Client learning and the performance of collaborative infrastructure projects

Australia is a leading user of collaborative procurement methods, which are used to deliver large and complex infrastructure projects. Project alliances, Early Contractor Involvement (ECI), and partnering are typical examples of collaborative procurement models. In order to increase procurement effectiveness and value for money (VfM), clients have adopted various learning strategies for new contract development. However client learning strategies and behaviours have not been systematically analysed before. Therefore, the current paper undertakes a literature review addressing the research question “How can client learning capabilities be effectively understood?”. From the resource-based and dynamic capability perspectives, this paper proposes that the collaborative learning capability (CLC) of clients drives procurement model evolution. Learning routines underpinning CLC carry out exploratory, transformative and exploitative learning phases associated with collaborative project delivery. This learning improves operating routines, and ultimately performance. The conceptualization of CLC and the three sequential learning phases is used to analyse the evidence in the construction management literature. The main contribution of this study is the presentation of a theoretical foundation for future empirical studies to unveil effective learning strategies, which help clients to improve the performance of collaborative projects in the dynamic infrastructure market.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Effectiveness of on-line resources to enhance student learning and engagement

BACKGROUND For engineering graduates to be work-ready with marketable skills they must not only be well-versed with engineering science and its applications, but also able to adapt to using commercial software that is widely used in engineering practice. Hydrological/hydraulic modelling is one aspect of engineering practice which demands the ability to apply fundamentals into design and construction using software. The user manuals for such software are usually tailored for the experienced engineer but not for undergraduates who typically are novices to concepts of modelling and software tools. As the focus of a course such as Advanced Water Engineering is on the wider aspects of engineering application of hydrological and hydraulic concepts, it is ineffective for the lecturers to direct the students to user manuals as students have neither the time nor the desire to sift through numerous pages in a manual. An alternative and efficient way to demonstrate the use of the software is enabling students to develop a model to simulate real-world scenario using the tools of the software and directing them to make informed decisions based on outcomes. PURPOSE Past experience of the lecturer showed that the resources available for the students left a knowledge gap leading to numerous student queries outside contact hours. The purpose of this study is to assess how effective purpose-built video resources can be in supplementing the traditional learning resources to enhance student learning. APPROACH Short-length animated video clips comprising guided step-by-step instructions were prepared using screen capture software to capture screen activity and later edited to focus on specific features using pop-up annotations; Vocal narration was purposely excluded to avoid disturbances due to noise and allow different learning paces of individual students. The video clips were made available to the students alongside the traditional resources/approaches such as in-class demonstrations, guideline notes, and tips for efficient and error-free procedural descriptions. The number of queries the lecturer received from the student cohort outside the lecture times was recorded. An anonymous survey to assess the usefulness and adequacy of the courseware was conducted. OUTCOMES While a significant decline in the number of student queries was noted, an overwhelming majority of the survey respondents confirmed the usefulness of the purpose-developed courseware. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The survey and lecturer’s experience indicated that animated demonstration video clips illustrating the various steps involved in developing hydrologic and hydraulic models and simulating design scenarios is an effective supplement for traditional learning resources. Among the many advantages of the custom-made video clips as a learning resource are that they (1) highlight the aspects that are important to undergraduate learning but not available in the software manuals as the latter are designed for more mature users/learners; (2) provide short, to-the point communication in a step-by-step manner; (3) allow students flexibility to self-learn at their own pace; (4) enhance student learning; and (5) enable time savings for the lecturer in the long term by avoiding queries of a repetitive nature. It is expected that these newly developed resources will be improved to incorporate students’ suggestions before being offered to future cohorts of students. The concept can also be expanded to other relevant courses where animated demonstrations of key modelling steps are beneficial to student learning.

Investigating the effectiveness of an ecological approach to learning design in a first year mathematics for engineering unit

This paper reports on the results of a project aimed at creating a research-informed, pedagogically reliable, technology-enhanced learning and teaching environment that would foster engagement with learning. A first-year mathematics for engineering unit offered at a large, metropolitan Australian university provides the context for this research. As part of the project, the unit was redesigned using a framework that employed flexible, modular, connected e-learning and teaching experiences. The researchers, interested in an ecological perspective on educational processes, grounded the redesign principles in probabilistic learning design (Kirschner et al., 2004). The effectiveness of the redesigned environment was assessed through the lens of the notion of affordance (Gibson, 1977,1979, Greeno, 1994, Good, 2007). A qualitative analysis of the questionnaire distributed to students at the end of the teaching period provided insight into factors impacting on the successful creation of an environment that encourages complex, multidimensional and multilayered interactions conducive to learning.

The application of building information modeling to enhance organisational learning

The construction industry is a knowledge-based industry where various actors with diverse expertise create unique information within different phases of a project. The industry has been criticized by researchers and practitioners as being unable to apply newly created knowledge effectively to innovate. The fragmented nature of the construction industry reduces the opportunity of project participants to learn from each other and absorb knowledge. Building Information Modelling (BIM), referring to digital representations of constructed facilities, is a promising technological advance that has been proposed to assist in the sharing of knowledge and creation of linkages between firms. Previous studies have mainly focused on the technical attributes of BIM and there is little evidence on its capability to enhance learning in construction firms. This conceptual paper identifies six ‘functional attributes’ of BIM that act as triggers to stimulate learning: (1) comprehensibility; (2) predictability; (3) accuracy; (4) transparency; (5) mutual understanding and; (6) integration.

The use of hand-held mobile devices as aids to learning within flexible learning spaces in tertiary Information Technology classes

Mobile devices are very popular among tertiary student populations. This study looks at student use of hand-held mobile devices within the context of a first year programming unit. This research sought for ways in which an educational app on these devices could be successfully integrated into such a class's learning.

Let's do it: A framework to investigate the affordances of experimental learning environments

BACKGROUND Experimental learning, traditionally conducted in on-campus laboratory venues, is the cornerstone of science and engineering education. In order to ensure that engineering graduates are exposed to ‘real-world’ situations and attain the necessary professional skill-sets, as mandated by course accreditation bodies such as Engineers Australia, face-to-face laboratory experimentation with real equipment has been an integral component of traditional engineering education. The online delivery of engineering coursework endeavours to mimic this with remote and simulated laboratory experimentation. To satisfy student and accreditation requirements, the common practice has been to offer equivalent remote and/or simulated laboratory experiments in lieu of the ones delivered, face-to face, on campus. The current implementations of both remote and simulated laboratories tend to be specified with a focus on technical characteristics, instead of pedagogical requirements. This work attempts to redress this situation by developing a framework for the investigation of the suitability of different experimental educational environments to deliver quality teaching and learning. PURPOSE For the tertiary education sector involved with technical or scientific training, a research framework capable of assessing the affordances of laboratory venues is an important aid during the planning, designing and evaluating stages of face-to-face and online (or cyber) environments that facilitate student experimentation. Providing quality experimental learning venues has been identified as one of the distance-education providers’ greatest challenges. DESIGN/METHOD The investigation draws on the expertise of staff at three Australian universities: Swinburne University of Technology (SUT), Curtin University (Curtin) and Queensland University of Technology (QUT). The aim was to analyse video recorded data, in order to identify the occurrences of kikan-shido (a Japanese term meaning ‘between desks instruction’ and over-the-shoulder learning and teaching (OTST/L) events, thereby ascertaining the pedagogical affordances in face-to-face laboratories. RESULTS These will be disseminated at a Master Class presentation at this conference. DISCUSSION Kikan-shido occurrences did reflect on the affordances of the venue. Unlike with other data collection methods, video recorded data and its analysis is repeatable. Participant bias is minimised or even eradicated and researcher bias tempered by enabling re-coding by others. CONCLUSIONS Framework facilitates the identification of experiential face-to-face learning venue affordances. Investigation will continue with on-line venues.

Motivation and behavioural patterns of students accessing a peer-facilitated support for learning program

This paper describes the development and use of personas, a Human Computer Interaction (HCI) research methodology, within the STIMulate peer learning program, in order to better understand student behaviour patterns and motivations. STIMulate is a support for learning program at the Queensland University of Technology (QUT) in Brisbane, Australia. The program provides assistance in mathematics, science and information technology (IT) for course work students. A STIMulate space is provided for students to study and obtain one-on-one assistance from Peer Learning Facilitators (PLFs), who are experienced students that have excelled in relevant subject areas. This paper describes personas – archetypal users - that represent the motivations and behavioural patterns of students that utilise STIMulate (particularly the IT stream). The personas were developed based on interviews with PLFs, and subsequently validated by a PLF focus group. Seven different personas were developed. The personas enable us to better understand the characteristics of the students utilising the STIMulate program. The research provides a clearer picture of visiting student motivations and behavioural patterns. This has helped us identify gaps in the services provided, and be more aware of our assumptions about students. The personas have been deployed in PLF training programs, to help PLFs provide a better service to the students. The research findings suggest further study on the resonances between some students and PLFs, which we would like to better elicit.

Collaborative learning from distributed places and spaces: Exploring the use of virtual collaborative tools for local and global student teamwork projects

Educating responsive graduates. Graduate competencies include reliability, communication skills and ability to work in teams. Students using Collaborative technologies adapt to a new working environment, working in teams and using collaborative technologies for learning. Collaborative Technologies were used not simply for delivery of learning but innovatively to supplement and enrich research-based learning, providing a space for active engagement and interaction with resources and team. This promotes the development of responsive ‘intellectual producers’, able to effectively communicate, collaborate and negotiate in complex work environments. Exploiting technologies. Students use ‘new’ technologies to work collaboratively, allowing them to experience the reality of distributed workplaces incorporating both flexibility and ‘real’ time responsiveness. Students are responsible and accountable for individual and group work contributions in a highly transparent and readily accessible workspace. This experience provides a model of an effective learning tool. Navigating uncertainty and complexity. Collaborative technologies allows students to develop critical thinking and reflective skills as they develop a group product. In this forum students build resilience by taking ownership and managing group work, and navigating the uncertainties and complexities of group dynamics as they constructively and professionally engage in team dialogue and learn to focus on the goal of the team task.

Making an IMPACT: Campus-wide collaboration for course and learning space transformation

At Purdue University, the Libraries participate in a provost-initiated, campus-wide course redesign program called Instruction Matters: Purdue Academic Course Transformation (IMPACT). This initiative aims to bring active-learning to foundational courses traditionally taught through lectures. Purdue librarians recognized the IMPACT initiative as one way to enter the conversations blooming on our campus about the nature of learning, curriculum design, and how space design impacts potential learning. This article presents three perspectives: 1) the information literacy coordinator, 2) a libraries’ administrator with a gift for space planning, and; 3) an in-the-trenches liaison to course redesign projects. Each discusses the IMPACT initiative from his or her unique perspective and view of its impact on librarian roles. Collectively, the article explains why we think it is essential that this kind of campus effort is supported by libraries.

Rethinking Culture and Diversity in Early Childhood Preservice Teacher Education: Looking at Teaching and Learning Through an Interdisciplinary Lens

Early Childhood Education (ECE) has a long history of building foundations for children to achieve their full potential, enabling parents to participate in the economy while children are cared for, addressing poverty and disadvantage, and building individual, community and societal resources. In so doing, ECE has developed a set of cultural practices and ways of knowing that shape the field and the people who work within it. ECE, consequently, is frequently described as unique and special (Moss, 2006; Penn, 2011). This works to define and distinguish the field while, simultaneously, insulating it from other contexts, professions, and ideas. Recognising this dualism illuminates some of the risks and challenges of operating in an insular and isolated fashion. In the 21st century, there are new challenges for children, families and societies to which ECE must respond if it is to continue to be relevant. One major issue is how ECE contributes to transition towards more sustainable ways of living. Addressing this contemporary social problem is one from which Early Childhood teacher education has been largely absent (Davis & Elliott, 2014), despite the well recognised but often ignored role of education in contributing to sustainability. Because of its complexity, sustainability is sometimes referred to as a ‘wicked problem’ (Rittel & Webber, 1973; Australian Public Service Commission, 2007) requiring alternatives to ‘business as usual’ problem solving approaches. In this chapter, we propose that addressing such problems alongside disciplines other than Education enables the Early Childhood profession to have its eyes opened to new ways of thinking about our work, potentially liberating us from the limitations of our “unique” and idiosyncratic professional cultures. In our chapter, we focus on understandings of culture and diversity, looking to broaden these by exploring the different ‘cultures’ of the specialist fields of ECE and Design (in this project, we worked with students studying Architecture, Industrial Design, Landscape Architecture and Interior Design). We define culture not as it is typically represented, i.e. in relation to ideas and customs of particular ethnic and language groups, but to the ideas and practices of people working in different disciplines and professions. We assert that different specialisms have their own ‘cultural’ practices. Further, we propose that this kind of theoretical work helps us to reconsider ways in which ECE might be reframed and broadened to meet new challenges such as sustainability and as yet unknown future challenges and possibilities. We explore these matters by turning to preservice Early Childhood teacher education (in Australia) as a context in which traditional views of culture and diversity might be reconstructed. We are looking to push our specialist knowledge boundaries and to extend both preservice teachers and academics beyond their comfort zones by engaging in innovative interdisciplinary learning and teaching. We describe a case study of preservice Early Childhood teachers and designers working in collaborative teams, intersecting with a ‘real-world’ business partner. The joint learning task was the design of an early learning centre based on sustainable design principles and in which early Education for Sustainability (EfS) would be embedded Data were collected via focus group and individual interviews with students in ECE and Design. Our findings suggest that interdisciplinary teaching and learning holds considerable potential in dismantling taken-for-granted cultural practices, such that professional roles and identities might be reimagined and reconfigured. We conclude the chapter with provocations challenging the ways in which culture and diversity in the field of ECE might be reconsidered within teacher education.

Designing informal learning experiences for early career academics using a knowledge ecosystem model

This article presents a ‘knowledge ecosystem’ model of how early career academics experience using information to learn while building their social networks for developmental purposes. Developed using grounded theory methodology, the model offers a way of conceptualising how to empower early career academics through 1) agency (individual and relational) and 2) facilitation of personalised informal learning (design of physical and virtual systems and environments) in spaces where developmental relationships are formed including programs, courses, events, community, home and social media. It is suggested that the knowledge ecosystem model is suitable for use in designing informal learning experiences for early career academics.

Indonesian library and information science professionals’ understanding of Library 2.0: A pilot study

The literature around Library 2.0 remains largely theoretical with few empirical studies and is particularly limited in developing countries such as Indonesia. This study addresses this gap and aims to provide information about the current state of knowledge on Indonesian LIS professionals’ understanding of Library 2.0. The researchers used qualitative and quantitative approaches for this study, asking thirteen closed- and open-ended questions in an online survey. The researchers used descriptive and in vivo coding to analyze the responses. Through their analysis, they identified three themes: technology, interactivity, and awareness of Library 2.0. Respondents demonstrated awareness of Library 2.0 and a basic understanding of the roles of interactivity and technology in libraries. However, overreliance on technology used in libraries to conceptualize Library 2.0 without an emphasis on its core characteristics and principles could lead to the misalignment of limited resources. The study results will potentially strengthen the research base for Library 2.0 practice as well as inform LIS curriculum in Indonesia so as to develop practitioners who are able to adapt to users’ changing needs and expectations. It is expected that the preliminary data from this study could be used to design a much larger and more complex future research project in this area.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.