Learning as movements in interspace

My research issues (in rearch project for PhD-degree, named Drama in School) concern how learning takes place in drama education in compulsory school. One part is to explore and problematize approaches to learning (in and through drama). In this paper will the concept learning be discussed by using a thought derived from Deleuze and Guattari’s nomad philosophy. They describe learning as a movement in the interspace. Focus is on process and inquiry, not on achievement of predetermined skills and competences. 

Comparison of learning between Digital gallery and Hands-on Laboratory

This thesis has been done in ROM (Royal Ontario Museum) located in Toronto Canada. It focuses on learning in two parts of the museum. It tries to find out how much each part is effective in terms of learning. Studies have been done in the Digital gallery, which has been equipped with digital video projector and workstation that allows visitors to interact with the collections in 2 or 3 dimensional spaces while they are watching the presenting film. The rest of the study was in Hands-on laboratory, which allows students to examine artifacts and discuss their findings .The method was used in this research is Concept mapping .In Digital gallery, 24 schools surveys in the form of pre-post- test by help of the concept mapping method has been done. In Hands-on laboratory, 12 schools have been studied by using the combination of interviewing and written pre post-test of concept mapping.

A machine learning approach for recognising woody plants on railway trackbeds

The purpose of this work in progress study was to test the concept of recognising plants using images acquired by image sensors in a controlled noise-free environment. The presence of vegetation on railway trackbeds and embankments presents potential problems. Woody plants (e.g. Scots pine, Norway spruce and birch) often establish themselves on railway trackbeds. This may cause problems because legal herbicides are not effective in controlling them; this is particularly the case for conifers. Thus, if maintenance administrators knew the spatial position of plants along the railway system, it may be feasible to mechanically harvest them. Primary data were collected outdoors comprising around 700 leaves and conifer seedlings from 11 species. These were then photographed in a laboratory environment. In order to classify the species in the acquired image set, a machine learning approach known as Bag-of-Features (BoF) was chosen. Irrespective of the chosen type of feature extraction and classifier, the ability to classify a previously unseen plant correctly was greater than 85%. The maintenance planning of vegetation control could be improved if plants were recognised and localised. It may be feasible to mechanically harvest them (in particular, woody plants). In addition, listed endangered species growing on the trackbeds can be avoided. Both cases are likely to reduce the amount of herbicides, which often is in the interest of public opinion. Bearing in mind that natural objects like plants are often more heterogeneous within their own class rather than outside it, the results do indeed present a stable classification performance, which is a sound prerequisite in order to later take the next step to include a natural background. Where relevant, species can also be listed under the Endangered Species Act.