Development of educational policy and early intervention for learning difficulties in Australia: In the case of Queensland

This paper aims to describe the historical outline and current development of the educational policy for students with learning difficulties in Australia, focusing especially on the state of Queensland. In order to develop educational policy of learning difficulities at the state level, the concept of learning difficulities had been discussed until the middle of the 1970's. Receiving the submissions which argued strongly against a diagnostically-oriented definition of learning disabilities, the Select Comittee concluded that there was much conceptual confusion regarding the definition and cause of learining difficulties that might take many years to resolve. Despite that it was recongnised that action was needed to assist children by looking at their "total learning environmerit", and recommended the development of an educational policy for students with learning difficulties. During 1980's, support teachers for students with learning difficulties were employed in many schools. Scince the early 1980's support teachers have been making their efforts in regular classrooms rather than in the resource rooms. Their roles have been to help students with learning difficulties using effective and specific skills, and to consult with the regular classroom teacher in solving the problems related to learning difficulties in regular classes. Currently, the support system for students with learning difficulties has been employed to organize a more systematic and broader approach in Queensland based on the accountability of schools. In the context of enphasizing literacy and numeracy, a systematic whole school approach and particular programs, such as the Year 2 Diagnostic Net and Reading Recovery, have been introduced into the educational system for early identification and early intervention.

Investigating technical and scale efficiencies of Australian universities through data envelopment analysis

Performance indicators in the public sector have often been criticised for being inadequate and not conducive to analysing efficiency. The main objective of this study is to use data envelopment analysis (DEA) to examine the relative efficiency of Australian universities. Three performance models are developed, namely, overall performance, performance on delivery of educational services, and performance on fee-paying enrolments. The findings based on 1995 data show that the university sector was performing well on technical and scale efficiency but there was room for improving performance on fee-paying enrolments. There were also small slacks in input utilisation. More universities were operating at decreasing returns to scale, indicating a potential to downsize. DEA helps in identifying the reference sets for inefficient institutions and objectively determines productivity improvements. As such, it can be a valuable benchmarking tool for educational administrators and assist in more efficient allocation of scarce resources. In the absence of market mechanisms to price educational outputs, which renders traditional production or cost functions inappropriate, universities are particularly obliged to seek alternative efficiency analysis methods such as DEA.

Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: A non-parametric approach

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for 1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was efficiency was 56.2 per cent and 69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure, and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs.