A topic based document relevance ranking model

Topic modelling has been widely used in the fields of information retrieval, text mining, machine learning, etc. In this paper, we propose a novel model, Pattern Enhanced Topic Model (PETM), which makes improvements to topic modelling by semantically representing topics with discriminative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to determine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Extensive experiments are conducted to evaluate the effectiveness of PETM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.

A little learning is a dangerous thing? The ethics of teaching accidental tourists in higher education

This paper raises questions about the ethical issues that arise for academics and universities when under-graduate students enrol in classes outside of their discipline - classes that are not designed to be multi-disciplinary or introductory. We term these students ‘accidental tourists'. Differences between disciplines in terms of pedagogy, norms, language and understanding may pose challenges for accidental tourists in achieving desired learning outcomes. This paper begins a discussion about whether lecturers and universities have any ethical obligations towards supporting the learning of these students. Recognising that engaging with only one ethical theory leads to a fragmented moral vision, this paper employs a variety of ethical theories to examine any possible moral obligations that may fall upon lecturers and/or universities. In regards to lecturers, the paper critically engages with the ethical theories of utilitarianism, Kantianism and virtue ethics (Aristotle) to determine the extent of any academic duty to accidental tourists. In relation to universities, this paper employs the emerging ethical theory of organisational ethics as a lens through which to critically examine any possible obligations. Organisational ethics stems from the recognition that moral demands also exist for organisations so organisations must be reconceptualised as ethical actors and their policies and practices subject to ethical scrutiny. The analysis in this paper illustrates the challenges faced by lecturers some of whom, we theorise, may experience a form of moral distress facing a conflict between personal beliefs and organisational requirements. It also critically examines the role and responsibilities of universities towards students and towards their staff and the inherent moral tensions between a market model and demands for ‘good' learning experiences. This paper highlights the tensions for academics, between academics and universities and within university policy and indicates the need for greater reflection about this issue, especially given the many constraints facing lecturers and universities.

Embedding EfS in teacher education : an introductory guide to using the systems change model

Welcome to this introductory guide on using a systems change model to embed Education for Sustainability (EfS) into teacher education. Pressing sustainability issues such as climate change, biodiversity loss and depletion of non-renewable resources pose new challenges for education. The importance of education in preparing future citizens to engage in sustainable living practices and help create a more sustainable world is widely acknowledged. As a result many universities around the world are beginning to recognize the need to integrate EfS into their teacher education programs. However, evidence indicates that there is little or no core EfS knowledge or pedagogy in pre-service teacher courses available to student teachers in a thorough and systematic fashion. Instead efforts are fragmented and individually or, at best, institutionally-based and lacking a systems approach to change, an approach that is seen as essential to achieving a sustainable society (Henderson & Tilbury, 2004). The result is new teachers are graduating without the necessary knowledge or skills to teach in ways that enable them to prepare their students to cope well with the new and emerging challenges their communities face. This guide has been prepared as part of a teaching and learning research project that applied a systems change approach to embedding the learning and teaching of sustainability into pre-service teacher education. The processes, outcomes and lessons learnt from this project are presented here as a guide for navigating pathways to systemic change in the journey of re-orienting teacher education towards sustainability. The guide also highlights how a systems change approach can be used to successfully enact change within a teacher education system. If you are curious about how to introduce and embed EfS into teacher education – or have tried other models and are looking for a more encompassing, transformative approach – this guide is designed to help you. The material presented in this guide is designed to be flexible and adaptive. However you choose to use the content, our aim is to help you and your students develop new perspectives, promote discussion and to engage with a system-wide approach to change.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Myosin light chain kinase regulates synaptic plasticity and fear learning in the lateral amygdala

Learning and memory depend on signaling mole- cules that affect synaptic efficacy. The cytoskeleton has been implicated in regulating synaptic transmission but its role in learning and memory is poorly understood. Fear learning depends on plasticity in the lateral nucleus of the amygdala. We therefore examined whether the cytoskeletal-regulatory protein, myosin light chain kinase, might contribute to fear learning in the rat lateral amygdala. Microinjection of ML-7, a specific inhibitor of myosin light chain kinase, into the lateral nucleus of the amygdala before fear conditioning, but not immediately afterward, enhanced both short-term memory and long-term memory, suggesting that myosin light chain kinase is involved specifically in memory acquisition rather than in posttraining consolidation of memory. Myosin light chain kinase inhibitor had no effect on memory retrieval. Furthermore, ML-7 had no effect on behavior when the train- ing stimuli were presented in a non-associative manner. An- atomical studies showed that myosin light chain kinase is present in cells throughout lateral nucleus of the amygdala and is localized to dendritic shafts and spines that are postsynaptic to the projections from the auditory thalamus to lateral nucleus of the amygdala, a pathway specifically impli- cated in fear learning. Inhibition of myosin light chain kinase enhanced long-term potentiation, a physiological model of learning, in the auditory thalamic pathway to the lateral nu- cleus of the amygdala. When ML-7 was applied without as- sociative tetanic stimulation it had no effect on synaptic responses in lateral nucleus of the amygdala. Thus, myosin light chain kinase activity in lateral nucleus of the amygdala appears to normally suppress synaptic plasticity in the cir- cuits underlying fear learning, suggesting that myosin light chain kinase may help prevent the acquisition of irrelevant fears. Impairment of this mechanism could contribute to pathological fear learning.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Incorporating anchored learning in a C# Intelligent Tutoring System

Learning programming is known to be difficult. One possible reason why students fail programming is related to the fact that traditional learning in the classroom places more emphasis on lecturing the material instead of applying the material to a real application. For some students, this teaching model may not catch their interest. As a result they may not give their best effort to understand the material given. Seeing how the knowledge can be applied to real life problems can increase student interest in learning. As a consequence, this will increase their effort to learn. Anchored learning that applies knowledge to solve real life problems may be the key to improving student performance. In anchored learning, it is necessary to provide resources that can be accessed by the student as they learn. These resources can be provided by creating an Intelligent Tutoring System (ITS) that can support the student when they need help or experience a problem. Unfortunately, there is no ITS developed for the programming domain that has incorporated anchored learning in its teaching system. Having an ITS that supports anchored learning will not only be able to help the student learn programming effectively but will also make the learning process more enjoyable. This research tries to help students learn C# programming using an anchored learning ITS named CSTutor. Role playing is used in CSTutor to present a real world situation where they develop their skills. A knowledge base using First Order Logic is used to represent the student's code and to give feedback and assistance accordingly.

A multidimensional model of client success when engaging external consultants

Too often the relationship between client and external consultants is perceived as one of protagonist versus antogonist. Stories on dramatic, failed consultancies abound, as do related anecdotal quips. A contributing factor to many "apparently" failed consultancies is a poor appreciation by both the client and consultant of the client's true goals for the project and how to assess progress toward these goals. This paper presents and analyses a measurement model for assessing client success when engaging an external consultant. Three main areas of assessment are identified: (1) the consultant;s recommendations, (2) client learning, and (3) consultant performance. Engagement success is emperically measured along these dimensions through a series of case studies and a subsequent survey of clients and consultants involved in 85 computer-based information system selection projects. Validation fo the model constructs suggests the existence of six distinct and individually important dimensions of engagement success. both clients and consultants are encouraged to attend to these dimensions in pre-engagement proposal and selection processes, and post-engagement evaluation of outcomes.

Advancing practice in critical care : a model of knowledge integration

Aim The aim of this paper was to discuss the potential development of a conceptual model of knowledge integration pertinent to critical care nursing practice. A review of the literature identified that reflective practice appeared to be at the forefront of professional development. Background It could be argued that advancing practice in critical care has been superseded by the advanced practice agenda. Some would suggest that advancing practice is focused on the core attributes of an individual’s practice, which then leads onto advanced practice status. However, advancing practice is more of a process than identifiable skills and as such is often negated when viewing the development of practitioners to the advanced practice level. For example, practice development initiatives can be seen as advancing practice for the masses, which ensures that practitioners are following the same level and practice of care. The question here is, are they developing individually? Relevance to clinical practice What this paper presents is that reflection may not be best suited to advancing practice if the individual practitioner does not have a sound knowledge base both theoretically and experientially. The knowledge integration model presented in this study uses multiple learning strategies that are focused in practice to develop practice, e.g. the use of work-based learning and clinical supervision. To demonstrate the models application, an exemplar of an issue from practice shows its relevance from a practical perspective. Conclusions In conclusion, further knowledge acquisition and its relationship with previously held theory and experience will enable individual practitioners to advance their own practice as well as being a resource for others.

Efficient lattice (H)IBE in the standard model

We construct an efficient identity based encryption system based on the standard learning with errors (LWE) problem. Our security proof holds in the standard model. The key step in the construction is a family of lattices for which there are two distinct trapdoors for finding short vectors. One trapdoor enables the real system to generate short vectors in all lattices in the family. The other trapdoor enables the simulator to generate short vectors for all lattices in the family except for one. We extend this basic technique to an adaptively-secure IBE and a Hierarchical IBE.

A threshold concepts focus to curriculum design: supporting student learning through application of variation theory

This project develops and evaluates a model of curriculum design that aims to assist student learning of foundational disciplinary ‘Threshold Concepts’. The project uses phenomenographic action research, cross-institutional peer collaboration and the Variation Theory of Learning to develop and trial the model. Two contrasting disciplines (Physics and Law) and four institutions (two research-intensive and two universities of technology) were involved in the project, to ensure broad applicability of the model across different disciplines and contexts. The Threshold Concepts that were selected for curriculum design attention were measurement uncertainty in Physics and legal reasoning in Law. Threshold Concepts are key disciplinary concepts that are inherently troublesome, transformative and integrative in nature. Once understood, such concepts transform students’ views of the discipline because they enable students to coherently integrate what were previously seen as unrelated aspects of the subject, providing new ways of thinking about it (Meyer & Land 2003, 2005, 2006; Land et al. 2008). However, the integrative and transformative nature of such threshold concepts make them inherently difficult for students to learn, with resulting misunderstandings of concepts being prevalent...

Developing learning cohorts for postgraduate research degrees

Development of researchers through higher degree research studies is a high priority in most universities. Yet, research about supervision as pedagogy and models of supervision is only recently gained increasing attention. Charged with producing good researchers within very limited resources, academics are constantly looking for more efficient models of supervision for higher degree research students. A cohort model of supervision promises several efficiencies, but we argue that its success lies importantly on how well the cohort is developed specifically for higher degree research studies. We drew on a growing body of literature on higher degree research supervision to design, implement and evaluate our approach to developing a cohort of seven students enrolled in the Master of Education (Research) degree. Our approach included four provisions: initial residential workshop, development of a learning community, nourishing scholarship, and ongoing learning opportunities. The four provisions resulted in gradually developing an environment and culture that students found very supportive and nurturing. This paper is based on the findings from data collected from student evaluations in the first year of studies, feedback from the cohort’s sponsor, and our reflective notes. The evaluation substantiated the value in investing time and resources for purposely developing a cohort for higher degree research studies. Whether the cohorts are sponsored or not, universities will still need to invest time and resources for cohort development if a cohort model is intended to gain wider efficiencies in supervision of higher degree research students.

Enhancing student learning in the workplace through developing the leadership capabilities of clinical supervisors in the nursing discipline

The purpose of this project was to build the leadership capacity of clinical supervisors in the nursing discipline by developing, implementing and systematically embedding a leadership model into the structure and practice of student supervision. The University worked in partnership with three major metropolitan hospitals in Queensland to develop a framework and professional development program incorporating leadership and clinical supervision. The Leadership and Clinical Education (LaCE) program consisted of two structured workshops complemented by individual personal development projects undertaken by participants. Participants were supported in these activities with a purpose-built website that provides access to a wide variety of information and other learning resources. Quantitative and qualitative evaluations indicated that the approach was highly valued by participants, as it promoted useful peer dialogue, sharing of experiences and personal development in relation to assisting leadership development and student learning in the workplace. The LaCE program provides an ideal springboard for introducing the development of welltrained leaders into the clinical workplace. The resources developed have the potential to provide ongoing support for clinical supervisors to improve the learning of undergraduate nursing student. The challenge will be to achieve continued innovation within clinical education through sustainable leadership programs.

Educating for digital futures : what the learning strategies of digital media professionals can teach higher education

This article explores how universities might engage more effectively with the imperative to develop students’ 21st century skills for the information society, by examining learning challenges and professional learning strategies of successful digital media professionals. The findings of qualitative interviews with professionals from Australian games, online publishing, apps and software development companies reinforce an increasing body of literature that suggests that legacy university structures and pedagogical approaches are not conducive to learning for professional capability in the digital age. Study participants were ambivalent about the value of higher education to digital careers, in general preferring a range of situated online and face-to-face social learning strategies for professional currency. This article draws upon the learning preferences of the professionals in this study to present a model of 21st century learning, as linked with extant theory relating to informal, self-determined learning and communities of practice.

Near knowledge : inductive learning systems in law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.