Intrinsic frames of reference in haptic spatial learning

It has been proposed that spatial reference frames with which object locations are specified in memory are intrinsic to a to-be-remembered spatial layout (intrinsic reference theory). Although this theory has been supported by accumulating evidence, it has only been collected from paradigms in which the entire spatial layout was simultaneously visible to observers. The present study was designed to examine the generality of the theory by investigating whether the geometric structure of a spatial layout (bilateral symmetry) influences selection of spatial reference frames when object locations are sequentially learned through haptic exploration. In two experiments, participants learned the spatial layout solely by touch and performed judgments of relative direction among objects using their spatial memories. Results indicated that the geometric structure can provide a spatial cue for establishing reference frames as long as it is accentuated by explicit instructions (Experiment 1) or alignment with an egocentric orientation (Experiment 2). These results are entirely consistent with those from previous studies in which spatial information was encoded through simultaneous viewing of all object locations, suggesting that the intrinsic reference theory is not specific to a type of spatial memory acquired by the particular learning method but instead generalizes to spatial memories learned through a variety of encoding conditions. In particular, the present findings suggest that spatial memories that follow the intrinsic reference theory function equivalently regardless of the modality in which spatial information is encoded.

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.

The SOLO Taxonomy as a means of shaping and assessing learning in higher education

This paper is a discussion of the use of the SOLO (Structure of Observed Learning Outcomes) Taxonomy (Biggs & Collis, 1982, 1989; Biggs, 1991, 1992a, 1992b; Boulton‐Lewis, 1992, 1994) as a means of developing and assessing higher order thinking in Higher Education. It includes a summary of the research into its use to date as an instrument to find out what students know and believe about their own learning, to assess entering knowledge in a discipline, to present examples of structural organization of knowledge in a discipline, to provide models of levels of desired learning outcomes, and in particular to assess learning outcomes. A proposal is made for further research.

The dynamics of expertise acquisition in sport : the role of affective learning design

Objectives The aim of this position paper is to discuss the role of affect in designing learning experiences to enhance expertise acquisition in sport. The design of learning environments and athlete development programmes are predicated on the successful sampling and simulation of competitive performance conditions during practice. This premise is captured by the concept of representative learning design, founded on an ecological dynamics approach to developing skill in sport, and based on the individual-environment relationship. In this paper we discuss how the effective development of expertise in sport could be enhanced by the consideration of affective constraints in the representative design of learning experiences. Conclusions Based on previous theoretical modelling and practical examples we delineate two key principles of Affective Learning Design: (i) the design of emotion-laden learning experiences that effectively simulate the constraints of performance environments in sport; (ii) recognising individualised emotional and coordination tendencies that are associated with different periods of learning. Considering the role of affect in learning environments has clear implications for how sport psychologists, athletes and coaches might collaborate to enhance the acquisition of expertise in sport.

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision.

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.

Learning Chinese as a heritage language in Australia and beyond : the role of capital

The benefits of learning and retaining heritage languages are well documented in the literature. Chinese heritage language learners’ commitment to their heritage language learning has gained significant research ground in social psychological and post-structural schools, with empirical evidence predominantly emerging from the North American contexts. There is scant sociological examination of similar problems conducted outside North America. The current study aims to make a contribution in this regard. The use of Bourdieu's sociological notion of capital in the Australian context complements the social psychological and post-structural work predominantly produced in the North American contexts. The initial quantitative sub-study analyses the impacts of various resources on the Chinese heritage language proficiency of 230 snowball-sampled Chinese Australian respondents to an online survey. The subsequent qualitative sub-study explores the profits of learning Chinese heritage language through interviews with a subset of the survey sample. Findings indicate that cultural, social, and symbolic capital significantly positively contributes to Chinese Australians’ Chinese heritage language proficiency, which, in return, produces profits in different forms of capital. The study suggests a reciprocal relationship between ‘capital’ and Chinese heritage language proficiency.

In and out of preschool learning with iPads

In this chapter we describe and explain the ways we negotiated these same epistemological tensions and structural realities as we implemented the iPad loan component of the project reported in this book (hereafter: “iPad loan program”).Children who participated in the iPad loan program were able to take home one of the project iPads used in their preschool centre, much as they were able to take home books and puzzles. This component was one reflection of the ethos of “digital inclusion” that infused the project. As we noted in the introduction to this book, there is international recognition of the role that schools can play in ensuring all communities can participate in digital culture and the digital economy (e.g., European Commission, 2014; United States Government, 2013). Accordingly, we conducted the project in preschool centres where at least some groups of children were thought to enjoy less access to learning on digital platforms than others. Our goal was to put the iPad into the hands of children who might not otherwise have had access to it, while supporting teachers and parents in capitalising on the learning potential of the device for all the children. Centres nominated for the project by administrators in the preschool system all served communities that were either affected by poverty and/or diverse in language and culture.

Moving forwards with the aim of going backwards fast : high performance rowing as a learning environment

Background and Purpose: - This paper focuses on the learning culture within the high performance levels of rowing. In doing so, we explore the case of an individual’s learning as he moves across athletic, coaching and administrative functions. This exploration draws on a cultural learning framework and complementary theorisings related to reflexivity. Method - This study makes use of an intellectually, morally and collaboratively challenging approach whereby one member of the research team was also the sole participant of this study. The participant’s careers as a high performance athlete, coach and administrator, coupled with his experience in conducting empirical research presented a rare opportunity to engage in collaborative research (involving degrees of insider and outsider status for each of the research team). We acknowledge that others have looked to combine roles of coach / athlete / administrator with that of researcher however few (if any) have attempted to combine them all in one project. Moreover, coupled with the approach to reflexivity adopted in this study and the authorship contributions we consider this scholarly direction uncommon. Data were comprised of recorded research conversations, a subsequently constructed learning narrative, reflections on the narrative, a stimulated reflective piece from the participant, and a final (re)construction of the participant’s story. Accordingly, data were integrated through an iterative process of thematic analysis. Results - The cultural (i.e., the ways things get done) and structural (e.g., the rules and regulations) properties of high performance rowing were found to shape both the opportunities to be present (e.g., secure a place in the crew) and to learn (e.g., learn the skills required to perform at an Olympic level). However, the individual’s personal properties were brought to bear on re-shaping the constraints such that many limitations could be overcome. In keeping with the theory of learning cultures, the culture of rowing was found to position individuals (a coxswain in this case) differentially. In a similar manner, a range of structural features was found to be important in shaping the cultural and personal elements in performance contexts. For example, the ‘field of play’ was found to be important as a structural feature (i.e., inability of coach to communicate with athletes) in shaping the cultural and personal elements of learning in competition (e.g., positioning the coxswain as an in-boat coach and trusted crewmate). Finally, the cultural and structural elements in rowing appeared to be activated by the participant’s personal elements, most notably his orientation towards quality performance. Conclusion - The participant in this study was found to be driven by the project that he cares about most and at each turn he has bent his understanding of his sport back on itself to see if he can find opportunities to learn and subsequently explore ways to improve performance. The story here emphasises the importance of learner agency, and this is an aspect that has often been missing in recent theorising about learning. In this study, we find an agent using his ‘personal emergent powers to activate the resources in the culture and structure of his sport in an attempt to improve performance. We conclude from this account that this particular high performance rowing culture is one that provided support but nonetheless encouraged those involved, to ‘figure things out’ for themselves – be it as athletes, coaches and/or administrators.

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Amygdala-dependent learning increases the number of spinophilin-immunoreactive dendritic spines in the lateral amygdala

During Pavlovian auditory fear conditioning a previously neutral auditory stimulus (CS) gains emotional significance through pairing with a noxious unconditioned stimulus (US). These associations are believed to be formed by way of plasticity at auditory input synapses on principal neurons in the lateral nucleus of the amygdala (LA). One proposed form of cellular plasticity involves structural changes in the number and morphology of dendritic spines...

Not learning from the past: Adaptive governance challenges for Australian natural resource management

Australia’s governance arrangements for NRM have evolved considerably over the last thirty years. The impact of changes in governance on NRM planning and delivery requires assessment. We undertake a multi-method program evaluation using adaptive governance principles as an analytical frame and apply this to Queensland to assess the impacts of governance change on NRM planning and governance outcomes. Data to inform our analysis includes: 1) a systematic review of sixteen audits/evaluations of Australian NRM over a fifteen-year period; 2) a review of Queensland’s first generation NRM Plans; and 3) outputs from a Queensland workshop on NRM planning. NRM has progressed from a bottom-up grassroots movement into a collaborative regional NRM model that has been centralised by the Australian Government. We found that while some adaptive governance challenges have been addressed, others remained unresolved. Results show that collaboration and elements of multi-level governance under the regional model were positive moves, but also that NRM arrangements contained structural deficiencies across multiple governance levels in relation to public involvement in decision-making and knowledge production for problem responsiveness. These problems for adaptive governance have been exacerbated since 2008. We conclude that the adaptive governance framework for NRM needs urgent attention so that important environmental management problems can be addressed.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Learning Chinese as a Heritage Language: An Australian Perspective

Summary: This book explores the fascinating topic of heritage language learning, looking in particular at Chinese Australians' learning of Chinese. The author studies the investment, challenges and benefits of heritage language learning across varied contexts including school, work, home and in the community. The book investigates how Chinese Australians navigate and negotiate their Chineseness and how resources are used to support their learning. The book is based on a mixed methods study which uses Bourdieu's sociological theory, and offers implications for sociologists of language and education, Chinese heritage language learners and teachers, as well as language and cultural policy makers. Review: This book is a compelling account of the habitus of Chineseness in a world of mobility. It offers up a plethora of insights into the implication of heritage language learning in the constitution of Chinese identity; it makes available a sophisticated mixed methods approach for using the thinking tools of Pierre Bourdieu; it adds to these tools a nuanced cultural dimension. Karen Dooley, Queensland University of Technology, Australia In our increasingly trans-migratory world, language can be central to cultural identity. Dr Mu’s research breaks new ground by adapting Bourdieu’s insights to examine how cultural identity (‘Chinese-ness’) is linguistically learned and practiced in Australia. His remarkable book will interest educators and researchers grappling with how language pertains to identity. Tom Strong, University of Calgary, Canada A thought-provoking, highly engaging work that has deftly shown how Bourdieusian framework can be applied in the research field of literacy education and Heritage Language learning. A must-read for those interested in overseas Chinese communities and Heritage Language learning across various immigrant communities in general! Liang Du, Beijing Normal University, China

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.