Forget about theory - practice is all? - Student teachers' learning in practicum

In 1999, all student teachers at secondary I level at the University of Bern who had to undertake an internship were asked to participate in a study on learning processes during practicum: 150 students and their mentors in three types of practicum participated—introductory practicum (after the first half‐year of studies), intermediate practicum (after two years of studies) and final practicum (after three years of studies). At the end of the practicum, student teachers and mentors completed questionnaires on preparing, teaching and post‐processing lessons. All student teachers, additionally, rated their professional skills and aspects of personality (attitudes towards pupils, self‐assuredness and well‐being) before and after the practicum. Forty‐six student teachers wrote daily semi‐structured diaries about essential learning situations during their practicum. Results indicate that in each practicum students improved significantly in preparing, conducting and post‐processing lessons. The mentors rated these changes as being greater than did the student teachers. From the perspective of the student teachers their general teaching skills also improved, and their attitudes toward pupils became more open. Furthermore, during practicum their self‐esteem and subjective well‐being increased. Diary data confirmed that there are no differences between different levels of practicum in terms of learning outcomes, but give some first insight into different ways of learning during internship.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Ethics in science: enhancing the democratisation of knowledge production in transdisciplinary research partnerships for sustainable development

Better access to knowledge and knowledge production has to be reconsidered as key to successful individual and social mitigation and adaptation strategies for global change. Indeed, concepts of sustainable development imply a transformation of science towards fostering democratisation of knowledge production and the development of knowledge societies as a strategic goal. This means to open the process of scientific knowledge production while simultaneously empowering people to implement their own visions for sustainable development. Advocates of sustainability science support this transformation. In transdisciplinary practice, they advance equity and accountability in the access to and production of knowledge at the science–society interface. UNESCO points to advancements, yet Northern dominance persists in knowledge production as well as in technology design and transfer. Further, transdisciplinary practice remains experimental and hampered by inadequate and asymmetrically equipped institutions in the North and South and related epistemological and operational obscurity. To help identify clear, practicable transdisciplinary approaches, I recommend examining the institutional route – i.e., the learning and adaptation process – followed in concrete cases. The transdisciplinary Eastern and Southern Africa Partnership Programme (1998–2013) is a case ripe for such examination. Understanding transdisciplinarity as an integrative approach, I highlight ESAPP’s three key principles for a more democratised knowledge production for sustainable development: (1) integration of scientific and “non-scientific” knowledge systems; (2) integration of social actors and institutions; and (3) integrative learning processes. The analysis reveals ESAPP’s achievements in contributing to more democratic knowledge production and South ownership in the realm of sustainable development.