Avsikten med denna litteraturstudie är att undersöka vad forskning menar krävs för att elever i årskurs f-3 ska utveckla läs- och skrivkunskaper. I studien beskrivs de överordnade läsinlärningsmetoderna phonics och whole language med arbetssätten Wittingmetoden och LTG. Vidare beskrivs undervisningen och lärarens betydelse för elevers läs- och skrivinlärning. Det framkommer i resultatet att läraren har en betydande roll för elevers läs- och skrivinlärning där undervisningen behöver vara strukturerad och organiserad. Undervisningen behöver innehålla viktiga grundläggande delar och dimensioner såsom fonologisk medvetenhet, ordavkodning, läsflyt, läsförståelse och läsintresse för att elever ska utvecklas i sin läsning. Det framgår även att tidiga insatser är av stor betydelse för att elever ska tillägna sig läs- och skrivkunskaper. Informationssökning har genomförts i olika databaser som till exempel ERIC och SwePub för att finna vetenskapligt material som valdes ut för att besvara litteraturstudiens syfte och frågeställningar. Materialet består av tidskriftsartiklar, doktorsavhandlingar, en licentiatavhandling och ett konferensbidrag som har analyserats och sammanställts och ligger till grund för denna litteraturstudie.

Habitat classification, the process of mapping a site with its habitats, is a crucial activity for monitoring environmental biodiversity. Phase 1 classification, a 10-class four-tier hierarchical scheme, is the most widely used scheme in the UK. Currently, no automatic approaches have been developed and its classification is carried out exclusively by ecologists. This manual approach using surveyors is laborious, expensive and subjective. To this date, no automatic approach has been developed. This thesis presents the first automatic system for Phase 1 classification. Our main contribution is an Automatic Image Annotation (AIA) framework for the automatic classification of Phase 1 habitats. This framework combines five elements to annotate unseen photographs: ground-taken geo-referenced photography, low-level visual features, medium-level semantic information, random projections forests and location-based weighted predictions. Our second contribution are two fully-annotated ground-taken photograph datasets, the first publicly available databases specifically designed for the development of multimedia analysis techniques for ecological applications. Habitat 1K has over 1,000 photographs and 4,000 annotated habitats and Habitat 3K has over 3,000 images and 11,000 annotated habitats. This is the first time ground-taken photographs have been used with such ecological purposes. Our third contribution is a novel Random Forest-based classifier: Random Projection Forests (RPF). RPFs use Random Projections as a dimensionality reduction mechanism in their split nodes. This new design makes their training and testing phase more efficient than those of the traditional implementation of Random Forests. Our fourth contribution arises from the limitations that low-level features have when classifying similarly visual classes. Low-level features have been proven to be inadequate for discriminating high-level semantic concepts, such as habitat classes. Currently, only humans posses such high-level knowledge. In order to obtain this knowledge, we create a new type of feature, called medium-level features, which use a Human-In-The-Loop approach to extract crucial semantic information. Our final contribution is a location-based voting system for RPFs. We benefit from the geographical properties of habitats to weight the predictions from the RPFs according to the geographical distance between unseen test photographs and photographs in the training set. Results will show that ground-taken photographs are a promising source of information that can be successfully applied to Phase 1 classification. Experiments will demonstrate that our AIA approach outperforms traditional Random Forests in terms of recall and precision. Moreover, both our modifications, the inclusion of medium-level knowledge and a location-based voting system, greatly improve the recall and precision of even the most complex habitats. This makes our complete image-annotation system, to the best of our knowledge, the most accurate automatic alternative to manual habitat classification for the complete categorization of Phase 1 habitats.

This study examined a Pseudoword Phonics Curriculum to determine if this form of instruction would increase students’ decoding skills compared to typical real-word phonics instruction. In typical phonics instruction, children learn to decode familiar words which allow them to draw on their prior knowledge of how to pronounce the word and may detract from learning decoding skills. By using pseudowords during phonics instruction, students may learn more decoding skills because they are unfamiliar with the “words” and therefore cannot draw on memory for how to pronounce the word. It was hypothesized that students who learn phonics with pseudowords will learn more decoding skills and perform higher on a real-word assessment compared to students who learn phonics with real words. Students from two kindergarten classes participated in this study. An author-created word decoding assessment was used to determine the students’ ability to decode words. The study was broken into three phases, each lasting one month. During Phase 1, both groups received phonics instruction using real words, which allowed for the exploration of baseline student growth trajectories and potential teacher effects. During Phase 2, the experimental group received pseudoword phonics instruction while the control group continued real-word phonics instruction. During Phase 3, both groups were taught with real-word phonics instruction. Students were assessed on their decoding skills before and after each phase. Results from multiple regression and multi-level model analyses revealed a greater increase in decoding skills during the second and third phases of the study for students who received the pseudoword phonics instruction compared to students who received the real-word phonics instruction. This suggests that pseudoword phonics instruction improves decoding skills quicker than real-word phonics instruction. This also suggests that teaching decoding with pseudowords for one month can continue to improve decoding skills when children return to real-word phonics instruction. Teacher feedback suggests that confidence with reading increased for students who learned with pseudowords because they were less intimidated by the approach and viewed pseudoword phonics as a game that involved reading “silly” words. Implications of these results, limitations of this study, and areas for future research are discussed.

The work described in this thesis was conducted at the University of Nottingham in the Sir Peter Mansfield Imaging Centre, between September 2011 and 2014. Subject motion in high- resolution magnetic resonance imaging (MRI) is a major source of image artefacts. It is a very complex problem, due to variety of physical motion types, imaging techniques, or k-space trajectories. Many techniques have been proposed over the years to correct images for motion, all looking for the best practical solution in clinical scanning, which would give cost- effective, robust and high accuracy correction, without decreasing patient comfort or prolonging the scan time. Moreover, if the susceptibility induced field changes due to head rotation are large enough, they will compromise motion correction methods. In this work a method for prospective correction of head motion for MR brain imaging at 7 T was proposed. It would employ innovative NMR tracking devices not presented in literature before. The device presented in this thesis is characterized by a high accuracy of position measurements (0.06 ± 0.04 mm), is considered very practical, and stands the chance to be used in routine imaging in the future. This study also investigated the significance of the field changes induced by the susceptibility in human brain due to small head rotations (±10 deg). The size and location of these field changes were characterized, and then the effects of the changes on the image were simulated. The results have shown that the field shift may be as large as |-18.3| Hz/deg. For standard Gradient Echo sequence at 7 T and a typical head movement, the simulated image distortions were on average equal to 0.5%, and not larger than 15% of the brightest voxel. This is not likely to compromise motion correction, but may be significant in some imaging sequences.

Multi-view stereo is the process of recreating three-dimensional data from a set of two or more images of a scene. The ability to acquire 3D data from 2D images is a core concept in computer vision with wide-ranging applications throughout areas such as 3D printing, robotics, recognition, navigation and a vast number of other fields. While 3D reconstruction has been increasingly well studied over the past decades, it is only with the recent evolution of CPU and GPU technologies that practical implementations, able to accurately, robustly and efficiently capture 3D data of photographed objects have begun to emerge. Whilst current research has been shown to perform well under specific circumstances and for a subset of objects, there are still many practical and implementary issues that remain an open problem for these techniques. Most notably, the ability to robustly reconstruct objects from sparse image sets or objects with low texture. Alongside a review of algorithms within the multi-view field, the work proposed in this thesis outlines a massively parallel patch based multi-view stereo pipeline for static scene recovery. By utilising advances in GPU technology, a particle swarm algorithm implemented on the GPU forms the basis for improving the density of patch-based methods. The novelty of such an approach removes the reliance on feature matching and gradient descent to better account for the optimisation of patches within textureless regions, for which current methods struggle. An enhancement to the photo-consistency matching metric, which is used to evaluate the optimisation of each patch, is then defined. Specifically targeting the shortcomings of the photo-consistency metric when used inside a particle swarm optimisation, increasing its effectiveness over textureless areas. Finally, a multi-resolution reconstruction system based on a wavelet framework is presented to further improve upon the robustness of reconstruction over low textured regions.

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For decades, together with his colleague and wife Yetta, Kenneth Goodman has been lionized and vilified by educators and education decision makers on both sides of the Atlantic. Kenneth Goodman was on point of the attack on whole language in The Reading Wars in the mid-1990s. Goodman’s theories are something out of the ordinary in this research field, this study examined his work over decades and the criticism he received throughout the years of working on his theories. The reading war is not yet settled, even though most scholars today favours Phonics, which has become the most popular theory, and which is most often used in studies today. Because of that I felt the need to discuss Goodman’s theories, to see what he, with his work could offer the educators of today. / <p>Godkänt datum 2021-01-17</p>