A PHENOMENOLOGY OF MIMETIC LEARNING AND MULTIMODAL COGNITION: INTEGRATING EXPERIENTIAL KNOWLEDGE INTO PROGRAMS IN RHETORIC, COMPOSITION, AND TECHNICAL COMMUNICATION

My dissertation emphasizes a cognitive account of multimodality that explicitly integrates experiential knowledge work into the rhetorical pedagogy that informs so many composition and technical communication programs. In these disciplines, multimodality is widely conceived in terms of what Gunther Kress calls “socialsemiotic” modes of communication shaped primarily by culture. In the cognitive and neurolinguistic theories of Vittorio Gallese and George Lakoff, however, multimodality is described as a key characteristic of our bodies’ sensory-motor systems which link perception to action and action to meaning, grounding all communicative acts in knowledge shaped through body-engaged experience. I argue that this “situated” account of cognition – which closely approximates Maurice Merleau-Ponty’s phenomenology of perception, a major framework for my study – has pedagogical precedence in the mimetic pedagogy that informed ancient Sophistic rhetorical training, and I reveal that training’s multimodal dimensions through a phenomenological exegesis of the concept mimesis. Plato’s denigration of the mimetic tradition and his elevation of conceptual contemplation through reason, out of which developed the classic Cartesian separation of mind from body, resulted in a general degradation of experiential knowledge in Western education. But with the recent introduction into college classrooms of digital technologies and multimedia communication tools, renewed emphasis is being placed on the “hands-on” nature of inventive and productive praxis, necessitating a revision of methods of instruction and assessment that have traditionally privileged the acquisition of conceptual over experiential knowledge. The model of multimodality I construct from Merleau-Ponty’s phenomenology, ancient Sophistic rhetorical pedagogy, and current neuroscientific accounts of situated cognition insists on recognizing the significant role knowledges we acquire experientially play in our reading and writing, speaking and listening, discerning and designing practices.

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.

"Going outside": discussing the connection between pedagogical practices with digital tools and outdoor learning in early childhood and primary education

Technology has an important role in children's lives and education. Based on several projects developed with ICT, both in Early Childhood Education (3-6 years old) and Primary Education (6-10 years old), since 1997, the authors argue that research and educational practices need to "go outside", addressing ways to connect technology with outdoor education. The experience with the projects and initiatives developed supported a conceptual framework, developed and discussed with several partners throughout the years and theoretically informed. Three main principles or axis have emerged: strengthening Children's Participation, promoting Critical Citizenship and establishing strong Connections to Pedagogy and Curriculum. In this paper, those axis will be presented and discussed in relation to the challenge posed by Outdoor Education to the way ICT in Early Childhood and Primary Education is understood, promoted and researched. The paper is exploratory, attempting to connect theoretical and conceptual contributions from Early Childhood Pedagogy with contributions from ICT in Education. The research-based knowledge available is still scarce, mostly based on studies developed with other purposes. The paper, therefore, focus the connections and interpellations between concepts established through the theoretical framework and draws on the almost 20 years of experience with large and small scale action-research projects of ICT in schools. The more recent one is already testing the conceptual framework by supporting children in non-formal contexts to explore vineyards and the cycle of wine production with several ICT tools. Approaching Outdoor Education as an arena where pedagogical and cultural dimensions influence decisions and practices, the paper tries to argue that the three axis are relevant in supporting a stronger connection between technology and the outdoor.

Scaling-up an efficacious school-based physical activity intervention: study protocol for the 'Internet-based professional learning to help teachers support activity in youth' (iPLAY) cluster randomized controlled trial and scale-up implementation evaluat

BACKGROUND: Despite the health benefits of regular physical activity, most children are insufficiently active. Schools are ideally placed to promote physical activity; however, many do not provide children with sufficient in-school activity or ensure they have the skills and motivation to be active beyond the school setting. The aim of this project is to modify, scale up and evaluate the effectiveness of an intervention previously shown to be efficacious in improving children's physical activity, fundamental movement skills and cardiorespiratory fitness. The 'Internet-based Professional Learning to help teachers support Activity in Youth' (iPLAY) study will focus largely on online delivery to enhance translational capacity.

METHODS/DESIGN: The intervention will be implemented at school and teacher levels, and will include six components: (i) quality physical education and school sport, (ii) classroom movement breaks, (iii) physically active homework, (iv) active playgrounds, (v) community physical activity links and (vi) parent/caregiver engagement. Experienced physical education teachers will deliver professional learning workshops and follow-up, individualized mentoring to primary teachers (i.e., Kindergarten - Year 6). These activities will be supported by online learning and resources. Teachers will then deliver the iPLAY intervention components in their schools. We will evaluate iPLAY in two complementary studies in primary schools across New South Wales (NSW), Australia. A cluster randomized controlled trial (RCT), involving a representative sample of 20 schools within NSW (1:1 allocation at the school level to intervention and attention control conditions), will assess effectiveness and cost-effectiveness at 12 and 24 months. Students' cardiorespiratory fitness will be the primary outcome in this trial. Key secondary outcomes will include students' moderate-to-vigorous physical activity (via accelerometers), fundamental movement skill proficiency, enjoyment of physical education and sport, cognitive control, performance on standardized tests of numeracy and literacy, and cost-effectiveness. A scale-up implementation study guided by the RE-AIM framework will evaluate the reach, effectiveness, adoption, implementation, and maintenance of the intervention when delivered in 160 primary schools in urban and regional areas of NSW.

DISCUSSION: This project will provide the evidence and a framework for government to guide physical activity promotion throughout NSW primary schools and a potential model for adoption in other states and countries.

Teacher stories of collusion and transformation: a feminist pedagogical framework and meta-language for cultural gender justice

This article examines data from two different studies concerning issues of social justice, gender and schooling and specifically the practices of secondary teachers, ‘Mr B’, a teacher from a school in Tasmania, Australia, and ‘Mr C’, a teacher from a school in Bedfordshire in the United Kingdom. Both teachers’ practices and relationships with students are analysed within a feminist interpretation of the Productive Pedagogies model of quality teaching and learning. The two different stories are presented as juxtapositions that serve to illuminate the capacities of pedagogy to, on the one hand, work in ways likely to re-inscribe the discourses of entitlement and privilege that perpetuate cultural gender injustice, and on the other, transform these discourses towards more equitable and just networks of multiple and intersecting differences.

Professional learning in mathematical reasoning: reflections of a primary teacher

Reasoning is an important aspect in the understanding and learning of mathematics. This paper reports on a case study presenting one Australian primary teacher’s reflections regarding the role played by a professional learning program in her developing understanding of mathematical reasoning. Examination of the transcripts of two interviews identified changes in her perceptions of mathematical reasoning by mapping interview responses against the Mathematical Reasoning Framework (Herbert et al., 2015). This change indicates that a well planned program of professional learning based on a demsonstration is efficacious in developing teachers’ understanding of mathematical reasoning.

Aligning neuroscience findings with socio-cultural perspectives on learning in science

Unfortunately, despite claims that this work will greatly impact education, very little research makes it into teacher practice. Although the reasons for this are varied, a primary concern is the lack of a proper translation framework.

Teaching teamwork in design: a framework for understanding effectiveness in student teams

This chapter proposes an Input-Process-Output framework for understanding what impacts the effectiveness of teamwork when higher education students are collaborating on design assignments. Theframework can help design educators integrate teamwork into their courses and better evaluate learning outcomes, and may also elucidate good practice for professional design teams. Explaining the genesis ofthe framework, the literature is assimilated on team effectiveness and predictors of team performance, including: definitions, dimensions and frameworks of team effectiveness in contexts far wider than designeducation. Informed by the challenges specific to teaching design, a 22-factor framework is proposed. The paper concludes with recommendations for teachers informed by the framework. The viability ofthe 22-factor model of team effectiveness is evidenced by national surveys across Australia, which are reported in summary here.

Applied machine learning for personalised early intervention in autism

This thesis is the first to address the problems of early intervention in Autism Spectrum Disorder through the lens of machine learning and data analytics. The key contribution is the establishment of large datasets in this domain for the first time together with a systematic data-based approach to extract knowledge relevant to Autism.

Social media as sensor for healthcare: A machine learning approach.

This thesis advances the area of applied machine learning, sentiment and psycholinguistic analysis in social media for health analytics. In particular, the thesis views social media as a gigantic form of 'sensor' to inform about mental health community and related topics.

DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.