The equity raw-score matrix - a multi-dimensional indicator of potential disadvantage in higher education

Issues surrounding student participation, transition, retention and successful completion in higher education are topical. While the Australian federal government has identified broad groupings of under-represented students, these do not shed light on the complexities underlying the issues of the educationally disadvantaged, such as the compounding problems of multiple equity-group membership or the overlay of the acute or chronic effects of equity sub-group membership. This paper details the Equity Raw-Score Matrix. The matrix is a multi-dimensional indicator of potential disadvantage in learners, created for the specific purposes of diagnosing the complexities of educational disadvantage and creating pre-emptive strategies for the participation, transition and retention of students who are disadvantaged. The paper also describes the qualitative research study that was the catalyst for the creation of the matrix.

Understanding the multidimensional nature of student disadvantage to better inform the provision of 'glocal' learning

There are growing calls to make equity a focus of research concern in Australian higher education. In turn such research will, it is anticipated, inform the planning, delivery and implementation of education in an era of rapid global and technological change. Yet to undertake such research requires generating a greater understanding of the complex and multidimensional nature of educational disadvantage for the purposes of equity. This paper explores the Equity Raw-Score Matrix as a means of capturing economic, geographic and social disadvantage.

Improvements in the score matrix calculation method using parallel score estimating algorithm

The increasing amount of sequences stored in genomic databases has become unfeasible to the sequential analysis. Then, the parallel computing brought its power to the Bioinformatics through parallel algorithms to align and analyze the sequences, providing improvements mainly in the running time of these algorithms. In many situations, the parallel strategy contributes to reducing the computational complexity of the big problems. This work shows some results obtained by an implementation of a parallel score estimating technique for the score matrix calculation stage, which is the first stage of a progressive multiple sequence alignment. The performance and quality of the parallel score estimating are compared with the results of a dynamic programming approach also implemented in parallel. This comparison shows a significant reduction of running time. Moreover, the quality of the final alignment, using the new strategy, is analyzed and compared with the quality of the approach with dynamic programming.