Learning in and through drama : The necessity to use multiple theoretical perspectives in order to understand how learning takes place in drama

The complexity of learning implies that learning seldom is about just one thing. It can be said that learning processes are interdisciplinary. Within educational contexts, learning is not limited to constructed school subjects. In drama education, learning is simultaneously about drama as aesthetic expression and content because drama always is about something. The mainly focus can be on form, content or social aspects. The different aspects are always present, but may be more or less foreground or the background depending on the purpose of education. How do development concerning understanding of form, content, and social interaction, interact in a learning process in drama? My research is based on the view that learning at the same time takes place as an individual, internal process and a socially situated, inter-subjective process. Can learning in drama imply learning that can be transferred between different situations, a transformative learning and if so, how? Transformative learning includes cognitive, affective and corporal and social action aspects and means that the individual's frames of reference are transformed, evolved, to become more insightful and flexible which implies a change of personality. It leads to an integrated knowledge that can be applied in different contexts.   In the paper that will be presented at the conference, theories about how we learn in drama will be discussed in relation to my empirical research concerning drama and learning.

BEE COLONIES APPLIED TO MULTIPROCESSOR SCHEDULING

In order to achieve the high performance, we need to have an efficient scheduling of a parallelprogram onto the processors in multiprocessor systems that minimizes the entire executiontime. This problem of multiprocessor scheduling can be stated as finding a schedule for ageneral task graph to be executed on a multiprocessor system so that the schedule length can be minimize [10]. This scheduling problem is known to be NP- Hard.In multi processor task scheduling, we have a number of CPU’s on which a number of tasksare to be scheduled that the program’s execution time is minimized. According to [10], thetasks scheduling problem is a key factor for a parallel multiprocessor system to gain betterperformance. A task can be partitioned into a group of subtasks and represented as a DAG(Directed Acyclic Graph), so the problem can be stated as finding a schedule for a DAG to beexecuted in a parallel multiprocessor system so that the schedule can be minimized. Thishelps to reduce processing time and increase processor utilization. The aim of this thesis workis to check and compare the results obtained by Bee Colony algorithm with already generatedbest known results in multi processor task scheduling domain.

Lost in mistranslation : A case study of Japanese TV-drama fansubs

「Fansubs」というのはファンが翻訳した字幕のことである。
 本稿では日本のテレビドラマの「Fansubs」の特徴を調べる為、3つのテレビドラマの日本 語の台詞と、ファンが作った字幕を比べている。原文のテキストが翻訳と字幕制作でどの ように変わったかを情報の増減や誤訳など9つのカテゴリーに分類し、字幕テキストを 分析した。作成したのがプロの翻訳者ではなく、または非常に短期間で作成されたこの 「Fansubs」は、公式のテレビやDVDの字幕よりも誤訳などのエラーが非常に多いことが分 かった。 

A finite sample improvement of the fixed effects estimator : applied to technical infficiency

The FE ('fixed effects') estimator of technical inefficiency performs poorly when N ('number of firms') is large and T ('number of time observations') is small. We propose estimators of both the firm effects and the inefficiencies, which have small sample gains compared to the traditional FE estimator. The estimators are based on nonparametric kernel regression of unordered variables, which includes the FE estimator as a special case. In terms of global conditional MSE ('mean square error') criterions, it is proved that there are kernel estimators which are efficient to the FE estimators of firm effects and inefficiencies, in finite samples. Monte Carlo simulations supports our theoretical findings and in an empirical example it is shown how the traditional FE estimator and the proposed kernel FE estimator lead to very different conclusions about inefficiency of Indonesian rice farmers.