Sequential versus simultaneous viewing of an environment : effects of focal attention to individual object locations on visual spatial learning

We investigated memories of room-sized spatial layouts learned by sequentially or simultaneously viewing objects from a stationary position. In three experiments, sequential viewing (one or two objects at a time) yielded subsequent memory performance that was equivalent or superior to simultaneous viewing of all objects, even though sequential viewing lacked direct access to the entire layout. This finding was replicated by replacing sequential viewing with directed viewing in which all objects were presented simultaneously and participants’ attention was externally focused on each object sequentially, indicating that the advantage of sequential viewing over simultaneous viewing may have originated from focal attention to individual object locations. These results suggest that memory representation of object-to-object relations can be constructed efficiently by encoding each object location separately, when those locations are defined within a single spatial reference system. These findings highlight the importance of considering object presentation procedures when studying spatial learning mechanisms.

Path information effects in visual and proprioceptive spatial learning

Objects in an environment are often encountered sequentially during spatial learning, forming a path along which object locations are experienced. The present study investigated the effect of spatial information conveyed through the path in visual and proprioceptive learning of a room-sized spatial layout, exploring whether different modalities differentially depend on the integrity of the path. Learning object locations along a coherent path was compared with learning them in a spatially random manner. Path integrity had little effect on visual learning, whereas learning with the coherent path produced better memory performance than random order learning for proprioceptive learning. These results suggest that path information has differential effects in visual and proprioceptive spatial learning, perhaps due to a difference in the way one establishes a reference frame for representing relative locations of objects.

Brain decoding based on functional magnetic resonance imaging using machine learning : a comparative study

Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

Standards that we can 'own' : Threshold Learning Outcomes for Journalism, Public Relations, and Media & Communication Studies

This paper outlines the progress by the JoMeC (Journalism, Media & Communication) Network in developing TLO (Threshold Learning Outcome) statements for Bachelor-level university programs in the disciplines of Journalism, Public Relations and Media & Communications Studies. The paper presents the finalised TLO statement for Journalism, and outlines moves to engage discipline-based groups to further develop preliminary TLOs for Public Relations and Media & Communication Studies. The JoMeC Network was formed in 2011, in response to requirements that from 2014 all degrees and qualifications at Australian universities would be able to demonstrate that they comply with the threshold learning standards set by the Australian Qualifications Framework (AQF). The AQF’s threshold standards define the minimum types and levels of knowledge, skills and capabilities that a student must demonstrate in order to graduate. The Tertiary Education Quality and Standards Agency (TEQSA) will use the AQF’s threshold standards as a key tool in recording and assessing the performance of higher educational institutions, and determining whether they should be registered as Australian Higher Education Providers under the Higher Education Standards Framework. The Office of Learning & Teaching (OLT) places the onus on discipline communities to collaborate in order to develop and ‘own’ the threshold learning standards that can be considered the minimum learning outcomes of university-level programs in that field. With the support of an OLT Grant, the JoMeC Network’s prime goal has been to develop three sets of discipline-specific TLOs – one each for the Journalism, Public Relations, and Media & Communications Studies disciplines. This paper describes the processes of research, consultation, drafting and ongoing revision of the TLO for Journalism. It outlines the processes that the JoMeC Network has taken in developing a preliminary TLO draft to initiate discussion of Public Relations and Media & Communication Studies. The JoMeC Network plans to hand management of further development of these TLOs to scholars within the discipline who will engage with academics and other stakeholders to develop statements that the respective disciplines can embrace and ‘own’.

Achieving superior SME performance : overarching role of marketing, innovation, and learning capabilities

While most studies examine the effect of marketing, innovation, and learning capabilities (often separately) on performance, this study develops a unified model to investigate the combined effect of these capabilities on performance. This study further examines the complementary effect of these capabilities on performance. This study draws on the resource-based view theory to examine 171 manufacturing SMEs. The findings suggest that marketing, innovation, and learning capabilities are positively related to SME performance. In addition, these capabilities interact with one another to create great synergy in achieving SME performance.

Doctoral research training : encouraging timely completion – case study reflections of one university on how it supports a vibrant research training culture

This is a case study of a young university striving to generate and sustain a vibrant Research Training culture. The university’s research training framework is informed by a belief in a project management approach to achieving successful research candidature. This has led to the definition and reporting of key milestones during candidature. In turn, these milestones have generated a range of training programs to support Higher Degree Research (HDR) students to meet these milestones in a timely fashion. Each milestone focuses on a specific set of skills blended with supporting the development of different parts of the doctoral thesis. Data on student progress and completion has provided evidence in highlighting the role that the milestones and training are playing in supporting timely completion. A university-wide reporting cycle generated data on the range of workshops and training provided to Higher Degree Research students and supervisors. The report provided details of thesis topic and format, as well as participation in research training events and participant evaluation of those events. Analysis of the data led to recommendations and comments on the strengths and weaknesses of the current research training program. Discussion considered strategies and drivers for enhancements into the future. In particular, the paper reflects on the significant potential role of centrally curated knowledge systems to support HDR student and supervisor access, and engagement and success. The research training program was developed using blended learning as a model. It covered face-to-face workshops as well as online modules. These were supplemented by web portals that offered a range of services to inform and educate students and supervisors and included opportunities for students to interact with each other. Topics ranged from the research life cycle, writing and publication, ethics, managing research data, managing copyright, and project management to use of software and the University’s Code of Conduct for Research. The challenges discussed included: How to reach off campus students and those studying in external modes? How best to promote events to potential participants? How long and what format is best for face-to-face sessions? What online resources best supplement face-to-face offerings? Is there a place for peer-based learning and what form should this take? These questions are raised by a relatively young university seeking to build and sustain a vibrant research culture. The rapid growth in enrolments in recent years has challenged previous one-to-one models of support. This review of research training is timely in seeking strategies to address changing research training support capacity and student needs. Part of the discussion will focus on supervisory training, noting that good supervision is the one remaining place where one-to-one support is provided. Ensuring that supervisors are appropriately equipped to address student expectations is considered in the context of the research training provisions. The paper concludes with reflection on the challenges faced, and recommended ways forward as the number of research students grows into the future.

From policy borrowing to implementation : an illuminative evaluation of learning and teaching in higher education in Australia (2002 to 2008)

This study documents and theorises the consequences of the 2003 Australian Government Reform Package focussed on learning and teaching in Higher Education during the period 2002 to 2008. This is achieved through the perspective of program evaluation and the methodology of illuminative evaluation. The findings suggest that the three national initiatives of that time, Learning and Teaching Performance Fund (LTPF), Australian Learning and Teaching Council (ALTC), and Australian Universities Quality Agency (AUQA), were successful in repositioning learning and teaching as a core activity in universities. However, there were unintended consequences brought about by international policy borrowing, when the short-lived nature of LTPF suggests a legacy of quality compliance rather than one of quality enrichment.

Tangible E-Learning

Tangible User Interfaces increasingly gain attention for their supportive potential in cognitive processes. More and more often the terms e-Learning and tangible interaction are been referred to in one word as Tangible e-Learning. This paper gives a general overview on the topic in reference to development history, research and effectiveness. It explains how epistemology, together with its idea that physicality enhances learning and that the cognitive process can happen in expressive or exploratory manner, motivates the emerging of TUIs and acts as a basis for the classification of Tangible e-Learning Systems. The benefits of TUIs in comparison to classical GUIs in terms of their contribution to the learning process, engagement, enjoyment and collaboration are empirically proven, although poorly. Nevertheless, it is not known whether TUIs have stronger potential than common Physical User Interfaces that are not electronically augmented. Still, TUIs that support learning have a strong potential to be integrated into real world scenarios.

Learning from Eames : six lessons

"Eventually everything connects—people, ideas, objects. The quality of the connections is the key toquality per se.” (Charles Eames) On 8 November, 2007, in a moment charged with serendipity, an Exhibition titled ‘The Gifted Eye of Charles Eames—A Portfolio of 100 images’ was opened exclusively to Brisbane. The Artisan Gallery in Fortitude Valley became the launch point for an international orbit of Fringe locations hosting one of 18 sets of 100 images each...

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Incorporating anchored learning in a C# intelligent tutoring system

This research showed that one solution that can be used to help the students learn how to program is by providing a system that can behave like a tutor to teach the students individually. An intelligent tutoring system named CSTutor was built in this research to assist the students. CSTutor asks the student to write programs in a role playing environment, presenting the most appropriate tasks to the students, and provides help to the students' problems.

Transforming the core business of teaching and learning in classrooms through ICT

Information and Communication Technology (ICT) has become an integral part of societies across the globe. This study demonstrates how successful technology integration by 10 experienced teachers in an Australian high school was dependent on teacher-driven change and innovation that influenced the core business of teaching and learning. The teachers were subject specialists across a range of disciplines, engaging their Year Eight students (aged 12–14 years) in the Technology Rich Classrooms programme. Two classrooms were renovated to accommodate the newly acquired computer hardware. The first classroom adopted a one-to-one desktop model with all the computers with Internet access arranged in a front-facing pattern. The second classroom had computers arranged in small groups. The students also used Blackboard to access learning materials after school hours. Qualitative data were gathered from teachers mainly through structured and unstructured interviews and a range of other approaches to ascertain their perceptions of the new initiative. This investigation showed that ICT was impacting positively on the core business of teaching and learning. Through the support of the school leadership team, the built environment was enabling teachers to use ICT. This influenced their pedagogical approaches and the types of learning activities they designed and implemented. As a consequence, teachers felt that students were motivated and benefited through this experience.

Reterritorializing multimodal design in the classroom through a socially mediated Indigenous heritage

National or International Significance Flows of cultural heritage in textual practices are vital to sustaining Indigenous communities - a national and international priority (Commonwealth of Australia, 2011). Indigenous heritage, whether passed on by oral tradition or ubiquitous social media, can be seen as a "conversation between the past and the future" (Fairclough, 2012, p. xv). Indigenous heritage involves appropriating memories within a cultural flow to pass on a spiritual legacy. This presentation reports ethnographic research of social media practices in a small independent Aboriginal school in Southeast Queensland, Australia that is resided over by the Yuggera elders and an Aboriginal principal. Quality of Research The purpose was to rupture existing notions of white literacies in schools, and to deterritorialize the uses of digital media by dominant cultures in the public sphere. Examples of learning experiences included the following: i. Integrating Indigenous language and knowledge into media text production; ii. Classroom visits from Indigenous elders; and iii. Publishing oral histories through digital scrapbooking. The program aligned with the Australian National Curriculum English (ACARA, 2014), which mandates the teaching of multimodal text creation. Data sources included a class set of digital scrapbooks collaboratively created in a preparatory-one primary classroom. The digital scrapbooks combined digitally encoded words, images of material artifacts, and digital music files. A key feature of the writing and digital design task was to retell and digitally display and archive a cultural narrative of significance to the Indigenous Australian community and its memories and material traces of the past for the future. Data analysis of the students' digital stories involved the application of key themes of negotiated, material, and digitally mediated forms of heritage practice. It drew on Australian Indigenous research by Keddie et al. (2013) to guard against the homogenizing of culture that can arise from a focus on a static view of culture. The interpretation of findings located Indigenous appropriation of social media within broader racialized politics that enables Indigenous literacy to be understood as a dynamic, negotiated, and transgenerational flows of practice. It demonstrates that Indigenous children's use of media production reflects "shifting and negotiated identities" in response to changing media environments that can function to sustain Indigenous cultural heritages (Appadurai, 1696, p. xv). Impact on practice, policy or theory The findings are important for teachers at a time when Aboriginal and Torres Strait Islander Histories and Cultures is a cross-curricular policy priority in the Australian Curriculum (ACARA, 2014). The findings show how curriculum policies can be applied to classroom practice in ways that are epistemologically consistent with Indigenous ways of knowing and being. Theoretically, it demonstrates how the children's experiences of culture are layered over time, as successive generations inherit, interweave, and hear others' cultural stories or maps. Practically, recommendations are provided for an approach to appropriating social media in schools that explicitly attends to the dynamic nature of Indigenous practices, negotiated through intercultural constructions and flows, and opening space for a critical anti-racist approach to multimodal text production. Timeliness The research is timely in the context of the accessibility and role of digital and multimodal forms of communication, including for Aboriginal and Torres Strait Islander communities.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.