Student Dis/satisfaction and Academic Dis/enchantment with Edu-capitalism

Over three decades there has been a shift from ideologies of idealism and educationalism towards instrumentalism in higher education due to the global circulation of neoliberal ideologies. Facilitated by digital technologies and encouraged by international ranking systems, there is a paradoxical trend towards homogenisation rather than heterogeneity in terms of what counts as valued knowledge, producing tensions in national policies, institutional responses and academic work in Australia as elsewhere. The paper identifies the implications of trends driving universities towards entrepreneurialism, hyper-instrumentalism, continual rebranding in their search for distinctiveness in global markets, restructuring towards specialisation, focusing on immediate use-value of research, vocationalising teaching, demand driven curriculum that makes students happy, and the disaggregation of curriculum underpinning new multimodal forms of online learning / management technologies.

A ranking list for information systems journals

There has been an increasing focus internationally on the quality and impact of research outputs in recent years. Several countries, including the United Kingdom and New Zealand have implemented schemes to base the funding of research on research quality. The Australian government is planning to implement a Research Quality Framework (RQF) in the next few years that will impact greatly on funding of research in Australian universities. A key issue for Australian researchers is how the quality and impact of research is defined and measured in their discipline areas. Although peer review is widely used to assess the quality of research outputs, it is expensive and labour intensive. Other surrogate quality measures are often used. This paper focuses on measuring the quality of research outputs in the information systems discipline. We argue that measures such as citation indexes are inappropriate for information systems and that the publication outlet is a more suitable indicator of quality. We present a ranking list of journals for the information systems discipline, and discuss the approach we have taken in developing the list. We discuss how the ranking list may be used in defining and measuring the quality of information systems research outputs, the limitations inherent in the approach and discuss lessons we have learned in developing the list.

ERA distribution of information systems journals

The Excellence in Research for Australia (ERA) initiative being conducted by the Australian Research Council (ARC), mandates a single journal and conference ranking scheme over every academic discipline in Australia. A universal publication outlet ranking list mandated by a government agency is unique and has attracted interest and comment both within Australia and overseas. Equally, the interest shown has come from all sectors involved in academic publishing – authors, reviewers, publishers – and from commercial and open access publishers. This paper investigates the distribution of information systems journals over the various ERA parameters and comments on a claim of bias whereby the ranking of a journal is positively influenced by the number of years it has been in existence in the areas of information systems and business journals. Clear evidence of the diversity of the information systems discipline is observed. The benefits of a multidisciplinary foundation for information systems is also noted. Longer established journals are shown to attract higher rankings and possible reasons for and implications flowing from this are discussed.

Ranking method for optimizing precision/recall of content-based image retrieval

The ranking method is a key element of Content-based Image Retrieval (CBIR) system, which can affect the final retrieval performance. In the literature, previous ranking methods based on either distance or probability do not explicitly relate to precision and recall, which are normally used to evaluate the performance of CBIR systems. In this paper, a novel ranking method based on relative density is proposed to improve the probability based approach by ranking images in the class. The proposed method can achieve optimal precision and recall. The experiments conducted on a large photographic collection show significant improvements of retrieval performance.

Role assessment of GIS analysis and its reliability while ranking urban sustainability using scenarios specific to regional climate, community and culture

Urban Sustainability expresses the level of conservation of a city while living a town or consuming its urban resources, but the measurement of urban sustainability depends on what are considered important indicators of conservation besides the permitted levels of consumption in accordance with adopted criteria. This criterion should have common factors that are shared for all the members tested or cities to be evaluated as in this particular case for Abu Dhabi, but also have specific factors that are related to the geographic place, community and culture, that is the measures of urban sustainability specific to a middle east climate, community and culture where GIS Vector and Raster analysis have a role or add a value in urban sustainability measurements or grading are considered herein. Scenarios were tested using various GIS data types to replicate urban history (ten years period), current status and expected future of Abu Dhabi City setting factors to climate, community needs and culture. The useful Vector or Raster GIS data sets that are related to every scenario where selected and analysed in the sense of how and how much it can benefit the urban sustainability ranking in quantity and quality tests, this besides assessing the suitable data nature, type and format, the important topology rules to be considered, the useful attributes to be added, the relationships which should be maintained between data types of a geo- database, and specify its usage in a specific scenario test, then setting weights to each and every data type representing some elements of a phenomenon related to urban suitability factor. The results of assessing the role of GIS analysis provided data collection specifications such as the measures of accuracy reliable to a certain type of GIS functional analysis used in an urban sustainability ranking scenario tests. This paper reflects the prior results of the research that is conducted to test the multidiscipline evaluation of urban sustainability using different indicator metrics, that implement vector GIS Analysis and Raster GIS analysis as basic tools to assist the evaluation and increase of its reliability besides assessing and decomposing it, after which a hypothetical implementation of the chosen evaluation model represented by various scenarios was implemented on the planned urban sustainability factors for a certain period of time to appraise the expected future grade of urban sustainability and come out with advises associated with scenarios for assuring gap filling and relative high urban future sustainability. The results this paper is reflecting are concentrating on the elements of vector and raster GIS analysis that assists the proper urban sustainability grading within the chosen model, the reliability of spatial data collected; analysis selected and resulted spatial information. Starting from selecting some important indicators to comprise the model which include regional culture, climate and community needs an example of what was used is Energy Demand & Consumption (Cooling systems). Thus, this factor is related to the climate and it‟s regional specific as the temperature varies around 30-45 degrees centigrade in city areas, GIS 3D Polygons of building data used to analyse the volume of buildings, attributes „building heights‟, estimate the number of floors from the equation, following energy demand was calculated and consumption for the unit volume, and compared it in scenario with possible sustainable energy supply or using different environmental friendly cooling systems this is followed by calculating the cooling system effects on an area unit selected to be 1 sq. km, combined with the level of greenery area, and open space, as represented by parks polygons, trees polygons, empty areas, pedestrian polygons and road surface area polygons. (initial measures showed that cooling system consumption can be reduced by around 15 -20 % with a well-planned building distributions, proper spaces and with using environmental friendly products and building material, temperature levels were also combined in the scenario extracted from satellite images as interpreted from thermal bands 3 times during the period of assessment. Other examples of the assessment of GIS analysis to urban sustainability took place included Waste Productivity, some effects of greenhouse gases measured by the intensity of road polygons and closeness to dwelling areas, industry areas as defined from land use land cover thematic maps produced from classified satellite images then vectors were created to take part in defining their role within the scenarios. City Noise and light intensity assessment was also investigated, as the region experiences rapid development and noise is magnified due to construction activities, closeness of the airports, and highways. The assessment investigated the measures taken by urban planners to reduce degradation or properly manage it. Finally as a conclusion tables were presented to reflect the scenario results in combination with GIS data types, analysis types, and the level of GIS data reliability to measure the sustainability level of a city related to cultural and regional demands.

Synthetic evaluation method for bridge priority ranking

In order to maintain the transportation operation, proper monitoring systems should be established on road structures, especially bridges. Since these systems need enormous investments, only a part of bridges should be equipped. Thus, the priorities of the bridges should be ranked. In this paper, a method based on two-level synthetic evaluation is proposed. First, the importance of each bridge is analyzed through the economic analysis. Six factors are considered for the bridges in a network, including construction cost, service duration, length, location importance coefficient, traffic volume, and reconstruction time. Second, the safety condition of the bridge is evaluated by using improved entropy method (IEM) which combines subjective weight with objective entropy weight. Five indices are incorporated in this step, i.e., design and construction condition, technical condition, level of overloading, hazard of wind and earthquake and environmental factors. Finally, the priorities of all the bridge in one network can be ranked and classified through a judge matrix. To demonstrate the effectiveness of the proposed method, a main highway including 16 bridges is taken as an illustrative example. The results show that the bridges can be ranked and classified quickly by using the proposed method.

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

Learning preference models from human generated data is an important task in modern information processing systems. Its popular setting consists of simple input ratings, assigned with numerical values to indicate their relevancy with respect to a specific query. Since ratings are often specified within a small range, several objects may have the same ratings, thus creating ties among objects for a given query. Dealing with this phenomena presents a general problem of modelling preferences in the presence of ties and being query-specific. To this end, we present in this paper a novel approach by constructing probabilistic models directly on the collection of objects exploiting the combinatorial structure induced by the ties among them. The proposed probabilistic setting allows exploration of a super-exponential combinatorial state-space with unknown numbers of partitions and unknown order among them. Learning and inference in such a large state-space are challenging, and yet we present in this paper efficient algorithms to perform these tasks. Our approach exploits discrete choice theory, imposing generative process such that the finite set of objects is partitioned into subsets in a stagewise procedure, and thus reducing the state-space at each stage significantly. Efficient Markov chain Monte Carlo algorithms are then presented for the proposed models. We demonstrate that the model can potentially be trained in a large-scale setting of hundreds of thousands objects using an ordinary computer. In fact, in some special cases with appropriate model specification, our models can be learned in linear time. We evaluate the models on two application areas: (i) document ranking with the data from the Yahoo! challenge and (ii) collaborative filtering with movie data. We demonstrate that the models are competitive against state-of-the-arts.