Transformative learning and international students negotiating higher education

This chapter aims to explore the theory of transformative learning as a possible explanation for the changes international students make in their journey to negotiate higher education. The chapter is derived from a doctoral study that involved international Chinese and Vietnamese students' adaptation to Australian higher education academic practices (Tran 2007). Within this chapter, transformative learning is viewed as a changing process in which international students construct reality through revisiting their existing assumptions and moving towards life-changing developments in their personal and professional perspectives (Cranton 2002; Mezirow 2000). It will be argued in this chapter that international students' process of negotiating higher education is a dynamic interplay between challenges and transformative power. Cross-border intercultural experiences are intimately linked to opportunities for self-transformation, and the challenging experiences that international students go through indeed foster the conditions for professional development and life-enhancing changes to take place. Given the current lack of theoretical and empirical research on the transformative power of international students, there is a critical need for more research on the transformative characteristics of international students and how best to capitalize on their potential. In this chapter, I draw on excerpts from two rounds of interviews with individual international students to illustrate the specific ways in which international students have the capacity to transform their own learning and develop life-enhancing skills. The discussion shows that they experience evolution in professional outlook, attitudes and personal qualities through the process of critical self-reflection and adaptation to disciplinary demands in higher education. The chapter also highlights the contradictions regarding the discursive practices within the current context of international education export.

Incremental learning of temporally-coherent gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive oneby- one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed, which is increased (or reduced) when enough evidence for a new component is seen. This is deduced from the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions

Incremental learning of temporally-coherent Gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive one- by-one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed which is increased (or reduced) when enough evidence for a new component is seen. This is deducedfrom the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions.

Localized learning by emerging multinational enterprises in developed host countries: a fuzzy-set analysis of Chinese foreign direct investment in Australia

Firms learn general international management and foreign market specific knowledge in their internationalization process. Firms' strategic emphasis on generalized vs. localized learning is an important yet underexplored issue in the extant literature. Drawing on the theoretical framework of dynamic capability, and in the context of emerging multinational enterprises' FDI into developed host countries, this study examines the equifinal process-position-path configurations of firms that will motivate them to engage in localized learning (as opposed to generalized learning). Utilizing primary and secondary data of eleven Chinese foreign direct investments in Australia, collected at both headquarters and subsidiary levels, we conducted fuzzy-set qualitative comparative analysis (fsQCA) that provided substantial support to our propositions. This study contributes to the internationalization process model by identifying equifinal process-position-path configurations, as well as their core and peripheral conditions that motivate localized learning at both the headquarters and the subsidiary levels.

Popularity of reinforcement-based and belief-based learning models: an evolutionary approach

In an evolutionary model, players from a given population meet randomly in pairs each instant to play a coordination game. At each instant, the learning model used is determined via some replicator dynamics that respects payoff fitness. We allow for two such models: a belief-based best-response model that uses a costly predictor, and a costless reinforcement-based one. This generates dynamics over the choice of learning models and the consequent choices of endogenous variables. We report conditions under which the long run outcomes are efficient (or inefficient) and they support the exclusive use of either of the models (or their co-existence).

Teacher agency and participation in professional learning for new literacies

The proliferation of digital technologies is influencing the relational as well as the technological and meaning-making aspects of literacy learning. There is a renewed focus on student learning that promotes agency and enables new literacies mindsets. However a lack of clarity persists as to the form and content of effective professional learning for teachers of new literacies. Combining elements from various models of professional learning to foster teacher agency and participation mobilises transformed processes and conditions. This article draws on literature from the areas of new literacies, student agency and teacher professional learning to argue for approaches to teacher professional learning that support new literacy learning. It explores the characteristics of models of professional learning for teachers; describes a professional learning program offered to teachers of literacy; outlines a mixed methods research study in the form of a survey of participants engaged in the professional learning program; and analyses teacher perceptions of their experiences of professional learning and how key characteristics influenced their learning.

Pedagogy of the rural: teaching and learning for rural futures?

Pedagogy of the Rural illustrates the complexities of rural space and considers some of the underlying assumptions, ‘truths’ and ‘realities’ about rural education and teaching in a complicated and dynamic policy context. Pedagogy of the Rural offers an alternative to current teacher education practice – it is responsive to policy demands as well as local conditions and traditions, and has a futures orientation, in that it provides a way forward for valuing rural contexts for what they bring to teacher identities beyond traditional deficit positionings dominant in current discourses on rural. The authors examine notions of size and how this impacts on the ways in which beginning teachers in rural locations are positioned in terms of identity at a macro, meso and micro level. They also examine what it means to ‘be rural’ and use Pedagogy of the Rural to conceptualise rural understandings as a pedagogy that is not a pedagogy ‘for’ or ‘about’ but rather ‘of’ the rural. Complexities of the Pedagogy of the Rural are understood through Harré’s (2004) positioning theory, Baudrillard’s (1983) notion of simulation and simulacra and Lefebvre’s (2009) arguments around space and economic geographies. The interrelationship of place, space and identity unify teachers’ understandings of who they (or we) are, and are becoming, in a specific time and geographical location, raising questions about: subjectivity - who we are; power - what we can do; and desire- who we might become (Harré, Moghaddam, Cairnie, Rothbart, & Sabat, 2009), and the influence of personal and professional histories and what rural brings to our pedagogy within this.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Time-place learning in food-restricted Nile tilapia

Time-place learning based on food association was investigated in eight food-restricted Nile tilapias. Each fish was individually housed for 10 days in an experimental tank for adjustments to laboratory conditions, and fed daily in excess. Feeding was then interrupted for 17 days. Training was then started, based on a food-restricted regime in a tank divided into three interconnected compartments. Daily food was offered in one compartment (left or right side) of the tank in the morning and on the opposite side in the afternoon, for a continuous 30-day period. Frequency of choices on the right side was measured on days 10, 20 and 30 (during these test days, fish were not fed). Following this 30-day conditioning period, the Nile tilapias were able to switch sides at the correct period of the day to get food, suggesting that food restriction facilitates time-place learning discrimination. (C) 2007 Elsevier B.V. All rights reserved.

Autenticação biométrica de usuários em sistemas de E-learning baseada em reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

Media Education and Brazilian Educational Policies for the Enhancement of Learning

As media education concepts and practices have been disseminated and strengthened in European countries and Americas, the policies responsible for that expansion remain little known, particularly in countries where the achievements have been recently noted. That is the case for Brazil, where there have been new opportunities for media education, considered as a valuable resource to help accomplish goals of the educational system. This paper looks into the contribution of media education to the enhancement of teaching and learning in the context of innovations brought by recent policies of the Brazilian Ministry of Education. After educational reform programmes which brought the opportunity for emerging fields such as media education, we produced teaching material and conducted a series of workshops with students and teachers from state secondary schools. By reading and producing multimedia information about local public services available to young people, pupils learned about democracy, citizenship, civic engagement, media language, and identity. Lessons from our experiment are discussed against the backdrop of education policies being implemented to ameliorate harsh conditions resulting from the recent economic crisis. We suggest that media education can help by creating a learning environment in which the students become aware of the value of educational attainments.

Effects of a metatextual intervention program in developing written narratives of students with learning difficulties

This study aimed to verify the effects of a metatextual intervention program, in the elaboration of stories written by students with learning difficulties. Four students were included in the sample of both genders, with ages ranging between eight years and four months and ten years and two months of age. The program was implemented at the participant schools, using an approach of multiple baseline within-subjects, with two conditions: baseline and intervention. Data analysis was based on the classification of stories produced by the students. Mann-Whitney testing was also applied, to analyze whether there have been significant changes in these productions. The results indicated that all students have improved performance in relation to the categories of produced stories, from elementary schemas (33%), for a more elaborate scheme (77%), with a better structuring of the elements that constitute a story. Statistical analysis also showed that the intervention has produced significant results for all variables analyzed. The data obtained have shown that the program was effective.

Traces, Patterns, Texture: In Search of Aesthetic Teaching/Learning Encounters

This inquiry reveals the crucial guidance of teachers toward surveying the capacity and needs of students, the formation of ideas, acting upon ideas, fostering connections, seeing potential, making judgments, and arranging conditions. Each aesthetic trace causes me to wonder how teachers learn to create experiences that foster student participation in the world aesthetically. The following considerations surface: • Given the emphasis in schools on outcomes and results, how do we encourage teachers to focus on acts of mind instead of end products in their work with students? • Given the orientations toward technical rationality, to fixed sequence, how do we help teachers experience fluid, purposeful learning adventures with students in which the imagi¬nation is given room to play? • Given the tendency to conceive of planning in teaching as the deciding of everything in advance, how do we help teachers and students become attuned to making good judgments derived from within learning experiences? • How do we help teachers build dialogical multivoiced conversations instead of monolithic curriculum? • What do we do to recover the pleasure dwelling in subject matter? How do we get teachers and students to engage thoughtfully in meaningful learning as opposed to covering curriculum7 • A capacity to attend sensitively, to perceive the complexity of relationships coming together in any teaching/learning experience seems critical. How do we help teachers and students attend to the unity of a learning experience and the play of meanings that arises from such undergoing and doing? The traces, patterns, and texture evidenced locate tremendous hope and wondrous possibilities alive within aesthetic teaching/learning encounters. It is such aliveness I encountered in the grade 4 art classroom that opened this account and continues to compel my attention. Possibilities for teaching, learning, and teacher education emerge. I am convinced they are most worthy of continued pursuit.

Novelty, but Not Operant Aversive Learning, Enhances Fos and Egr-1 Expression in the Medial Prefrontal Cortex and Hippocampal Areas of Rats

Immediate early genes (IEG) are presumed to be activated in response to stress, novelty, and learning. Evidence supports the involvement of prefrontal and hippocampal areas in stress and learning, but also in the detection of novel events. This study examined whether a previous experience with shocks changes the pattern of Fos and Egr-1 expression in the medial prefrontal cortex (mPFC), the hippocampal cornus ammonis 1 (CA1), and dentate gyrus (DG) of adult male Wistar rats that learned to escape in an operant aversive test. Subjects previously exposed to inescapable footshocks that learned to escape from Shocks were assigned to the treated group (EXP). Subjects from Group Novelty (NOV) rested undisturbed during treatment and also learned to escape in the test. The nonshock group (NSH) rested undisturbed in both sessions. Standard immunohistochemistry procedures were used to detect the proteins in brain sections. The results show that a previous experience with shocks changed the pattern of IEG expression, then demonstrating c-fos and egr-1 induction as experience-dependent events. Compared with NSH and EXP an enhanced Fos expression was detected in the mPFC and CA1 subfield of Group NOV, which also exhibited increased Egr-1 expression in the mPFC and DG in comparison to NSH. No differences were found in the DG for Fos, or in the CA1 for Egr-1. Novelty, and not the operant aversive escape learning, seems to have generated IEG induction. The results suggest novel stimuli as a possible confounding factor in studies on Fos and/or Egr-1 expression in aversive conditions.