Article excerpt

This is the first issue for 2011 of the Australasian Journal of Engineering Education (AJEE). This issue showcases the best papers from our annual conference held in Adelaide in December 2009.

The conference was a great success, bringing together engineering educators from Australia, New Zealand and all around the world. As part of the peer review process for the conferences, the reviewers were asked to nominate any papers they consider candidates for the conference's Best Paper Award. These papers were then subjected to a further review process and authors were invited to submit for publication in the AJEE. Several of these papers report on progress from funded Australian Learning and Teaching Council grants (ALTC) and Diversity and Structural Adjustment grants (DEEWR).

This issue comprises the four best papers, including the winner of the Best Paper Award--David Dowling's paper "Managing student diversity in the Master of Engineering Practice program: By Design"--which details an innovative program that enables students to use their workplace learning to demonstrate their achievement of objectives in up to half of the courses in the program. The paper describes the educational strategies used to develop the program and the flexible teaching and assessment approaches used to manage student diversity and facilitate student learning. Rosalie Goldsmith and colleagues' paper titled "Designing the future" reports on the findings from a two-day ALTC-funded forum that sought to establish a shared understanding with stakeholders (students, academics and industry) about how to achieve a design-based engineering curriculum. Findings reveal industry willingness to engage in the engineering curriculum to enhance authentic learning experiences. Faisal Anwar and colleagues' paper "Key factors for determining the suitability of converting a fluid-mechanics laboratory to remote-access mode" analyses a second-year fluid-mechanics laboratory to determine its suitability for conversion to remote access and then draws from this analysis three key factors (learning factors, equipment factors and cohort factors) that can be used as a preliminary framework for making generalised decisions regarding the suitability of any laboratory class for use in remote mode. …


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