Can teacher collaboration overcome barriers to interdisciplinary learning in a disciplinary university? A case study using climate change

A teacher network was formed at an Australian university in order to better promote interdisciplinary student learning on the complex social-environmental problem of climate change. Rather than leaving it to students to piece together disciplinary responses, eight teaching academics collaborated on the task of exposing students to different types of knowledge in a way that was more than the summing of disciplinary parts. With a part-time network facilitator providing cohesion, network members were able to teach into each other’s classes, and share material and student activities across a range of units that included business, zoology, marine science, geography and education. Participants reported that the most positive aspects of the project were the collegiality and support for teaching innovation provided by peers. However, participants also reported being time-poor and overworked. Maintaining the collaboration beyond the initial one year project proved difficult because without funding for the network facilitator, participants were unable to dedicate the time required to meet and collaborate on shared activities. In order to strengthen teacher collaboration in a university whose administrative structures are predominantly discipline-based, there is need for recognition of the benefits of interdisciplinary learning to be matched by recognition of the need for financial and other resources to support collaborative teaching initiatives.

In pursuit of becoming a senior coach: The learning culture for Australian Football League coaches

- Background and Purpose Given the turbulent and highly contested environment in which professional coaches work, a prime concern to coach developers is how coaches learn their craft. Understanding the learning and development of senior coaches (SCs) and assistant coaches (ACs) in the Australian Football League (AFL – the peak organisation for Australian Rules Football) is important to better develop the next generation of performance coaches. Hence the focus of this research was to examine the learning of SC and AC in the AFL. Fundamental to this research was an understanding that the AFL and each club within the league be regarded as learning organisations and workplaces with their own learning cultures where learning takes place. The purpose of this paper was to examine the learning culture for AFL coaches. - Method Five SCs, 6 ACs, and 5 administrators (4 of whom were former coaches) at 11 of the 16 AFL clubs were recruited for the research project. First, demographic data were collected for each participant (e.g. age, playing and coaching experience, development and coach development activities). Second, all participants were involved in one semi-structured interview of between 45 and 90 minutes duration. An interpretative (hierarchical content) analysis of the interview data was conducted to identify key emergent themes. - Results Learning was central to AFL coaches becoming a SC. Nevertheless, coaches reported a sense of isolation and a lack of support in developing their craft within their particular learning culture. These coaches developed a unique dynamic social network (DSN) that involved episodic contact with a number of respected confidantes often from diverse fields (used here in the Bourdieuian sense) in developing their coaching craft. Although there were some opportunities in their workplace, much of their learning was unmediated by others, underscoring the importance of their agentic engagement in limited workplace affordances. - Conclusion The variety of people accessed for the purposes of learning (often beyond the immediate workplace) and the long time taken to establish networks of supporters meant that a new way of describing the social networks of AFL coaches was needed; DSN. However, despite the acknowledged utility of learning from others, all coaches reported some sense of isolation in their learning. The sense of isolation brought about by professional volatility in high-performance Australian Football offers an alternative view on Hodkinson, Biesta and James' attempt in overcoming dualisms in learning.

Journalism, media & communication network: Final report 2015

The JoMeC Network project had three key objectives. These were to: 1. Benchmark the pedagogical elements of journalism, media and communication (JoMeC) programs at Australian universities in order to develop a set of minimum academic standards, to be known as Threshold Learning Outcomes (TLOs), which would applicable to the disciplines of Journalism, Communication and/or Media Studies, and Public Relations; 2. Build a learning and teaching network of scholars across the JoMeC disciplines to support collaboration, develop leadership potential among educators, and progress shared priorities; 3. Create an online resources hub to support learning and teaching excellence and foster leadership in learning and teaching in the JoMeC disciplines. In order to benchmark the pedagogical elements of the JoMeC disciplines, the project started with a comprehensive review of the disciplinary settings of journalism, media and communication-related programs within Higher Education in Australia plus an analysis of capstone units (or subjects) offered in JoMeC-related degrees. This audit revealed a diversity of degree titles, disciplinary foci, projected career outcomes and pedagogical styles in the 36 universities that offered JoMeC-related degrees in 2012, highlighting the difficulties of classifying the JoMeC disciplines collectively or singularly. Instead of attempting to map all disciplines related to journalism, media and communication, the project team opted to create generalised TLOs for these fields, coupled with detailed TLOs for bachelor-level qualifications in three selected JoMeC disciplines: Journalism, Communication and/or Media Studies, and Public Relations. The initial review’s outcomes shaped the methodology that was used to develop the TLOs. Given the complexity of the JoMeC disciplines and the diversity of degrees across the network, the project team deployed an issue-framing process to create TLO statements. This involved several phases, including discussions with an issue-framing team (an advisory group of representatives from different disciplinary areas); research into accreditation requirements and industry-produced materials about employment expectations; evaluation of learning outcomes from universities across Australia; reviews of scholarly literature; as well as input from disciplinary leaders in a variety of forms. Draft TLOs were refined after further consultation with industry stakeholders and the academic community via email, telephone interviews, and meetings and public forums at conferences. This process was used to create a set of common TLOs for JoMeC disciplines in general and extended TLO statements for the specific disciplines of Journalism and Public Relations. A TLO statement for Communication and/or Media Studies remains in draft form. The Australian and New Zealand Communication Association (ANZCA) and Journalism Education and Research Association of Australian (JERAA) have agreed to host meetings to review, revise and further develop the TLOs. The aim is to support the JoMeC Network’s sustainability and the TLOs’ future development and use.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.