991 (RMA). The RMA received the Royal Assent on 22 July 1991 and came into force as law on 1 October 1991. It restated and reformed “the law relating to the use of land, air, and water”, and is the principal statute governing the New Zealand environment. 
Despite the rhetoric of sustainable management, environmental law and governance in New Zealand continue to grapple with persistent challenges in relation to protecting biodiversity on private land, maintaining freshwater quantity and quality, and recognising Maori interests in resource management. New strategies are however emerging for promoting environmental justice and using the law to advance sustainability. My paper will therefore explore these aspects of the New Zealand experiment in legislating for sustainable management.

year: 2012