Personality types and languages learning strategies: chameleons changing colours

This exploration of associations between the reported Language Learning Strategy (LLS) preferences of learners of English as a Second Language (ESL) and their personality types is positioned within the contention that the two are generally related. Our findings unequivocally support the existence of this relationship. Moreover, they also provide a platform from which to understand the contribution to learning a second language of two very commonly cited personality traits, introversion/extroversion and neuroticism. However, they also provide the basis for the important caution that the association between personality types and LLS is quite volatile. We have found that it is variation rather than unwavering stability that features in how personality traits apply as predictive of ESL learners' specific LLS preferences. Such prediction is specified even further by the particular contexts of ESL learning where the LLS are applied, for example for listening or speaking and whether this occurs inside or outside a classroom. The implications of these findings for ESL teaching and learning are discussed as is the explanatory power of the chameleon metaphor.

Developement and evidencing achievement of graduate learning outcomes in Deakin University's enhanced mooc

There is now a plethora of Massive Open On-line courses (MOOCs) offered worldwide. Whilst many MOOCs focus on discipline-specific content, little attention has been paid to how MOOCs can explicitly help participants develop generic employability skills such as communication, digital literacy, global citizenship and the like. Similarly little attention been paid to explicitly assuring the quality of MOOCs with respect to alignment with regulatory body standards. Deakin University's first MOOC, DeakinPrimer, is an introduction to humanitarian responses to 21st century disasters. It has been designed to assist participants to explicitly evidence generic or employability skills, some of Deakin's eight Graduate Learning Outcomes (GLOs) including communication, digital literacy, critical thinking and global citizenship. Other key features of DeakinPrimer include opportunities for networking with fellow participants and experts within the humanitarian field, and the opportunity to apply for credit towards the Graduate Certificate in International Community Development (level 8 in the Australian Qualifications Framework [AQF]) and for those with a prior Bachelor degree, the Masters in Humanitarian Assistance or the Masters of International Community Development (level 9 in the AQF). DeakinPrimer is designed as a test bed for a learning innovation, particularly micro-credentialing GLOs using digital badges to enable self and peer endorsement of evidence of learning. Badging is integrated in two ways. Firstly, DeakinPrimer participants build portfolios of learning artefacts associated with learning activities, then assess their work against a set of holistic, generic learning outcomes standards rubrics. If they judge their evidence as meeting the required standard, they can claim a badge (self endorsement) associated with particular GLOs. Secondly, participants can request and provide peer feedback and endorsement (using peer badges). The integration of self and peer review in the assessment tasks helps participants develop important employability skills, the ability to critically self-reflect on their own work and critically analyse the work of others and provide evidence-based feedback. DeakinPrimer is scheduled to commence in July 2013. This paper explains the way in which the course curricula has been designed to use technologies to enable participants to curate evidence of learning, and self and peer endorse such learning against defined standards.

The pedagogy of using video to develop reflective practice in learning to teach science

Purpose The purpose of this chapter is to identify the pedagogical approaches that foster critical reflection using video among the preservice teachers during tutorials. Methodology/approach The research is situated in a school-based teaching programme in which pairs of pre-service teachers taught small groups of primary aged children over a period of seven weeks. Volunteer pre-service teachers videotaped their lessons and selected video excerpts to share with their peers in the tutorial. The educator guided the preservice teachers’ reflection using the video. A case study drawing on interviews with pre-service teachers and audio recordings of tutorials, charted the development of pedagogical decisions made by the educators to promote reflection.Findings The pre-service teachers had difficulties undertaking deep reflection of their own and peers’ teaching practice. The response by educators was to promote collaboration among pre-service teachers by discussing specific aspects of the teaching in small groups and to use a jigsaw approach. This enabled a deeper analysis of particular elements of the lesson that were then integrated to produce a more holistic understanding of the teaching. The video data is most suitable for reflection and provides valuable evidence for pre-service teachers to develop their practice. Practical implications For pre-service teachers to develop effective skills to analyse their own practice they need to experience teaching in a safe but challenging environment, over a sustained period; have opportunities to develop a shared understanding of what constitutes quality teaching; have opportunities to critically analyse their teaching in discussion with peers and educators and be able to be guided by a framework of reflective strategies.

Deep ecology and Australian suburbia: learning from Aboriginal Australia philosophy

Naess’ Deep Ecology [50] represents a fundamental philosophical and conceptual shift from the dominant Western thinking that can be traced back to the Greek and Roman Empires. Like all philosophy, Naess’ Deep Ecology was born of and is most relevant to a specific time and place being northern Europe. Although the fundamentals of the Deep Ecology philosophy were new to modern Western thinking, it is not new to traditional Indigenous cultures, including the world’s oldest culture, that of Aboriginal Australia. While the past four decades has seen an increasing recognition of Aboriginal philosophical approaches, there is very little understanding of what this philosophical approach is and means for the management of the Australian environment in which humans are a central part. Since European arrival, Australia has been one of the world’s most urban societies. Unlike northern Europe, urban Australia is low density and suburban, a legacy of British and North American influences. Nearly 90% of Australians live in detached houses surrounded by gardens. Managed by individual residents, this land use accounts for about 70% of the total area of cities like Melbourne. Deeply culturally embedded, the Australian desire for living in low-density suburbs is unlikely to change soon. Contemporary cities are widely recognized as causing severe environmental degradation and are not sustainable. Yet in Australia introduced philosophical and design approaches are still used to address the unsustainable impacts of urban forms introduced from another time and place. While impractical to remove the existing suburban form in Australian cities, there is a significant opportunity to retrofit them using Australian Aboriginal philosophical and land management understandings developed and tested over tens of thousands of years. This paper establishes a contemporary Australian Deep Ecology philosophical approach to sustainably living in the suburbs that recognizes and works with the legacies of Australian Aboriginal, English, North American and contemporary Australian influences.

A machine learning approach to measure and monitor physical activity in children to help fight overweight and obesity

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.

The relation between receptive grammar and procedural, declarative, and working memory in specific language impairment

What memory systems underlie grammar in children, and do these differ between typically developing (TD) children and children with specific language impairment (SLI)? Whilst there is substantial evidence linking certain memory deficits to the language problems in children with SLI, few studies have investigated multiple memory systems simultaneously, examining not only possible memory deficits but also memory abilities that may play a compensatory role. This study examined the extent to which procedural, declarative, and working memory abilities predict receptive grammar in 45 primary school aged children with SLI (30 males, 15 females) and 46 TD children (30 males, 16 females), both on average 9;10 years of age. Regression analyses probed measures of all three memory systems simultaneously as potential predictors of receptive grammar. The model was significant, explaining 51.6% of the variance. There was a significant main effect of learning in procedural memory and a significant group × procedural learning interaction. Further investigation of the interaction revealed that procedural learning predicted grammar in TD but not in children with SLI. Indeed, procedural learning was the only predictor of grammar in TD. In contrast, only learning in declarative memory significantly predicted grammar in SLI. Thus, different memory systems are associated with receptive grammar abilities in children with SLI and their TD peers. This study is, to our knowledge, the first to demonstrate a significant group by memory system interaction in predicting grammar in children with SLI and their TD peers. In line with Ullman's Declarative/Procedural model of language and procedural deficit hypothesis of SLI, variability in understanding sentences of varying grammatical complexity appears to be associated with variability in procedural memory abilities in TD children, but with declarative memory, as an apparent compensatory mechanism, in children with SLI.

Cocaine-specific neuroplasticity in the ventral striatum network is linked to delay discounting and drug relapse

AIMS: To contrast functional connectivity on ventral and dorsal striatum networks in cocaine dependence relative to pathological gambling, via a resting-state functional connectivity approach; and to determine the association between cocaine dependence-related neuroadaptations indexed by functional connectivity and impulsivity, compulsivity and drug relapse. DESIGN: Cross-sectional study of 20 individuals with cocaine dependence (CD), 19 individuals with pathological gambling (PG) and 21 healthy controls (HC), and a prospective cohort study of 20 CD followed-up for 12 weeks to measure drug relapse. SETTING AND PARTICIPANTS: CD and PG were recruited through consecutive admissions to a public clinic specialized in substance addiction treatment (Centro Provincial de Drogodependencias) and a public clinic specialized in gambling treatment (AGRAJER), respectively; HC were recruited through community advertisement in the same area in Granada (Spain). MEASUREMENTS: Seed-based functional connectivity in the ventral striatum (ventral caudate and ventral putamen) and dorsal striatum (dorsal caudate and dorsal putamen), the Kirby delay-discounting questionnaire, the reversal-learning task and a dichotomous measure of cocaine relapse indicated with self-report and urine tests. FINDINGS: CD relative to PG exhibit enhanced connectivity between the ventral caudate seed and subgenual anterior cingulate cortex, the ventral putamen seed and dorsomedial pre-frontal cortex and the dorsal putamen seed and insula (P≤0.001, kE=108). Connectivity between the ventral caudate seed and subgenual anterior cingulate cortex is associated with steeper delay discounting (P≤0.001, kE=108) and cocaine relapse (P≤0.005, kE=34). CONCLUSIONS: Cocaine dependence-related neuroadaptations in the ventral striatum of the brain network are associated with increased impulsivity and higher rate of cocaine relapse.

Responding to the widening participation agenda through improved access to and within 3D virtual learning environments

The Australian Government's widening participation agenda - also referred to as the social inclusion agenda - considers equity through the triple focus of access, participation and outcomes. These foci are catalysts for re-examining teaching and learning approaches in formal education. This article considers this national refocus and the possibilities for addressing access and equity issues through and within threedimensional virtual learning environments (3DVLEs). The findings of an Australian Learning and Teaching Council (ALTC)-funded project that investigated the potential of an accessible 3DVLE for increasing access and participation of students with disabilities are reported, and strategies for improving outcomes (i.e. retention, success and completion) proposed. The article also highlights some of the remaining challenges with regard to the goal of improving outcomes for under-represented learner groups. The final section of the article identifies areas for further research.

Mealtime behaviour among children with disabilities: family interactions and stress

 The impact of feeding problems on parental stress, quality of mealtime interactions and opportunity for socialisation and naturalistic learning during mealtimes was examined in children with developmental disabilities and typically developing children through survey and qualitative analysis of video recordings of family mealtimes.

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

‘‘Model age-based” and ‘‘copy when uncertain” biases in children’s social learning of a novel task

Theoretical models of social learning predict that individuals can benefit from using strategies that specify when and whom to copy. Here the interaction of two social learning strategies, model age-based biased copying and copy when uncertain, was investigated. Uncertainty was created via a systematic manipulation of demonstration efficacy (completeness) and efficiency (causal relevance of some actions). The participants, 4- to 6-year-old children (N = 140), viewed both an adult model and a child model, each of whom used a different tool on a novel task. They did so in a complete condition, a near-complete condition, a partial demonstration condition, or a no-demonstration condition. Half of the demonstrations in each condition incorporated causally irrelevant actions by the models. Social transmission was assessed by first responses but also through children’s continued fidelity, the hallmark of social traditions. Results revealed a bias to copy the child model both on first response and in continued interactions. Demonstration efficacy and efficiency did not affect choice of model at first response but did influence solution exploration across trials, with demonstrations containing causally irrelevant actions decreasing exploration of alternative methods. These results imply that uncertain environments can result in canalized social learning from specific classes of mode