Pedagoginen liveroolipelaaminen historian opetusmetodina

In this research pedagogical live action role playing is examined as a method of teaching history. The objective of this research is to present and define the concept of pedagogical live action role playing as a method of teaching history and examine its capability in teaching historical empathy. Pedagogical live action role playing is a teaching method under development which has elements from drama pedagogics and different learning theories. The theoretical part of this research consists of play theories and drama pedagogical theories which are relevant to live action role playing, and also the concepts of historical empathy and historical thinking. In drama pedagogics the main sources are the researches of Laakso (2004) and Heikkinen (2002, 2004). The historical part uses the researches of VanSledright (2002), Cooper (2000) and Ashby and Lee (1987) as its main sources. The research was carried out at the Viikki Teacher Training School in the class 6b during autumn 2007. The research material consists of three-phased interviews. During all phases three pupils were interviewed, and during two of the phases, also the teacher of the class was interviewed. The methods used in the interviews were thematic interview and stimulated recall -interview. The data gathered from the interviews was analyzed using content analysis. Pedagogical live action role playing has three phases when teaching history: pre-game (preparations), game (live action role play) and post-game (discussion after the game and in the classroom). The teacher's attention and carefulness is important during all phases. The mistakes made in the pre-game face e.g. in writing the characters seem to cause major problems in the pupils' game experiences. Pedagogical live action role playing is capable of teaching historical empathy, but according to my research this has some conditions, from which the most important is the pupil's historical knowledge before the game. Teaching historical empathy was based on esthetic dualition: the pupil made decions about the actions of his character as him/herself. The more background information the pupil had about the era they played, the more the pupil seemed to make decisions based on his historical empathy, aka his understanding of the era of the larp and of the people of the same era.

Becoming to Know: Essays on Extended Epistemology of Knowledge Creation

This qualitative, explorative study, which comprises four essays, focuses on knowledge management (KM). It seeks to answer the question: How can the knowledge creation theory of KM benefit from social learning theories? While studying the five development phases of knowledge creation theory of KM through 1995-2008 and applying some social learning theories in essays, the concepts of knowing, learning and becoming have emerged. Drawing on these three concepts and on becoming ontology and extended epistemology as research philosophies the study suggests the ‘becoming epistemology’ concept and develops the ‘becoming to know’ framework. The framework proposes becoming as phronesis of dialectic interactions between learning and knowing. It shows how becoming to know evolves as an interplay between concrete experience and logical thinking in the present and in a living context. The proposed framework could be considered a contribution to the current development phase of the knowledge creation theory of KM because it illustrates how ontological and epistemological knowledge spirals come together, which is the essence of the knowledge creation theory of KM.

Consumer Evaluation of Hybrid Innovations (summary section only)

Hybrid innovations, or new products that combine two existing product categories into one, are increasingly popular in today’s marketplace. Despite this proliferation, few studies address them. The purpose of this thesis is to examine consumer evaluation of hybrid innovations by focusing on consumer categorization of such innovations and on factors contributing positively and negatively to their evaluation. This issue is examined by means of three studies. The first study addresses the proportion of consumers categorizing hybrid products as single- versus dual-purpose, what contributes to such a categorization, what differences can be found between the two groups, and if categorization can and should be included in models of innovation adoption. The second study expands on the scope by including motivation as a predictor of consumer evaluation and examines two cognitive and affective factors and their differential impact on innovation evaluation. Finally, the third study examines the product comparisons single- versus dual-purpose categorization induce. These three essays together build up a broader understanding of hybrid innovation evaluation. The thesis uses theories from both psychology and marketing to examine the issues at hand. Conceptual combination and analogical learning theories from psychology are used to comprehend categorization and knowledge transfer. From marketing, consumer behavior and innovation adoption studies are addressed to better understand the link between categorization and product evaluation and the factors contributing to product evaluation. The main results of the current thesis are that (1) most consumers categorize hybrid products as single- and not as dual-purpose products, (2) consumers that categorize them as dual-purpose find them more attractive (3) motivation has a significant effect on consumer evaluation of innovations; cognitive factors promote an emphasis on product net benefits, whereas affective factors induce consumers to consider product meaning in the form of categorization and perceived product complexity, (4) categorization constrains subsequent product evaluation, and (5) categorization can and should be included to models of innovation adoption. Maria Sääksjärvi is associated with CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.