Trends, fads and futures : computers in learning in tertiary education, 1983-2002

ASCILITE, the Australasian Society for Computers in Learning in Tertiary Education, is a professional society focusing on computers in learning in tertiary education that has been sponsoring conferences for its membership since 1986. Prior to this, three earlier conferences formed the genesis of ASCILITE. Over that period there have been significant changes in pedagogy and technology but few attempts, if any, have been made to analyse the ways in which conference proceedings have reflected these changes and shifts. The purpose of this research paper is to review the ASCILITE proceedings and provide an analysis of “trends, fads and futures” to reflect on past initiatives, propose potential directions and assist the society identify strategic directions. In addition, the analysis will provide a basis from which further research can be justified in terms of better understanding “computers in learning in tertiary education”.

The use of laptop computers in programming lectures

This research explores the effect of the use of laptop computers on students’ learning experiences during lectures. Our methodology involves embedding laptops with visualization software as a learning aid during lectures. We then employ a framework of seven principles of good practice in higher education to evaluate the impact of the use of laptop computers on the learning experience of computer programming students. Overall, we found that students were highly motivated and supportive of this innovative use of laptop computers with lectures.

Flood forecasting in large rivers with data-driven models

Results from the application of adaptive neuro-fuzzy inference system (ANFIS) to forecast water levels at 3 stations along the mainstream of the Lower Mekong River are reported in this paper. The study investigated the effects of including water levels from upstream stations and tributaries, and rainfall as inputs to ANFIS models developed for the 3 stations. When upstream water levels in the mainstream were used as input, improvements to forecasts were realized only when the water levels from 1 or at most 2 upstream stations were included. This is because when there are significant contributions of flow from the tributaries, the correlation between the water levels in the upstream stations and stations of interest decreases, limiting the effectiveness of including water levels from upstream stations as inputs. In addition, only improvements at short lead times were achieved. Including the water level from the tributaries did not significantly improve forecast results. This is attributed mainly to the fact that the flow contributions represented by the tributaries may not be significant enough, given that there could be large volume of flow discharging directly from the catchments which are ungauged, into the mainstream. The largest improvement for 1-day forecasts was obtained for Kratie station where lateral flow contribution was 17 %, the highest for the 3 stations considered. The inclusion of rainfall as input resulted in significant improvements to long-term forecasts. For Thakhek, where rainfall is most significant, the persistence index and coefficient of efficiency for 5-lead-day forecasts improved from 0.17 to 0.44 and 0.89 to 0.93, respectively, whereas the root mean square error decreased from 0.83 to 0.69 m.