Levelling expectations across different years in an undergraduate degree to support work-related learning : evidence for a multi-pronged approach.

One of the ways in which university departments and faculties can enhance the quality of learning and assessment is to develop a ‘well thought out criterion‐referenced assessment system’ (Biggs, 2003, p. 271). In designing undergraduate degrees (courses) this entails making decisions about the levelling of expectations across different years through devising objectives and their corresponding criteria and standards: a process of alignment analogous to what happens in unit (subject) design. These decisions about levelling have important repercussions in terms of supporting students’ work‐related learning, especially in relation to their ability to cope with the increasing cognitive and skill demands made on them as they progress through their studies. They also affect the accountability of teacher judgments of students’ responses to assessment tasks, achievement of unit objectives and, ultimately, whether students are awarded their degrees and are sufficiently prepared for the world of work. Research reveals that this decision‐making process is rarely underpinned by an explicit educational rationale (Morgan et al, 2002). The decision to implement criterion referenced assessment in an undergraduate microbiology degree was the impetus for developing such a rationale because of the implications for alignment, and therefore ‘levelling’ of expectations across different years of the degree. This paper provides supporting evidence for a multi‐pronged approach to levelling, through backward mapping of two revised units (foundation and exit year). This approach adheres to the principles of alignment while combining a work‐related approach (via industry input) with the blended disciplinary and learner‐centred approaches proposed by Morgan et al. (2002). It is suggested that this multi‐pronged approach has the potential for making expectations, especially work‐related ones across different year levels of degrees, more explicit to students and future employers.

Harnessing assessment and feedback in the first year to support learning success, engagement and retention

It has been argued that intentional first year curriculum design has a critical role to play in enhancing first year student engagement, success and retention (Kift, 2008). A fundamental first year curriculum objective should be to assist students to make the successful transition to assessment in higher education. Scott (2006) has identified that ‘relevant, consistent and integrated assessment … [with] prompt and constructive feedback’ are particularly relevant to student retention generally; while Nicol (2007) suggests that ‘lack of clarity regarding expectations in the first year, low levels of teacher feedback and poor motivation’ are key issues in the first year. At the very minimum, if we expect first year students to become independent and self-managing learners, they need to be supported in their early development and acquisition of tertiary assessment literacies (Orrell, 2005). Critical to this attainment is the necessity to alleviate early anxieties around assessment information, instructions, guidance, and performance. This includes, for example:  inducting students thoroughly into the academic languages and assessment genres they will encounter as the vehicles for evidencing learning success; and  making expectations about the quality of this evidence clear. Most importantly, students should receive regular formative feedback of their work early in their program of study to aid their learning and to provide information to both students and teachers on progress and achievement. Leveraging research conducted under an ALTC Senior Fellowship that has sought to articulate a research-based 'transition pedagogy' (Kift & Nelson, 2005) – a guiding philosophy for intentional first year curriculum design and support that carefully scaffolds and mediates the first year learning experience for contemporary heterogeneous cohorts – this paper will discuss theoretical and practical strategies and examples that should be of assistance in implementing good assessment and feedback practices across a range of disciplines in the first year.

Synchronous learner support for music sequencing software

In the field of music technology there is a distinct focus on networking between spatially disparate locales to improve teaching and learning through real-time communication. This article proposes a new delivery model for learner support based on a review of technical and learning services, pilot research using remote desktops to teach music-sequencing software, and recent education research regarding professional development. A 24/7 delivery model using remote desktops, mobile devices and shared calendars offers a flexible real-time addition to the learner support services already on offer. Treating every user of the service as a potential expert, the model aims to deliver universal support situated in a personalized context, which will serve the technical and education requirements of teachers and learners.

Self-efficacy and social support as predictors of smoking after a quit attempt

Examined findings (e.g., A. J. Yates and J. Thain [see PA, Vol 73:28269]) that suggest that perceived social support for attempts to quit smoking is a determinant of self-efficacy (SE). 102 adults (aged 18–71 yrs) who participated in a trial of 4 smoking interventions were studied over a 10-mo follow-up period. The study attested to the validity of SE as a predictor of sustained success from an attempt to stop smoking. The tendency for SE theory to be more strongly supported in the longer term was highly consistent with the proposed mechanism for SE effects. The absence of a relationship with perceived social support might be an advantage for SE, since support was a poor predictor of outcomes during follow-up. Results suggest that perceived social influences had less utility than personal skills and SE in predicting sustained non-smoking outcomes.

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).

Augmenting competitiveness of cities through tourism planning : the role of collaborative decision support systems

Many cities around the globe are now considering tourism facilities and their remarkable revenues in order to become competitive in the global economy. In many of these cities a great emphasis is given to the cultural tourism as it plays an important role in the establishment of creative and knowledge-base of cities. The literature points out the importance of local community support in cultural tourism. In such context, the use of new approach and technologies in tourism planning in order to increase the community participation and competitiveness of cities’ cultural assets gains a great significance. This paper advocates a new planning approach for tourism planning, particularly for cultural tourism, to increase the competitiveness of cities. As part of this new approach, the paper introduces the joined up planning approach integrated with a collaborative decision support system: ‘the community-oriented decision support system’. This collaborative planning support system is an effective and efficient tool for cultural tourism planning, which provides a platform for local communities’ participation in the development decision process.

Intelligent urban management systems : online decision support systems for ubiquitous cities

A successful urban management support system requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism. The chapter emphasizes the importance of integrated urban management to better tackle the climate change, and to achieve sustainable urban development and sound urban growth management. This chapter introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for ubiquitous cities. The chapter discusses the essential role of online collaborative decision making in urban and infrastructure planning, development and management, and advocates transparent, fully democratic and participatory mechanisms for an effective urban management system that is particularly suitable for ubiquitous cities. This chapter also sheds light on some of the unclear processes of urban management of ubiquitous cities and online collaborative decision making, and reveals the key benefits of integrated and participatory mechanisms in successfully constructing sustainable ubiquitous cities.

A decision support system for sustainable urban development : the integrated land use and transportation indexing model

The broad definition of sustainable development at the early stage of its introduction has caused confusion and hesitation among local authorities and planning professionals. The main difficulties are experience in employing loosely-defined principles of sustainable development in setting policies and goals. The question of how this theory/rhetoric-practice gap could be filled will be the theme of this study. One of the widely employed sustainability accounting approaches by governmental organisations, triple bottom line, and applicability of this approach to sustainable urban development policies will be examined. When incorporating triple bottom line considerations with the environmental impact assessment techniques, the framework of GIS-based decision support system that helps decision-makers in selecting policy option according to the economic, environmental and social impacts will be introduced. In order to embrace sustainable urban development policy considerations, the relationship between urban form, travel pattern and socio-economic attributes should be clarified. This clarification associated with other input decision support systems will picture the holistic state of the urban settings in terms of sustainability. In this study, grid-based indexing methodology will be employed to visualise the degree of compatibility of selected scenarios with the designated sustainable urban future. In addition, this tool will provide valuable knowledge about the spatial dimension of the sustainable development. It will also give fine details about the possible impacts of urban development proposals by employing disaggregated spatial data analysis (e.g. land-use, transportation, urban services, population density, pollution, etc.). The visualisation capacity of this tool will help decision makers and other stakeholders compare and select alternative of future urban developments.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Intellectual disability and sexuality : attitudes of disability support staff and leisure industry employees

Background: The attitudes of support staff and others in the community towards the sexuality of individuals with an intellectual disability (ID) have the potential to influence opportunities for normalised life experiences in the area of sexuality. ----- Method: A sample of 169 disability support staff and 50 employees from leisure and services industries completed the Attitudes to Sexuality Questionnaires (Individuals with an Intellectual Disability [ASQ–ID], and Individuals from the General Population [ASQ–GP]). ----- Results: Support staff and leisure workers reported generally positive attitudes towards the sexuality of individuals with an ID, but men were seen as having less self-control than women. Support staff were more cautious in their views about parenting, and both groups considered a lower level of sexual freedom to be desirable for women with an ID compared to women who are developing typically. Conclusions Attitudes of both groups are generally quite positive in relation to ID and sexuality.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

The roles and importance of formal and informal sources of business support in young firms : evidence from the UK’s 2008 Federation of Small Business Survey

Objectives This research explores the relationship between young firms, their growth orientation-intention and a range of relationships which can be seen to provide business support. Prior-work Research indicates that networks impact the firm’s ability to secure resources (Sirmon and Hitt 2003; Liao and Welsch. 2004; Hanlon and Saunders 2007). Networks have been evaluated in a number of ways ranging from simple counts to characteristics of their composition (Davidsson and Honig 2003), strength of relationships (Granovetter 1973) and network diversity (Carter et al 2003). By providing access to resources and knowledge (from start-up assistance and raising capital, (e.g. Smallbone et al, 2003), networks may assist in enabling continued persistence during those times where firms may experience resource constraints owing to firm growth (Baker and Nelson 2005). Approach The data used in this research was generated in the 2008 UK Federation of Small Businesses (FSB) survey. Over 1,000 of the firms responding were found to fall into the category of “young”, ((defined as firms under 4 years old). Firms were considered the unit of analysis with the entrepreneur being the chief spokesperson for the firm. Preliminary data analysis considered key demographic characteristics and industry classifications, comparing the FSB data with that of the UK government’s own (BERR) Small Business Surveys of 2007 and 2008, to establish some degree of representativeness of the respondents. The analysis then examined networks with varying potential ability to provide support for young firms, the networks measured in terms of number, diversity, characteristic and strength in its relationship to young firm growth orientation. The diversity of business-support-related relationships ranged from friends and family, through professional services, customers and suppliers, and government business services, to trade associations and informal business networks. The characteristics of these formal and informal sources of support for new businesses are examined across a range of business support-type activities for new firms. The number of relationships and types of business support are also explored. Finally, the strength of these relationships is examined by analysis of the source of business support, type of business support, and links to the growth orientation-intention of the firm, after controlling for a number of key variables related to firm and industry status and owner characteristics. Results Preliminary analysis of the data by means of univariate analysis showed that average number of sources of advice was around 2.5 (from a potential total of 6). In terms of the diversity of relationships, universities had by far the smallest percentage of firms receiving beneficial advice from them. Government business services were beneficially used by 40% of young firms, the other relationship types being around the 50-55% mark. In terms of characteristics of the advice, the average number of areas in which benefit was achieved was around 5.5 of a maximum of 15. Start-up advice has by far the highest percentage of firms obtaining beneficial advice, with increasing sales, improving contacts and improving confidence being the other categories at or around the 50% mark. Other market-focused areas where benefits were also received were in the areas of new markets, existing product improvements and new product improvements, where around 40% of the young responding firms obtained benefit. Regression techniques evaluating the strength of these relationships in terms of the links between business support (by source of support, type of support, and range of support) and firm growth orientation-intention focus highlighted a number of significant relationships, even after controlling for a range of other explanatory variables identified in the literature. Specifically, there was found to be a positive relationship between receiving business advice generally (regardless of type or source) and growth orientation. This relationship was seen to be stronger, however, when looking at the number of types of beneficial advice received, and stronger again for the number of sources of this advice. In terms of individual sources of advice, customers and suppliers had the strongest relationship with growth, with Government business services also found to be significant. Combining these two sources was also seen to increase the strength of the relationship between these two sources of advice and growth orientation. In considering areas of support, growth was most strongly positively related to advice that benefited the development of new products and services, and also business confidence, but was negatively related to advice linked to business recovery. Finally, amalgamating the 4 key types and sources of advice to examine the impact of combinations of these types and sources of advice also improved the strength of the relationship. Implications The findings will assist in the understanding of young firms in general and growth more specifically, particularly the role and importance of specific sources, types and combinations of business support used more extensively by new young growth-oriented firms. Value This research may assist in processes designed to allow entrepreneurs to make better decisions; educators and support organizations to develop better advice and assistance, and Governments design better conditions for the creation of new growth-oriented businesses.

Scheduling trains as a blocking parallel-machine job shop scheduling problem

In this paper, the train scheduling problem is modelled as a blocking parallel-machine job shop scheduling (BPMJSS) problem. In the model, trains, single-track sections and multiple-track sections, respectively, are synonymous with jobs, single machines and parallel machines, and an operation is regarded as the movement/traversal of a train across a section. Due to the lack of buffer space, the real-life case should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold the train until next section on the routing becomes available. Based on literature review and our analysis, it is very hard to find a feasible complete schedule directly for BPMJSS problems. Firstly, a parallel-machine job-shop-scheduling (PMJSS) problem is solved by an improved shifting bottleneck procedure (SBP) algorithm without considering blocking conditions. Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models. The proposed algorithms have been implemented and validated using real-world data from Queensland Rail. Sensitivity analysis has been applied by considering train length, upgrading track sections, increasing train speed and changing bottleneck sections. The outcomes show that the proposed methodology would be a very useful tool for the real-life train scheduling problems