Integration of technical equipment in a project driven learning environment

Purpose: This paper describes the integration of technical equipment in a project driven learning environment in the School of Engineering in the Faculty of Science, Engineering and Built Environment at Deakin University, Geelong, Australia. Technical or laboratory equipment is a critical factor when designing learning environments and more so in a project driven learning environment. Important Findings: Deakin University has strong partnerships with industry and the community and with its cloud and located based learning policy has extremely flexible learning environments tailored to the needs of the students, with all the programs being offered in on-campus mode as well as off-campus mode. The off-campus study mode has made it even more important to have flexibility in the usage and access of the technical equipment in the laboratories. Conclusion: The School of Engineering at Deakin University Australia has developed a project-oriented design based learning environment which allows students to learn through design activities while being driven by the deliverables and outputs of a project. The technical equipment is required to be able to be used for traditional laboratory experiments in order to achieve fundamental knowledge requirements as well as project oriented knowledge and practice.

Infusing instructional design principles into an active student-centred social learning framework

Social and participatory media offer a plethora of ways for students to communicate, collaborate, and learn in schools. Using a social learning approach, Casey (2013a) investigated ways that social media could be integrated into Australian public high school classrooms to enhance student learning. In the process, she developed a social learning framework as discussed in Casey (2013b). Similarly, Davidson-Shivers and Hulon (2013; Hulon & Daidson-Shivers, 2013) suggest ways to employ ID principles to prepare college instructors and pre-service teachers to integrate technology into classrooms. Prior to that, Davidson-Shivers with Rasmussen (2006) developed an instructional design (ID) model for creating effective Web-based learning environments. Through collaboration, Casey and Davidson-Shivers consider a wide range of social learning and instructional design principles and approaches to help develop frameworks for new media integration that can work within varying levels of education.

Developing creative citizens through experiential learning environments in applied drama

The Seoul Agenda (2010, p.8) recognizes the value of arts education in enhancing creative and innovative capacity in young people. It goes so far as to suggest that applying arts will “cultivate a new generation of creative citizens”. This paper documents a specific area of arts education in university level drama degrees. In a case study approach, it discusses the outcomes of a work-based learning approach for students of applied drama. It explores the drama student‟s experience and considers how engaging in the study of applied drama and applied performance and having the support and opportunity to transfer these skills in real contexts acts to develop creative capacity and to contribute to consolidating the students‟ identities as citizens

The challenges of blended learning using a media annotation tool

Blended learning has been evolving as an important approach to learning and teaching in tertiary education. This approach incorporates learning in both online and face-to-face modes and promotes deep learning by incorporating the best of both approaches. An innovation in blended learning is the use of an online media annotation tool (MAT) in combination with face-to-face classes. This tool allows students to annotate their own or teacher-uploaded video adding to their understanding of professional skills in various disciplines in tertiary education. Examination of MAT occurred in 2011 and included nine cohorts of students using the tool. This article canvasses selected data relating to MAT including insights into the use of blended learning focussing on the challenges of combining face-to-face and online learning using a relatively new online tool.

Learning latent activities from social signals with hierarchical dirichlet processes

Understanding human activities is an important research topic, most noticeably in assisted-living and healthcare monitoring environments. Beyond simple forms of activity (e.g., an RFID event of entering a building), learning latent activities that are more semantically interpretable, such as sitting at a desk, meeting with people, or gathering with friends, remains a challenging problem. Supervised learning has been the typical modeling choice in the past. However, this requires labeled training data, is unable to predict never-seen-before activity, and fails to adapt to the continuing growth of data over time. In this chapter, we explore the use of a Bayesian nonparametric method, in particular the hierarchical Dirichlet process, to infer latent activities from sensor data acquired in a pervasive setting. Our framework is unsupervised, requires no labeled data, and is able to discover new activities as data grows. We present experiments on extracting movement and interaction activities from sociometric badge signals and show how to use them for detecting of subcommunities. Using the popular Reality Mining dataset, we further demonstrate the extraction of colocation activities and use them to automatically infer the structure of social subgroups. © 2014 Elsevier Inc. All rights reserved.

Interprofessional learning at work: what spatial theory can tell us about workplace learning in an acute care ward

It is widely recognized that every workplace potentially provides a rich source of learning. Studies focusing on health care contexts have shown that social interaction within and between professions is crucial in enabling professionals to learn through work, address problems and cope with challenges of clinical practice. While hospital environments are beginning to be understood in spatial terms, the links between space and interprofessional learning at work have not been explored. This paper draws on Lefebvre’s tri-partite theoretical framework of perceived, conceived and lived space to enrich understandings of interprofessional learning on an acute care ward in an Australian teaching hospital. Qualitative analysis was undertaken using data from observations of Registered Nurses at work and semi-structured interviews linked to observed events. The paper focuses on a ward round, the medical workroom and the Registrar’s room, comparing and contrasting the intended (conceived), practiced (perceived) and pedagogically experienced (lived) spatial dimensions. The paper concludes that spatial theory has much to offer understandings of interprofessional learning in work, and the features of work environments and daily practices that produce spaces that enable or constrain learning.

Preference Networks: probabilistic models for recommendation systems

Abstract
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional
Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

A hybrid FAM–CART model and its application to medical data classification

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability–plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM–CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM–CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability–plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM–CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM–CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

Challenges to providing work integrated learning to international business students at Australian universities

Purpose – The purpose of this paper is to report on a three-year Australian study of international business and accounting students and the transition to employment. For international students seeking to differentiate themselves in a highly competitive global labour market, foreign work experience is now an integral part of the overseas study “package”. Work-integrated learning (WIL) is seen to provide critical “employability” knowledge and skills, however, international students have low participation rates. The high value placed on WIL among international students poses challenges for Australia as well as opportunities. Understanding the issues surrounding international students and WIL is closely linked to Australia’s continued success in the international education sector which has broad, long-term, social and economic implications.Design/methodology/approach – This paper draws on 59 interviews with a range of stakeholders including international students, universities, government, employers and professional bodies. Central to the paper is an in-depth case study of WIL in the business and accounting discipline at one Australian university.Findings – Providing international students with access to discipline-related work experience has emerged as a critical issue for Australian universities. The study finds that enhancing the employability skills of internationals students via integrated career education, a focus on English language proficiency and “soft skills” development are central to success in WIL. Meeting the growing demand for WIL among international students requires a multipronged approach which hinges on cooperation between international students, universities, employers and government.Originality/value – This project aims to fill a critical knowledge gap by advancing theories in relation to international students and WIL. While there is a significant body of research in the fields of international education and WIL, there is an absence of research exploring the intersection between the two fields. The study will contribute to the advancement of knowledge in both fields by exploring the emerging issue of WIL and international students.

Locating learning: making connections between virtual and physical spaces with mobile touch-screen devices

This paper is concerned with the potential of mobile touch-screen devices and emerging socio-technological practices to support pedagogies of place that provide a means for young people to reflect critically on the social construction of place and to take actions that speak of and to their own locatedness. Drawing on de Certeau's (1984) concept of space as a practiced place and Massey's (2005) perspective of spatiality and interrelatedness, we examine two school-based examples of learning activities that bring together the virtual and physical as in experiences and representations of place. The first example is an Australian local history unit, where lower secondary school students participated in a series of field trips, planned and conducted under the guidance of an indigenous elder. They used Smartphones and iPads to capture and create personalised audio-visual records of their knowledge of place that were then used to create geo-location games. In the second example, upper primary school students worked with local authorities and environmental educators to select sites for two environmental monitoring posts, which were then installed and provided a locus for the students' school-based environmental science learning as well as a vehicle for community engagement. Drawing on interview, video and photographic data, this paper examines the way mobile technologies were deployed for student knowledge production, engagement with place, reconstruction of place and engagement with community.

Towards anomalous diffusion sources detection in a large network

Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.