A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Accounting for student/educator diversity: resurrecting coaction theory

This chapter challenges current approaches to defining the context and process of entrepreneurship education. In modeling our classrooms as a microcosm of the world our current and future students will enter, this chapter brings to life (and celebrates) the everpresent diversity found within. The chapter attempts to make an important (and unique) contribution to the field of enterprise education by illustrating how we can determine the success of (1) our efforts as educators, (2) our students, and (3) our various teaching methods. The chapter is based on two specific premises, the most fundamental being the assertion that the performance of student, educator and institution can only be accounted for by accepting the nature of the dialogic relationship between the student and educator and between the educator and institution. A second premise is that at any moment in time, the educator can be assessed as being either efficient or inefficient, due to the presence of observable heterogeneity in the learning environment that produces differential learning outcomes. This chapter claims that understanding and appreciating the nature of heterogeneity in our classrooms provides an avenue for improvement in all facets of learning and teaching. To explain this claim, Haskell’s (1949) theory of coaction is resurrected to provide a lens through which all manner of interaction occurring within all forms of educational contexts can be explained. Haskell (1949) asserted that coaction theory had three salient features.

Organizational affordances: A structuration theory approach to affordances

Affordance is an important concept in the field of human–computer interaction. There are various interpretations of affordances, often extending the original notion of James J. Gibson. Often the treatment of affordances in the current human–computer interaction literature has been a one-to-one relationship between a user and an artefact. We believe that the social and cultural contexts within which an artefact is situated affect the way in which the artefact is used and the notion of affordance needs to be seen as a dynamic, always emerging relationship between people and their environment. Using a Structuration Theory approach, we conceptualize the notion of affordance at a much broader level, encompassing social and cultural aspects. We suggest that affordances should be seen at three levels: single user, organizational (or work group) and societal. Focusing on the organizational level affordances, we provide details of several important factors that affect the emergence of affordances. - This article provides a new perspective on the discourse of affordance with the use of Structuration Theory. - It shows how affordance can be understood as ‘use’ in situated practices (i.e. ‘technology-in-practice’) - The Structuration Theory approach to affordances is showcased using two case studies.

Applying urban morphology theory to design practice

The aim of this paper is to investigate the relationship between research and design through an applied example of how architects can approach the design for a building guided by data derived from morphological research. An Expert Focus Group consisting of nine leading architects and urban designers from Brisbane, Australia was given the task of testing this proposition. Analysis of the workshop outcomes are examined, and the design drawings of each participant are assessed to determine the relative congruence of the design proposals within the morphological commodity of the specific context. In addition, qualitative data, captured through verbal and written feedback is assessed highlighting the participants' observations and experiences of the workshop. This paper makes a contribution to the current debate within the field on the opportunities of integrating research with practice.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

The relationship between class participation and law students' learning, engagement and stress: Do demographics matter?

Student participation in the classroom has long been regarded as an important means of increasing student engagement and enhancing learning outcomes by promoting active learning. However, the approach to class participation common in U.S. law schools, commonly referred to as the Socratic method, has been criticised for its negative impacts on student wellbeing. A multiplicity of American studies have identified that participating in law class discussions can be alienating, intimidating and stressful for some law students, and may be especially so for women, and students from minority backgrounds. Using data from the Law School Student Assessment Survey (LSSAS), conducted at UNSW Law School in 2012, this Chapter provides preliminary insights into whether assessable class participation (ACP) at an Australian law school is similarly alienating and stressful for students, including the groups identified in the American literature. In addition, we compare the responses of undergraduate Bachelor of Laws (LLB) and graduate Juris Doctor (JD) students. The LSSAS findings indicate that most respondents recognise the potential learning and social benefits associated with class participation in legal education, but remain divided over their willingness to participate. Further, in alignment with general trends identified in American studies, LLB students, women, international students, and non-native English speakers perceive they contribute less frequently to class discussions than JD students, males, domestic students, and native English speakers, respectively. Importantly, the LSSAS indicates students are more likely to be anxious about contributing to class discussions if they are LLB students (compared to their JD counterparts), and if English is not their first language (compared to native English speakers). There were no significant differences in students’ self-reported anxiety levels based on gender, which diverges from the findings of American research.

Innovative field options

"A practical guide for educators and managers involved in supervising field education. Drawing on the experience of academics, clinicians and educators from Australia, New Zealand, Canada and the UK, the collection explores how to make the most of fieldwork experience."--Libraries Australia