Meeting under the “Omei” Tree in the Torres Strait Islands : networks and funds of knowledge of mathematical ideas

This paper focuses on the turning point experiences that worked to transform the researcher during a preliminary consultation process to seek permission to conduct of a small pilot project on one Torres Strait Island. The project aimed to learn from parents how they support their children in their mathematics learning. Drawing on a community research design, a consultative meeting was held with one Torres Strait Islander community to discuss the possibility of piloting a small project that focused on working with parents and children to learn about early mathematics processes. Preliminary data indicated that parents use networks in their community. It highlighted the funds of knowledge of mathematics that exist in the community and which are used to teach their children. Such knowledges are situated within a community’s unique histories, culture and the voices of the people. “Omei” tree means the Tree of Wisdom in the Island community.

Family tree

Artist's Statement: These suspended shipping floats symbolise the artist's grandfather's home on Keriri (Hammond Island), where the trees are decorated with floats of all colours that have washed up on the beach. Across the entire Torres Strait, these floats, often from Asia, wash ashore and become decorative objects, strung from trees and hung from island shacks. Their vivid colours, and sometimes reflective glass surfaces, play against the lush tropical setting, while their re-use reflects the innovative character of island life. This arrangement of the floats represents the artist's family tree, which he has traced back six generations to Mer (Murray Island) and Keriri. The strings of orange floats represent his immediate family and direct lineage, each member of which is named on a float, with the totem of the family painted on the base. The remaining floats trace additional ancestry and spread further back through time and space, spanning the Torres Strait from west to east.

The potential availability of roosting sites for lesser short-tailed bats (Mystacina tuberculata) on Kapiti Island, New Zealand: Implications for a translocation

Lesser short-tailed bats (Mystacina tuberculata) have recently been translocated to Kapiti Island in an attempt to form a new population of this threatened species. However, the island's vegetation is regenerating, and there was doubt that the forests provided enough large trees with cavities for bats to roost in. This study measured the availability of tree-trunk cavities of the right size for potential roost sites on Kapiti Island, and assessed if habitat restoration would be required to increase the translocation's chance of success. First, trees with cavities accessible to us were sampled in six of Kapiti Island's forest types. Size variables known to affect roost site selection by lesser short-tailed bats at the tree and cavity level were measured. Trees were classified as containing cavities that could potentially provide suitable roosts if their values for all variables measured fell within the range of roosts used by lesser short-tailed bats in natural populations. Roosts were classified as suitably sized for solitary bats or for colonies, using measurements from both types of roosts in natural populations. Second, the density of these potential roost cavities was calculated. Cavities of a size potentially suitable for colonies were found in four of the six forest types at densities ranging from 3.2 +/- 3.2 SE to 52.4 +/- 14.0 trees per ha. Density of potential solitary roosts was much higher. Not all potential cavities will be suitable because they may be damp, poorly insulated, or have an unsuitable microclimate. Nevertheless, our estimates indicated that the two most extensive forest types each contained thousands of potential cavities of a size suitable for colonies of lesser short-tailed bats. In addition, there were tens of thousands of cavities large enough to shelter solitary bats. Roost habitat restoration appears unnecessary to assist translocated Mystacina tuberculata on Kapiti Island.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

How is a fruit tree like you? Using artistic metaphors to explore and develop emotional competence in children

Counselling children often requires the use of supplementary strategies in order to interest and engage the child in the therapeutic process. One such strategy is the Metaphorical Fruit Tree (MFT); an art metaphor suited to exploring and developing self-concept. Quantitative and qualitative data was used to explore the relationships between children’s ability to use metaphor, age, gender, and level of emotional competence (N = 58). Quantitative and qualitative analyses revealed a significant negative relationship between self-reported emotional competence and ability to use the MFT. It is proposed that children rely on different processes to understand self and as children’s ability to cognitively report on their emotional capabilities via the Emotional Competence Questionnaire (ECQ) increases, their ability to report creatively on those capabilities via the MFT is undermined. It is suggested that the MFT may be used, via creative processes and as an alternative to cognitive processes, to increase understanding and awareness of intrapersonal and interpersonal concepts of self in the child during counselling.

Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

Torres Strait Island parents' involvement in their children's mathematics learning : a discussion paper

This paper is a beginning point for discussing what the literature states about parents’ involvement in their children’s mathematics education. Where possible it will focus on Torres Strait Islander Peoples. Little is known about how Torres Strait Islander parents approach their children’s learning of mathematics and how important early mathematics is to mothers. What is known is that is they are keen for their children to receive an education that provides them with opportunities for their present and future lives. However, gaining access to education is challenging given that the language of instruction in schools is written to English conventions, decontextualised and disconnected from the students’ culture, community and home language. This paper discusses some of the issues raised in the literature about what parents are confronted with when making decisions about their children’s education.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.