Active learning has a long heritage in Geography and allied subjects, and many claims are made about its benefits. This article attempts a long run assessment of these benefits, drawing on a survey of 180 respondents who graduated from a capstone Geography course over a 15-year period. The course focuses on community-based research and depends on the development and curation of an ecosystem of students, academic staff mentors, community partners and university managers. It is designed to enable students to make a difference, whilst gaining experience of working in groups, managing time and expectations, and workplace preparation. The paper assesses impacts on respondents in the long term, rather than the more usual results of course surveys conducted at the time. The context of the course is notable, as it began one year before the Canterbury earthquake sequence, which sharpened both community need and opportunity for such learning methods. The insights gained from developing and running the course over the period of time in question are outlined.
The quality of public space is vital to livable cities. Yet livable cities also require empowered communities. This thesis asks: how is the landscape architect’s design expertise expressed as part of the public participation process, what are the key features of design expertise that lead to an effective design-based participation process and how does quality in the participation process relate to the quality of design outcomes? A theoretical framework is developed from which to clarify the relationship between decision-making processes in design and public participation. Insights from design theory are combined with the findings of key informant interviews with New Zealand and Northern Europe design experts, and with landscape architects, community and Council staff working in post-earthquake Ōtautahi/Christchurch, Aotearoa/New Zealand. Results of a case study of Albion Square in Ōhinehou/Lyttelton reveal that the designer’s interactions with the public play a critical role in shaping elegant design outcomes in public space design. Four key insights reveal that participatory design processes in New Zealand need to be reconsidered in order to enable landscape architects to work more closely with communities in mutual learning, rather than the currently limiting technical problem solving process. Institutional, professional and theoretical implications are drawn from the findings.
Following the 2010-2011 earthquakes in Canterbury, New Zealand, the University of Canterbury (UC) was faced with the need to respond to major challenges in its teaching and learning environment. With the recognition of education as a key component to the recovery of the Canterbury region, UC developed a plan for the transformation and renewal of the campus. Central to this renewal is human capital – graduates who are distinctly resilient and broadly skilled, owing in part to their living and rebuilding through a disaster. Six desired graduate attributes have been articulated through this process: knowledge and skills of a recognized subject, critical thinking skills, the ability to interpret information from a range of sources, the ability to self-direct learning, cultural competence, and the recognition of global connections through social, ethical, and environmental values. All of these attributes may readily be identified in undergraduate geoscience field education and graduate field-based studies, and this is particularly important to highlight in a climate where the logistical and financial requirements of fieldwork are becoming a barrier to its inclusion in undergraduate curricula. Fieldwork develops discipline-specific knowledge and skills and fosters independent and critical thought. It encourages students to recognize and elaborate upon relevant information, plan ways to solve complicated problems, execute and re-evaluate these plans. These decisions are largely made by the learners, who often direct their own field experience. The latter two key graduate attributes, cultural competence and global recognition of socio-environmental values, have been explicitly addressed in field education elsewhere and there is potential to do so within the New Zealand context. These concepts are inherent to the sense of place of geoscience undergraduates and are particularly important when the field experience is viewed through the lens of landscape heritage. This work highlights the need to understand how geoscience students interact with field places, with unique implications for their cultural and socio-environmental awareness as global citizens, as well as the influence that field pedagogy has on these factors.
High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination of whether a ground motion record is high-quality is poorly handled by automation with mathematical functions and can become prohibitive if done manually. Machine learning applications are well-suited to this problem, and a previous feed-forward neural network was developed (Bellagamba et al. 2019) to determine high-quality records from small crustal events in the Canterbury and Wellington regions for simulation validation. This prior work was however limited by the omission of moderate-to-large magnitude events and those from other tectonic environments, as well as a lack of explicit determination of the minimum usable frequency of the ground motion. To address these shortcomings, an updated neural network was developed to predict the quality of ground motion records for all magnitudes and all tectonic sources—active shallow crustal, subduction intraslab, and subduction interface—in New Zealand. The predictive performance of the previous feed-forward neural network was matched by the neural network in the domain of small crustal records, and this level of predictive performance is now extended to all source magnitudes and types in New Zealand making the neural network applicable to global ground motion databases. Furthermore, the neural network provides quality and minimum usable frequency predictions for each of the three orthogonal components of a record which may then be mapped into a binary quality decision or otherwise applied as desired. This framework provides flexibility for the end user to predict high-quality records with various acceptability thresholds allowing for this neural network to be used in a range of applications.
