From tiers to tables – enhancing student experience through collaborative learning spaces

BACKGROUND Collaborative and active learning have been clearly identified as ways students can engage in learning with each other and the academic staff. Traditional tier based lecture theatres and the didactic style they engender are not popular with students today as evidenced by the low attendance rates for lectures. Many universities are installing spaces designed with tables for group interaction with evolutions on spaces such as the TEAL (Technology Enabled Active Learning) (Massachusetts Institute of Technology, n.d.) and SCALE-UP (Student-Centred Activities for Large-Enrolment Undergraduate Programs) (North Carolina State University, n.d.) models. Technology advances in large screen computers and applications have also aided the move to these collaborative spaces. How well have universities structured learning using these spaces and how have students engaged with the content, technology, space and each other? This paper investigates the application of collaborative learning in such spaces for a cohort of 800+ first year engineers in the context of learning about and developing professional skills representative of engineering practice. PURPOSE To determine whether moving from tiers to tables enhances the student experience. Does utilising technology rich, activity based, collaborative learning spaces lead to positive experiences and active engagement of first year undergraduate engineering students? In developing learning methodology and approach in new learning spaces, what needs to change from a more traditional lecture and tutorial configuration? DESIGN/METHOD A post delivery review and analysis of outcomes was undertaken to determine how well students and tutors engaged with learning in new collaborative learning spaces. Data was gathered via focus group and survey of tutors, students survey and attendance observations. The authors considered the unit delivery approach along with observed and surveyed outcomes then conducted further review to produce the reported results. RESULTS Results indicate high participation in the collaborative sessions while the accompanying lectures were poorly attended. Students reported a high degree of satisfaction with the learning experience; however more investigation is required to determine the degree of improvement in retained learning outcomes. Survey feedback from tutors found that students engaged well in the activities during tutorials and there was an observed improvement in the quality of professional practice modelled by students during sessions. Student feedback confirmed the positive experiences in these collaborative learning spaces with 30% improvement in satisfaction ratings from previous years. CONCLUSIONS It is concluded that the right mix of space, technology and appropriate activities does engage students, improve participation and create a rich experience to facilitate potential for improved learning outcomes. The new Collaborative Teaching Spaces, together with integrated technology and tailored activities, has transformed the delivery of this unit and improved student satisfaction in tutorials significantly.

Collaborative tagging in participatory webs : the death of information organisation?

Many organisations, companies and libraries started to use participatory webs to extend their services and engage more users. However, some librarians are still hesitated to implement participatory webs in their libraries, particularly in developing countries. This paper explores the advantages and disadvantages of participatory webs focusing on collaborative tagging. This paper draws from the literature of published articles discussing topics but not limited to participatory webs, participatory libraries, collaborative tagging, folksonomy and taxonomy. The advantages of implementation of the participatory webs in the library outweigh the disadvantages of it. Participatory webs do not necessarily mean the death of information organisation but it can supplement and improves information organisation in the library. This paper may help to broaden knowledge of LIS professionals in the implementation of participatory webs in the library.

Comparison of PCR, nested PCR, and random amplified polymorphic DNA PCR for detection and typing of Ureaplasma urealyticum in specimens from pregnant women

A PCR assay, using three primer pairs, was developed for the detection of Ureaplasma urealyticum, parvo biovar, mba types 1, 3, and 6, in cultured clinical specimens. The primer pairs were designed by using the polymorphic base positions within a 310- to 311-bp fragment of the 5* end and upstream control region of the mba gene. The specificity of the assay was confirmed with reference serovars 1, 3, 6, and 14 and by the amplified-fragment sizes (81 bp for mba 1, 262 bp for mba 3, and 193 bp for mba 6). A more sensitive nested PCR was also developed. This involved a first-step PCR, using the primers UMS-125 and UMA226, followed by the nested mba-type PCR described above. This nested PCR enabled the detection and typing of small numbers of U. urealyticum cells, including mixtures, directly in original clinical specimens. By using random amplified polymorphic DNA (RAPD) PCR with seven arbitrary primers, we were also able to differentiate the two biovars of U. urealyticum and to identify 13 RAPD-PCR subtypes. By applying these subtyping techniques to clinical samples collected from pregnant women, we established that (i) U. urealyticum is often a persistent colonizer of the lower genital tract from early midtrimester until the third trimester of pregnancy, (ii) mba type 6 was isolated significantly more often (P 5 0.048) from women who delivered preterm than from women who delivered at term, (iii) no particular ureaplasma subtype(s) was associated with placental infections and/or adverse pregnancy outcomes, and (iv) the ureaplasma subtypes most frequently isolated from women were the same subtypes most often isolated from infected placentas.

Damage detection in slab-on-girder bridges using vibration characteristics

This paper develops and applies a multi-criteria procedure, incorporating changes in natural frequencies, modal flexibility and the modal strain energy, for damage detection in slab-on-girder bridges. The proposed procedure is first validated through experimental testing of a model bridge. Numerically simulated modal data obtained through finite element analyses are then used to evaluate the vibration parameters before and after damage and used as the indices for assessment of the state of structural health. The procedure is illustrated by its application to full scale slab-on-girder bridges under different damage scenarios involving single and multiple damages on the deck and girders.

Developing a model for collaborative research

Collaboration between nurses in clinical and educational settings has been advocated as a means of ensuring nursing research is both practice oriented and scientifically valid. This paper describes a model, jointly developed by colleagues from the Nursing Departments of Alfred Hospital and La Trobe University, to foster collaborative research and steer research projects generated by clinical nurses from conceptualisation to publication.

Anomalous event detection using a semi-two dimensional hidden Markov model

The rapid increase in the deployment of CCTV systems has led to a greater demand for algorithms that are able to process incoming video feeds. These algorithms are designed to extract information of interest for human operators. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned `normal' model. Many researchers have tried various sets of features to train different learning models to detect abnormal behaviour in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM) to model the normal activities of people. The outliers of the model with insufficient likelihood are identified as abnormal activities. Our Semi-2D HMM is designed to model both the temporal and spatial causalities of the crowd behaviour by assuming the current state of the Hidden Markov Model depends not only on the previous state in the temporal direction, but also on the previous states of the adjacent spatial locations. Two different HMMs are trained to model both the vertical and horizontal spatial causal information. Location features, flow features and optical flow textures are used as the features for the model. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Comparison of antigen detection and quantitative PCR in the detection of chlamydial infection in koalas (Phascolarctos cinereus)

The gold standard method for detecting chlamydial infection in domestic and wild animals is PCR, but the technique is not suited to testing animals in the field when a rapid diagnosis is frequently required. The objective of this study was to compare the results of a commercially available enzyme immunoassay test for Chlamydia against a quantitative Chlamydia pecorum-specific PCR performed on swabs collected from the conjunctival sac, nasal cavity and urogenital sinuses of naturally infected koalas (Phascolarctos cinereus). The level of agreement for positive results between the two assays was low (43.2%). The immunoassay detection cut-off was determined as approximately 400 C. pecorum copies, indicating that the test was sufficiently sensitive to be used for the rapid diagnosis of active chlamydial infections.

Elaborated intrusion theory : a cognitive-emotional theory of food craving

A clear understanding of the cognitive-emotional processes underpinning desires to overconsume foods and adopt sedentary lifestyles can inform the development of more effective interventions to promote healthy eating and physical activity. The Elaborated Intrusion Theory of Desires offers a framework that can help in this endeavor through its emphases on the roles of intrusive thoughts and elaboration of multisensory imagery. There is now substantial evidence that tasks that compete for limited working memory resources with food-related imagery can reduce desires to eat that food, and that positive imagery can promote functional behavior. Meditation mindfulness can also short-circuit elaboration of dysfunctional cognition. Functional Decision Making is an approach that applies laboratory-based research on desire, to provide a motivational intervention to establish and entrench behavior changes, so healthy eating and physical activity become everyday habits.

Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

De-mystifying [a facet of] mobile learning : collaborative media in studio

“Our students have changed radically. Today’s students are no longer the people our educational system was designed to teach” (Prensky, 2001, p. 1). The influx of available new technology has helped to democratise knowledge, transforming when, where and how learning takes place, and changing perceptions of traditional learning landscapes (JISC, 2006; Neary et al., 2010). Mobile computers combined with wireless technology, have completely transformed the educational world; students have turned nomad[ic], engaging in conversations and thinking across traditional campus spaces (Alexander, 2004; Fisher, 2005). In this workshop we will be attempting to de-mystify a facet of mobile learning, by working in small groups to set up and kick start a number of social media sites, which can be used for collaboration and information exchange, in the design studio.

Collaborative 3D BPMN process modeller

A video detailing our new virtual world BPMN process modelling tool developed by Erik Poppe. Enables better situational awareness via use of remotely connected avatars and a shared 3D process diagram.

SERS substrate for detection of explosives

A novel gold coated femtosecond laser nanostructured sapphire surface – an “optical nose” - based on surface-enhanced Raman spectroscopy (SERS) for detecting vapours of explosive substances was investigated. Four different nitroaromatic vapours at room temperature were tested. Sensor responses were unambiguous and showed response in the range of 0.05 – 15 uM at 25 °C. The laser fabricated substrate nanostructures produced up to an eight-fold increase in Raman signal over that observed on the unstructured portions of the substrate. This work demonstrates a simple sensing system that is compatible with commercial manufacturing practices to detect taggants in explosives which can undertake as part of an integrated security or investigative mission.

SAIVT-QUT@TRECVid 2012 : interactive surveillance event detection

In this paper, we propose an approach which attempts to solve the problem of surveillance event detection, assuming that we know the definition of the events. To facilitate the discussion, we first define two concepts. The event of interest refers to the event that the user requests the system to detect; and the background activities are any other events in the video corpus. This is an unsolved problem due to many factors as listed below: 1) Occlusions and clustering: The surveillance scenes which are of significant interest at locations such as airports, railway stations, shopping centers are often crowded, where occlusions and clustering of people are frequently encountered. This significantly affects the feature extraction step, and for instance, trajectories generated by object tracking algorithms are usually not robust under such a situation. 2) The requirement for real time detection: The system should process the video fast enough in both of the feature extraction and the detection step to facilitate real time operation. 3) Massive size of the training data set: Suppose there is an event that lasts for 1 minute in a video with a frame rate of 25fps, the number of frames for this events is 60X25 = 1500. If we want to have a training data set with many positive instances of the event, the video is likely to be very large in size (i.e. hundreds of thousands of frames or more). How to handle such a large data set is a problem frequently encountered in this application. 4) Difficulty in separating the event of interest from background activities: The events of interest often co-exist with a set of background activities. Temporal groundtruth typically very ambiguous, as it does not distinguish the event of interest from a wide range of co-existing background activities. However, it is not practical to annotate the locations of the events in large amounts of video data. This problem becomes more serious in the detection of multi-agent interactions, since the location of these events can often not be constrained to within a bounding box. 5) Challenges in determining the temporal boundaries of the events: An event can occur at any arbitrary time with an arbitrary duration. The temporal segmentation of events is difficult and ambiguous, and also affected by other factors such as occlusions.

Exploring governance issues on collaborative contracts in the construction industry

This paper reports the findings of an in-depth literature review, which was designed as the first phase of a study that ultimately aims to rank the importance of key governance mechanisms on collaborative construction projects, in terms of impact on value-for-money. The absence of such information in the global knowledge base has prompted the current study. Seminal research completed recently concluded that deductive evidence with regard to the performance outcomes of collaborative procurement mechanisms is currently limited (Eriksson and Westerberg 2011). The authors aim to address this gap in current understanding. The literature review identifies key features of both formal and informal mechanisms which have been applied within collaborative contracting contexts. The literature review lays a solid foundation for designing a deductive research strategy to be implemented in the second phase of the study, which will employ a large-scale quantitative survey to shed light on the governance structures of collaborative contracts, and the ways in which they impact on realisation of VfM during project delivery in the Australian infrastructure industry. The current paper aims to identify the main categories of formal and informal governance mechanisms currently being employed globally. This will provide structure for the development of the survey in the second phase of the study.