Closing the loop 21st century style : providing feedback on written assessment via MP3 recordings

Feedback on student performance, whether in the classroom or on written assignments, enables them to reflect on their understandings and restructure their thinking in order to develop more powerful ideas and capabilities. Research has identified a number of broad principles of good feedback practice. These include the provision of feedback that facilitates the development of reflection in learning; helps clarify what good performance is in terms of goals, criteria and expected standards; provides opportunities to close the gap between current and desired performance; delivers high quality information to students about their learning; and encourages positive motivational beliefs and self-esteem. However, high staff–student ratios and time pressures often result in a gulf between this ideal and reality. Whilst greater use of criteria referenced assessment has enabled an improvement in the extent of feedback being provided to students, this measure alone does not go far enough to satisfy the requirements of good feedback practice. Technology offers an effective and efficient means by which personalised feedback may be provided to students. This paper presents the findings of a trial of the use of the freely available Audacity program to provide individual feedback via MP3 recordings to final year Media Law students at the Queensland University of Technology on their written assignments. The trial has yielded wide acclaim by students as an effective means of explaining the exact reasons why they received the marks they were awarded, the things they did well and the areas needing improvement. It also showed that good feedback practice can be achieved without the burden of an increase in staff workload.

Relevance feature discovery for text analysis

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.

State of Origin : Queensland Crown Leasehold - lessons from New South Wales

Throughout Australia freehold land interests are protected by statutory schemes which grant indefeasibility of title to registered interests. Queensland freehold land interests are protected by Torrens system established by the Land Title Act 1994. However, no such protection exists for Crown land interests. The extent of Queensland occupied under some form of Crown tenure, in excess of 70%, means that Queensland Crown land users are disadvantaged when compared to freehold land users. This article examines the role indefeasibility of title has in protecting interests in Crown land. A comparative analysis is undertaken between Queensland and New South Wales land management frameworks to determine whether interests in crown land are adequately protected in Queensland.

Music selection using a touch screen interface : effect of auditory and visual feedback on driving and usability

This study aimed to examine the effects on driving, usability and subjective workload of performing music selection tasks using a touch screen interface. Additionally, to explore whether the provision of visual and/or auditory feedback offers any performance and usability benefits. Thirty participants performed music selection tasks with a touch screen interface while driving. The interface provided four forms of feedback: no feedback, auditory feedback, visual feedback, and a combination of auditory and visual feedback. Performance on the music selection tasks significantly increased subjective workload and degraded performance on a range of driving measures including lane keeping variation and number of lane excursions. The provision of any form of feedback on the touch screen interface did not significantly affect driving performance, usability or subjective workload, but was preferred by users over no feedback. Overall, the results suggest that touch screens may not be a suitable input device for navigating scrollable lists.

Peer state assessment of TxDOT maintenance program and practices, part II : test section survey findings and recommendations

This paper presents a road survey as part of a workshop conducted by the Texas Department of Transportation (TxDOT) to evaluate and improve the maintenance practices of the Texas highway system. Directors of maintenance from six peer states (California, Kansas, Georgia, Missouri, North Carolina, and Washington) were invited to this 3-day workshop. One of the important parts of this workshop was a Maintenance Test Section Survey (MTSS) to evaluate a number of pre-selected one-mile roadway sections. The workshop schedule allowed half a day to conduct the field survey and 34 sections were evaluated. Each of the evaluators was given a booklet and asked to rate the selected road sections. The goals of the MTSS were to: 1. Assess the threshold level at which maintenance activities are required as perceived by the evaluators from the peer states; 2. Assess the threshold level at which maintenance activities are required as perceived by evaluators from other TxDOT districts; and 3. Perform a pilot evaluation of the MTSS concept. This paper summarizes the information obtained from survey and discusses the major findings based on a statistical analysis of the data and comments from the survey participants.

State convergence in the initialisation of the Sfinks stream cipher

Sfinks is a shift register based stream cipher designed for hardware implementation. The initialisation state update function is different from the state update function used for keystream generation. We demonstrate state convergence during the initialisation process, even though the individual components used in the initialisation are one-to-one. However, the combination of these components is not one-to-one.

Beyond the walls of academia : architectural internships

A full architectural education typically involves five years of formal education and two years of practice experience under the supervision of a registered architect. In many architecture courses some of this period of internship can be taken either as a ‘year out’ between years of study, or during enrolment as credited study; work place learning or work integrated learning. This period of learning can be characterised as an internship in which the student, as an adult learner, is supervised by their employer. This is a highly authentic learning environment, but one in which the learner is both student and employee, and the architect is both teacher and employer; at times conflicting roles. While the educational advantages of such authentic practice experience are well recognised, there are also concerns about the quality and variability of such experiences. This paper reviews the current state of practice, with respect to architectural internships, and analyses such practice using Laurillard’s ‘conversational framework’ (2002). The framework highlights the interactions and affordances between teacher and student in the form of concepts, adaptations, reflections, actions and feedback. A review of common practice in architectural work place learning, internships in other fields of education, and focused research at the author’s own university, are discussed, then analysed for ‘affordances’ of learning. Such analysis shows both the potential of work place learning to offer a unique environment for learning, and the need to organise and construct such experiences in ways that facilitates learning.

Current state of the application of infrared optical methods for assessing articular cartilage

This paper reviews the current state in the application of infrared methods, particularly mid-infrared (mid-IR) and near infrared (NIR), for the evaluation of the structural and functional integrity of articular cartilage. It is noted that while a considerable amount of research has been conducted with respect to tissue characterization using mid-IR, it is almost certain that full-thickness cartilage assessment is not feasible with this method. On the contrary, the relatively more considerable penetration capacity of NIR suggests that it is a suitable candidate for full-thickness cartilage evaluation. Nevertheless, significant research is still required to improve the specificity and clinical applicability of the method if we are going to be able to use it for distinguishing between functional and dysfunctional cartilage.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Integrated approach for HP-LNG pump diagnosis and prognostics based on health state probability estimation

This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

PySSM : a Python module for Bayesian inference of linear Gaussian state space models

PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models (SSM). PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries Numpy and Scipy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimised and parallelised Fortran routines. These Fortran routines heavily utilise Basic Linear Algebra (BLAS) and Linear Algebra Package (LAPACK) functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

Sensor fault-tolerant observer applied in satellite attitude control

The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control system (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.

Preliminary Research Findings : Volunteers in Queensland State Government

As 2001 was the International Year of the Volunteer as it seemed timely to look at the legal, social and political frameworks which provide for the long term growth of volunteers. The focus of this research is on the nature and extent of volunteers in the Queensland State Government. The social capital debate (expanded by Robert Putnam in 1995) is about citizens’ participation in extracurricular activities and has been extended to mean a collective intelligence – a capacity as a people to create the society we want. The volunteer phenomenon has been used to indicate social and ethical concern.

A novel CDVM based high-voltage converter using low power solid-state switches and a tuned resonant circuit designed for pulsed power applications

A novel concept of producing high dc voltage for pulsed-power applications is proposed in this paper. The topology consists of an LC resonant circuit supplied through a tuned alternating waveform that is produced by an inverter. The control scheme is based on the detection of variations in the resonant frequency and adjustment of the switching signal patterns for the inverter to produce a square waveform with exactly the same frequencies. Therefore the capacitor voltage oscillates divergently with an increasing amplitude. A simple one-stage capacitor-diode voltage multiplier (CDVM) connected to the resonant capacitor then rectifies the alternating voltage and gives a dc level equal to twice the input voltage amplitude. The produced high voltage appears then in the form of high-voltage pulses across the load. A basic model is simulated by Simulink platform of MATLAB and the results are included in the paper.