Assessment for learning : a sociocultural approach

Assessment for Learning (AfL) is a title given to classroom evaluative practices that share the purpose of diagnosing and informing teachers and students about learning progress, during the learning process. These practices also have the potential to develop learner autonomy by increasing student motivation and mastery through developing the learner's capacity to monitor and plan his or her own learning progress. Yet teacher adoption of the practices is not a straightforward implementation of techniques within an existing classroom repertoire. Recent research highlights a more complex interrelationship between teacher and student beliefs, identities, and traditions of power within assessment and learning in classroom contexts. These often hidden relationships can add layers of complexity for teachers implementing assessment change, and may act as barriers that frustrate efforts to realise the AfL goal of learner autonomy. By interpreting AfL practices from a sociocultural perspective, the social and cultural contexts that influence classroom assessment can be better understood. In turn teachers can thus be better supported in adopting AfL practices within the complexities of the social, cultural and policy contexts of schooling.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

The present via the past : an archaelogical approach to analysing the design and use of a contemporary urban village

This research applies an archaeological lens to an inner-city master planned development in order to investigate the tension between the design of space and the use of space. The chosen case study for this thesis is Kelvin Grove Urban Village (KGUV), located in inner city Brisbane, Australia. The site of this urban village has strong links to the past. KGUV draws on both the history of the place in particular along with more general mythologies of village life in its design and subsequent marketing approaches. The design and marketing approach depends upon notions of an imagined past where life in a place shaped like a traditional village was better and more socially sustainable than modern urban spaces. The appropriation of this urban village concept has been criticised as a shallow marketing ploy. The translation and applicability of the urban village model across time and space is therefore contentious. KGUV was considered both in terms of its design and marketing and in terms of a reading of the actual use of this master planned place. Central to this analysis is the figure of the boundary and related themes of social heterogeneity, inclusion and exclusion. The refraction of history in the site is also an important theme. An interpretive archaeological approach was used overall as a novel method to derive this analysis.

A collaborative approach to reading workshop in the middle years

Reflection Questions • How does the collaborative reading workshop approach engage students in higher order thinking and deep engagement with text? • How does the collaborative reading workshop approach support students to be active citizens and critically literate? • How does the interaction and collaborative thinking in this approach contribute to the students’ intellectual engagement and the teacher’s pedagogical rigor? • How could this approach be implemented or adapted at your school?

User behaviours and intentions in digital media in Australia

This paper reports findings from a study of user behaviours and intentions towards online news and information in Australia, undertaken by the Queensland University of Technology Creative Industries Faculty and the Smart Services Cooperative Research Centre. It has used a literature review, online survey, focus groups and interviews to explore attitudes and behaviours towards online news and information. The literature review on consumer user of online media highlighted emerging technical opportunities, and flagged existing barriers to access experienced by consumers in the Australian digital media sector. The literature review highlighted multiple disconnects between consumer interests in online news and their ability to fulfil them. This presents an opportunity for news entities to appraise and resolve. Doing so may enhance their service offering, attract consumers and improve loyalty. These themes were further explored by the survey. The survey results revealed three typologies of user, described as ‘convenience’, ‘loyal’ and ‘customising’. Convenience users tend to access news by default, for example when they log out of email. Loyal users seek out a trusted brand such as mainstream news mastheads. Customising users tend to tailor news to their preferences, and be the first to use leading edge media. Respondents to the survey were then invited to participate in focus groups, which aimed to test the survey results. Consumer perceptions and attitudes are important factors in progression towards an information economy, because ultimately consumers are customers. By segmenting the online news market according to customer typology, media providers may identify new opportunities to attract and retain customers.

Sustainable urban futures : an ecological approach to sustainable urban development

In recent years, cities show increasing signs of environmental problems due to the negative impacts of urban activities. The degradation and depletion of natural resources, climate change and development pressure on green areas have become major concerns for cities. In response to these problems, urban planning policies have shifted to a sustainable focus and authorities have begun to develop new strategies for improving the quality of urban ecosystems. An extremely important function of an urban ecosystem is to provide healthy and sustainable environments for both natural systems and communities. Therefore, ecological planning is a functional requirement in the establishment of sustainable built environment. With ecological planning human needs are supplied while natural resources are used in the most effective and sustainable manner. And the maintenance of ecological balance is sustained. Protecting human and environmental health, having healthy ecosystems, reducing environmental pollution and providing green spaces are just a few of the many benefits of ecological planning. In this context, the paper briefly presents a short overview of the importance of the implementation of ecological planning into sustainable urban development. Furthermore, the paper defines the conceptual framework of a new method for developing sustainable urban ecosystems through ecological planning approach. In the future of the research, this model will be developed as a guideline for the assessment of the ecological sustainability in built environments.

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

A holistic understanding of web design elements

Web design elements are significantly important for web designers to understand target users in terms of effective communication design and to develop a successful web site. However, web design elements generally known are broad and various that are hardly conceived and classified, so many practitioners and design researchers approach to web design elements based on graphic and visual design that mainly focus on print media design. This paper discusses about web design elements in terms of online user experience, as web media certainly differs from print media. It aims to propose a fundamentally new concept, called 'UEDUs: User Experience Design Units' which enables web designers to define web design elements and conceptualise user experience depending on the purpose of web site development.

Augmenting web service discovery by cognitive semantics and abduction

We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distri- butions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document's initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur's search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

HCX : an efficient hybrid clustering approach for XML documents

This paper proposes a novel Hybrid Clustering approach for XML documents (HCX) that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The empirical analysis reveals that the proposed method is scalable and accurate.

XCFS - a novel approach for clustering XML documents using both the structure and the content

XML document clustering is essential for many document handling applications such as information storage, retrieval, integration and transformation. An XML clustering algorithm should process both the structural and the content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.

The effective and the evocative : reflecting on practice-led research approaches in art and design

Early in the practice-led research debate, Steven Scrivener (2000, 2002) identified some general differences in the approach of artists and designers undertaking postgraduate research. His distinctions centered on the role of the artefact in problem-based research (associated with design) and creative-production research (associated with artistic practice). Nonetheless, in broader discussions on practice-led research, 'art and design' often continues to be conflated within a single term. In particular, marked differences between art and design methodologies, theoretical framing, research goals and research claims have been underestimated. This paper revisits Scrivener's work and establishes further distinctions between art and design research. It is informed by our own experiences of postgraduate supervision and research methods training, and an empirical study of over sixty postgraduate, practice-led projects completed at the Creative Industries Faculty of QUT between 2002 and 2008. Our reflections have led us to propose that artists and designers work with differing research goals (the evocative and the effective, respectively), which are played out in the questions asked, the creative process, the role of the artefact and the way new knowledge is evidenced. Of course, research projects will have their own idiosyncrasies but, we argue, marking out the poles at each end of the spectrum of art and design provides useful insights for postgraduate candidates, supervisors and methodologists alike.

Tag based collaborative filtering for recommender systems

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user’s important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.