”Jeg føler jeg har veldig lite ordinær undervisning i den klassen”: hva åtte lærere legger vekt på ved tilpasset opplæring

Dagens rammeverk for grunnskolen har i stor grad overlatt til praksisfeltet å definere, operasjonalisere og samarbeide om tilpasset opplæring. Denne studien undersøker hvordan en gruppe lærere i grunnskolen forstår og håndterer tilpasset opplæring i sin praksis. Artikkelen belyser ulike sider ved tilpasset opplæring: som overordnet perspektiv, som hverdagspraksis, som relasjon og som samarbeid. Artikkelen konkluderer med at lærerne tolker og håndterer begrepet svært ulikt. Dette kommer blant annet til uttrykk i forhold til organisering av spesialundervisning. Lærerne opplever lite samarbeid om tilpasset opplæring og de gir uttrykk for at den privatpraktiserende lærer fortsatt rår grunnen.publishedVersio

Some hydraulic properties of Sandy-silty Norweigan Tills

Norwegian tills are usually coarse. The saturated water movement is mainly concentrated along fractures, fissures and' sorted sediment zones. The studies indicate that les that 10% of the water drains through the pore system of the tillmatrix. There is no marked correlation between the grain-size composition of the till and the saturated permeability. Homogeneous Norwegian tills may form important conduits of capillary water transport from the groundwater level to the vegetation during dry summers. This is mainly because the tills are dominated by equidimensional minerals which form an open pore system and because the groundwater level in many cases is situated at shallow depths

The deep kernelized autoencoder

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordAutoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing kernel Hilbert space, hence capturing topological properties of input data. In this paper, we enhance the autoencoder's ability to learn effective data representations by aligning inner products between codes with respect to a kernel matrix. By doing so, the proposed kernelized autoencoder allows learning similarity-preserving embeddings of input data, where the notion of similarity is explicitly controlled by the user and encoded in a positive semi-definite kernel matrix. Experiments are performed for evaluating both reconstruction and kernel alignment performance in classification tasks and visualization of high-dimensional data. Additionally, we show that our method is capable to emulate kernel principal component analysis on a denoising task, obtaining competitive results at a much lower computational cost.Norwegian Research Council FRIPR

Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series

The task of clustering unlabeled time series and sequences entails a particular set of challenges, namely to adequately model temporal relations and variable sequence lengths. If these challenges are not properly handled, the resulting clusters might be of suboptimal quality. As a key solution, we present a joint clustering and feature learning framework for time series based on deep learning. For a given set of time series, we train a recurrent network to represent, or embed, each time series in a vector space such that a divergence-based clustering loss function can discover the underlying cluster structure in an end-to-end manner. Unlike previous approaches, our model inherently handles multivariate time series of variable lengths and does not require specification of a distance-measure in the input space. On a diverse set of benchmark datasets we illustrate that our proposed Recurrent Deep Divergence-based Clustering approach outperforms, or performs comparable to, previous approaches

Enhancing Teachers’ Emotional Awareness Through Continuing Professional Development: Mission Possible?

In the context of the contemporary emphasis on the school’s role in supporting student wellbeing, this qualitative study explored the teachers’ experience of a Continuing Professional Development (CPD) programme, which focused on enhancing teachers’ emotional awareness within the context of everyday life. An implicit assumption in this approach is that student wellbeing can be nurtured (or undermined) through the everyday relations of teaching and learning in which emotional experiences are integrated. Focus groups with 22 primary and secondary school teachers in four schools in Norway were carried out, and a thematic analysis was conducted. The findings provide an illustration of how enhancing emotional awareness can strengthen professional competence in ways that can benefit the wellbeing of both teachers and students. The findings might inform a wider debate across national boundaries about the value of prioritising emotional awareness as an aspect of teachers’ CPD regarding their role in supporting student wellbeing.publishedVersio

Enhancing Teachers’ Emotional Awareness Through Continuing Professional Development: Mission Possible?

In the context of the contemporary emphasis on the school’s role in supporting student wellbeing, this qualitative study explored the teachers’ experience of a Continuing Professional Development (CPD) programme, which focused on enhancing teachers’ emotional awareness within the context of everyday life. An implicit assumption in this approach is that student wellbeing can be nurtured (or undermined) through the everyday relations of teaching and learning in which emotional experiences are integrated. Focus groups with 22 primary and secondary school teachers in four schools in Norway were carried out, and a thematic analysis was conducted. The findings provide an illustration of how enhancing emotional awareness can strengthen professional competence in ways that can benefit the wellbeing of both teachers and students. The findings might inform a wider debate across national boundaries about the value of prioritising emotional awareness as an aspect of teachers’ CPD regarding their role in supporting student wellbeing.publishedVersio

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology