4 resultados para Learning Performance
em Helda - Digital Repository of University of Helsinki
Resumo:
Most musicians choose a career in music based on their love of the art and a desire to share it with others. However, being a performing musician is highly demanding. Despite considerable evidence of the great frequency of performance-related problems (e.g. debilitating performance anxiety) among professional musicians or aspiring musicians in the current Western classical music tradition these problems are seldom discussed openly. The existing system offers musicians very little help in learning how to build sustainable performance success into their musical career. This study it is first of its kind in Finland which addresses the issue on larger scale in a systematic way. I devised the HOPE intervention (Holistically-Oriented Top Performance and Well-Being Enhancement), in order to learn how to integrate professional peak performance and a sense of personal well-being into the lives and careers of musicians. Unlike most interventions in previous research, the HOPE intervention is explicitly holistic and aims at enhancing the whole musician, not just alleviating performance anxiety. Earlier research has not in principle focused on musicians´ psychological well-being or on their subjective perceptions. The main purpose of the study is to understand the perceived impacts of the specially devised HOPE intervention on the participants and particularly in four key areas: performing, playing or singing well-being, and overall (performing, playing or singing and well-being combined). Furthermore, it is hoped that a deeper understanding of performers´ development will be gained. The research method is interdisciplinary and mainly qualitative. The primary data consist of a series of linked questionnaires (before and after the intervention) and semi-structured follow-up interviews collected during action research-oriented HOPE intervention courses for music majors in the Sibelius Academy. With the longitudinal group called Hope 1, the core data were collected during a nine month HOPE intervention course and from follow-up interviews conducted six months later in 2003-2004. The core data of Hope 1 (nine participants) are compared with the perceived impacts on fifty-three other participants in the HOPE courses during the period since their inception, 2001-2006. The focus is particularly on participants´ subjective perceptions. Results of the study suggest that the HOPE intervention is beneficial in enhancing overall performance capacity, including music performance, and a personal sense of well-being in a music university setting. The findings indicate that within all key areas significant positive changes take place between the beginning and the end of a HOPE intervention course. The longitudinal data imply that the perceived positive changes are still ongoing six months after the HOPE intervention course is finished. The biggest change takes place within the area of performing and the smallest, in participants´ perception of their playing or singing. The main impacts include reduced feelings of stress and anxiety (an enhanced sense of well-being) as well as increased sense of direction and control in one's life. Since the results of the present research gave no other reason to believe otherwise, it is to be expected that the HOPE intervention and the results of the study can be exploited in other areas of human activity as well, especially where continuous professional top performance is a prerequisite such as in business or sports. Keywords: performance enhancement, professional top performance, subjective well-being, subjective perceptions, holism, coaching, music performance anxiety, studying music, music.
Resumo:
"Fifty-six teachers, from four European countries, were interviewed to ascertain their attitudes to and beliefs about the Collaborative Learning Environments (CLEs) which were designed under the Innovative Technologies for Collaborative Learning Project. Their responses were analysed using categories based on a model from cultural-historical activity theory [Engestrom, Y. (1987). Learning by expanding.- An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit; Engestrom, Y., Engestrom, R., & Suntio, A. (2002). Can a school community learn to master its own future? An activity-theoretical study of expansive learning among middle school teachers. In G. Wells & G. Claxton (Eds.), Learning for life in the 21st century. Oxford: Blackwell Publishers]. The teachers were positive about CLEs and their possible role in initiating pedagogical innovation and enhancing personal professional development. This positive perception held across cultures and national boundaries. Teachers were aware of the fact that demanding planning was needed for successful implementations of CLEs. However, the specific strategies through which the teachers can guide students' inquiries in CLEs and the assessment of new competencies that may characterize student performance in the CLEs were poorly represented in the teachers' reflections on CLEs. The attitudes and beliefs of the teachers from separate countries had many similarities, but there were also some clear differences, which are discussed in the article. (c) 2005 Elsevier Ltd. All rights reserved."
Resumo:
We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.