Intercultural Learning and Hybridity in the Culture Laboratory

The number of immigrant students in vocational education and training is steadily increasing in Finland. This poses challenges for teachers and schools. This research focuses on emerging questions of intercultural learning in the context of immigrant training, and on a method the Culture Laboratory that was developed in an attempt to respond to the challenges. The main methodological and theoretical framework lies in cultural-historical activity theory, developmental work research, and in the concepts of the intercultural and hybridity. The empirical material consists of videotaped recordings of discussions in the Culture Laboratory. The five main research questions focused on the strengths and limitations of the Culture Laboratory as a tool for intercultural learning, the significance of disturbances in it, the potential of suggestions for intercultural learning, paper as a mediating artifact , and the concept of intercultural space. The findings showed that the Culture Laboratory offered a solid background for developing intercultural learning. The disturbances manifested revealed a multitude of scripts and activities. It was also suggested that the structure of expansive learning could start from externalization instead of internalization. The suggestions the participants made opened up a hybrid learning space for intercultural development, and offered a good springboard for new ideas. Learning in Paperland posed both challenges and opportunities for immigrant students, and different paper trails emerged. Intercultural space in the Culture Laboratory was a developmental zone in which a hybrid process of observing, comparing, and creating took place. Key words: intercultural learning, immigrant training, cultural-historical activity theory, developmental work research,

Transcending “the Impossible” : From Dead End to Expansive Learning

This dissertation investigates changes in bank work and the experience of impossibility attached to these by workers at the local level from the viewpoint of work-related well-being and collective learning. A special challenge in my work is to conceptualize the experience of impossibility as related to change, and as a starting point and tool for development work. The subject of the dissertation, solving the impossible as a collective learning process, came up as a central theme in an earlier project: Work Units between the Old and the New (1997 – 1999). Its aim was to investigate how change is constructed as a long-term process, starting from the planning of the change until its final realization in everyday banking work. I studied changes taking place in the former Postipankki (Postal Bank), later called Leonia. The three-year study involved the Branch Office of Martinlaakso, and was conducted from the perspective of well-being in a change process. The sense of impossibility involved in changes turned out to be one of the most crucial factors impairing the sense of well-being. The work community that was the target of my study did not have the available tools to construct the change locally, or to deal with the change-related impossibility by solving it through a mutual process among themselves. During the last year of the project, I carried out an intervention for development in the Branch Office, as collaboration between the researchers and the workers. The purpose of the intervention was to resolve such perceived change-related impossibility as experienced repeatedly and considered by the work community as relevant to work-related well-being. The documentation of the intervention – audio records from development sessions, written assignments by workers and assessment or evaluation interviews – constitute the essential data for my dissertation. The earlier data, collected and analysed during the first two years, provides a historical perspective on the process, all the way from construction of the impossibility towards resolving and transcending it. The aim of my dissertation is to understand the progress of developmental intervention as a shared, possibly expansive learning process within a work community and thus to provide tools for perceiving and constructing local change. I chose the change-related impossibility as a starting point for development work in the work community and as a target of conceptualization. This, I feel, is the most important contribution of my dissertation. While the intervention was in progress, the concept of impossibility started emerging as a stimulating tool for development work. An understanding of such a process can be applied to development work outside banking work as well. According to my results, it is pivotal that a concept stimulating development is strongly connected with everyday experiences of and speech about changes in work activity, as well as with the theoretical framework of work development. During this process, development work on a local level became of utmost interest as a case study for managing change. Theoretically, this was conceptualized as so-called second-order work and this concept accompanies us all the way through the research process. Learning second-order work and constructing tools based on this work have proved crucial for promoting well-being in the change circumstances in a local work unit. The lack of second-order work has led to non-well-being and inability to transcend the change-related sense of impossibility in the work community. Solving the impossible, either individually or situationally, did not orient the workers towards solving problems of impossibility together as a work community. Because the experience of the impossibility and coming to terms with transcending it are the starting point and the target of conceptualization in this dissertation, the research provides a fresh viewpoint on the theoretical framework of change and developmental work. My dissertation can facilitate construction of local changes necessitated by the recent financial crisis, and thus promote fluency and well-being in work units. It can also support change-related well-being in other areas of working life.

Analysing the Impact of Learning Inputs - Application to Terrain Traversability Estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain.

The evolution of entrepreneurial learning

- Purpose The purpose of this paper is to present an evolutionary perspective on entrepreneurial learning, whilst also accounting for fundamental ecological processes, by focusing on the development of key recurring, knowledge components within nascent and growing small businesses. - Design/methodology/approach The paper relates key developments within the organizational evolution literature to research on entrepreneurial learning, with arguments presented in favor of adopting a multi‐level co‐evolutionary perspective that captures and explains hidden ecological process, such as niche‐construction. - Findings It is argued in the paper that such a multi‐level focus on key recurring knowledge components can shed new light on the process of entrepreneurial learning and lead to the cross‐fertilization of ideas across different domains of study, by offering researchers the opportunity to use the framework of variation‐selection‐retention to develop a multi‐level representation of organizational and entrepreneurial learning. - Originality/value Entrepreneurial learning viewed in this way, as a multi‐level struggle for survival amongst competing knowledge components, can provide entrepreneurs with a set of evolutionary heuristics as they re‐interpret their understanding of the evolution of their business.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Enhancing professional learning for rural educators by rethinking connectedness

Impending changes in Australian education brings forth the expected transformation of teachers working in schools. Three key points for transforming Australian schools has been identified by Gillard (2008a) including the improvement of quality teaching, ensuring every child benefits and mandating transparency and accountability. A number of initiatives were considered to assist with such reform including the implementation of a Digital Education Revolution, the move to the Australian Curriculum and the implementation of a National Framework for Professional Standards for Teaching. As these transformative initiatives are rolled out to teachers across Australia, the equitable access to PD to support all teachers, regardless of their geographical location, is in question. In line with the literature, the author proposes the concept of delivering PD and accessing PD from regional and remote areas be reconsidered. This research paper will outline the findings from the study including travel time being significant and impacting on teachers personal time; limited relief teachers impacting on access to PD; promotion and teacher registration being explicitly linked to PD; professional learning communities being valued but often limited by small staff numbers; professional learning conducted in the local context being preferred; professional learning established at the teacher and school level being desirable; teachers being confident in using technology and accessing PD online if required; and social cohesiveness being valued and often limited by isolation. Further this research has culminated in the development of a conceptual framework that would facilitate improving the amount and variety of professional learning available to regional and remote teachers.

Rethinking connectedness : an investigation into the access of teacher professional learning in regional and remote Western Australia

Many teachers working in remote and regional areas have limited access to collegial support networks. This research aimed to examine the existing strategies that were being undertaken by the Department of Education in Western Australia, to provide professional learning to teachers in regional and remote areas. It was important to establish the perceptions of teachers’ access to professional learning from those working at the coalface in geographically dispersed areas. Consequently, the possible opportunity for improving the amount and variety of professional learning, through the application of both synchronous and asynchronous technologies was proposed. The study was guided by the primary research question: “In what ways might technology be used to support professional development of regional and remote teachers in Western Australia?” Generating descriptions of current practice of professional learning along with the teacher perceptions were central to this research endeavour. The study relied on a mixed method research approach in order to attend to the research question. The data were collected in phases, referred to as an explanatory mixed methods design. Quantitative data were collected from 104 participants to provide a general picture of the research problem. To further refine this general picture, qualitative data were collected through interviews and e-interviews of 10 teachers. Participants in the study included graduate teachers, teachers who had taught more than two years, senior teachers and Level Three teachers from seven teaching districts within Western Australia. An investigation into current practice was included in this phase and technologies available to support a professional learning community over distance were documented. The final phase incorporated the formulation of a conceptual framework where a model was developed to facilitate the successful implementation of a professional learning community through the application of synchronous and asynchronous technologies. The study has identified that travel time in order to access professional development is significant and impacts on teachers’ personal time. There are limited relief teachers available in these isolated areas which impacts on the opportunities to access professional development. Teachers face inequities, in terms of promotion, because professional development is explicitly linked to promotional opportunities. Importantly, it was found that professional learning communities are valued, but are often limited by small staff numbers at the geographic locality of the school. Teachers preferred to undertake professional learning in the local context of their district, school or classroom and this professional learning must be established at the need of the individual teacher in line with the school priorities. Teachers reported they were confident in using technology and accessing professional development online if required, however, much uncertainty surrounded the use of web 2.0 technologies for this purpose. The recommendations made from the study are intended to identify how a professional learning community might be enhanced through synchronous and asynchronous technologies.

Rethinking connectedness: improving access to professional learning for regional and remote teachers

Transformation of Australian education is occurring at a rapid rate through the implementation of a number of initiatives. These initiatives include the Digital Education Revolution, the move to a National Curriculum and the implementation of a National Framework for Professional Standards for Teachers and Principals. As these initiatives are rolled out to schools across Australia, the equitable access to professional learning to support all teachers, regardless of their geographical location, is in question. A number of studies have been conducted in Australia that highlight the importance of professional learning and the difficulty faced by regional and remote teachers with regard to access (Gerard Daniels, 2007; Lysons, Cooksey, Panizzon, Parnell & Pegg 2006; Ministerial Review of Schooling, 1994, Rural and Remote Education Advisory Council, 2000; Vinson, 2002). Along with access to professional learning, has been the discussion of effective modes of delivery. Face to face professional learning, in regional and metropolitan areas, is offered in isolation, or in some cases, is complimented with virtual learning environments. The need for a more sustainable approach to professional learning is highly necessary. A mixed method research approach was utilised in order to answer the primary research question "In what ways might technology be used to support professional learning of regional and remote teachers in Western Australia?" This research paper outlines the findings from the study including the significance of travel time; impact of limited relief teachers; implications for promotion and teacher registration; professional learning communities being valued but often limited by small staff numbers; professional learning conducted in the local context being preferred; professional learning established at the teacher and school level being desirable; teachers being confident in using technology and accessing PD online if required; and social cohesiveness being valued and often limited by isolation. Further, this research has culminated in the development of a "model of rethinking connectedness" that would facilitate improving the amount and variety of professional learning available to regional and remote teachers.

Design a simulated multimedia enriched immersive learning environment (SMILE) for nursing care of dementia patient

This thesis is to establish a framework to guide the development of a simulated, multimedia-enriched, immersive, learning environment (SMILE) framework. This framework models essential media components used to describe a scenario applied in healthcare (in a dementia context), demonstrates interactions between the components, and enables scalability of simulation implementation. The thesis outcomes also include a simulation system developed in accordance with the guidance framework and a preliminary evaluation through a user study involving ten nursing students and practicioners. The results show that the proposed framework is feasible and effective for designing a simulation system in dementia healthcare training.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Establishing a framework for transforming student engagement, success and retention in higher education institutions

This poster summarises the outcomes of a national project to develop and provide a holistic framework consisting of a series of sequential and increasingly sophisticated stages that will allow higher education institutions (HEIs) to manage and improve their student engagement and retention strategies/programs.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

A tutorial survey of reinforcement learning

This paper gives a compact, self-contained tutorial survey of reinforcement learning, a tool that is increasingly finding application in the development of intelligent dynamic systems. Research on reinforcement learning during the past decade has led to the development of a variety of useful algorithms. This paper surveys the literature and presents the algorithms in a cohesive framework.

Weighted subspace methods and spatial smoothing: analysis and comparison

The effect of using a spatially smoothed forward-backward covariance matrix on the performance of weighted eigen-based state space methods/ESPRIT, and weighted MUSIC for direction-of-arrival (DOA) estimation is analyzed. Expressions for the mean-squared error in the estimates of the signal zeros and the DOA estimates, along with some general properties of the estimates and optimal weighting matrices, are derived. A key result is that optimally weighted MUSIC and weighted state-space methods/ESPRIT have identical asymptotic performance. Moreover, by properly choosing the number of subarrays, the performance of unweighted state space methods can be significantly improved. It is also shown that the mean-squared error in the DOA estimates is independent of the exact distribution of the source amplitudes. This results in a unified framework for dealing with DOA estimation using a uniformly spaced linear sensor array and the time series frequency estimation problems.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.