Veterinary students' usage and perception of video teaching resources

<p>Abstract</p> <p>Background</p> <p>The purpose of our study was to use a student-centred approach to develop an online video learning resource (called 'Moo Tube') at the School of Veterinary Medicine and Science, University of Nottingham, UK and also to provide guidance for other academics in the School wishing to develop a similar resource in the future.</p> <p>Methods</p> <p>A focus group in the format of the nominal group technique was used to garner the opinions of 12 undergraduate students (3 from year-1, 4 from year-2 and 5 from year-3). Students generated lists of items in response to key questions, these responses were thematically analysed to generate key themes which were compared between the different year groups. The number of visits to 'Moo Tube' before and after an objective structured practical examination (OSPE) was also analysed to provide data on video usage.</p> <p>Results</p> <p>Students highlighted a number of strengths of video resources which can be grouped into four overarching themes: (1) teaching enhancement, (2) accessibility, (3) technical quality and (4) video content. Of these themes, students rated teaching enhancement and accessibility most highly. Video usage was seen to significantly increase (P < 0.05) prior to an examination and significantly decrease (P < 0.05) following the examination.</p> <p>Conclusions</p> <p>The students had a positive perception of video usage in higher education. Video usage increases prior to practical examinations. Image quality was a greater concern with year-3 students than with either year-1 or 2 students but all groups highlighted the following as important issues: i) good sound quality, ii) accessibility, including location of videos within electronic libraries, and iii) video content. Based on the findings from this study, guidelines are suggested for those developing undergraduate veterinary videos. We believe that many aspects of our list will have resonance in other areas of medicine education and higher education.</p

Hierarchical Interpretation of Fractal Image Coding and Its Applications

The basics of a block oriented fractal image coder are reviewed. The output of the coder is an IFS (Iterated Function System), which approximates the image as a fixed point of a contractive transformation. A new hierarchical interpretation of the IFS code, which relates the different scales of the fixed point, is introduced. We prove the existence of a unique function of a continuous variable that is associated with the IFS code. It is further shown that the different scales of the IFS fixed point are directly computable from this so called IFS embedded function. The computation of the IFS-code, depends on the sampling method, an issue that is also discussed. A matrix representation of the IFS code is described and related to the fractal dimension of the IFS embedded function. An application to a new super-resolution method, using an IFS-code, is demonstrated, and its characteristics are analyzed. Another application of the hierarchical representation to fast decoding is also presented..

A Multi-Resolution Framework for Fractal Image Representation and its applications

The starting point of this paper is the basic fractal coder suggested by Jacquin. The coder finds and encodes the parameters of a partitioned iterated function system (PIFS), which approximates the signal as a fixed-point of a contractive transformation. The work presented here can be divided into two parts. The first part begins with a presentation of the hierarchical structure of the PIFS code. This structure relates the code and its fixed-point in different resolutions. It is shown that there exists a function of a continuous variable which is directly related to the PIFS. It is shown that by properly manipulating this function, called the PIFS embedded-function, one can compute the fixed-points related to the code in any desired resolution. We end the first part with a brief description of several applications, such as a fast non-iterative decoder, a method for fractal interpolation of the signal via its PIFS code, and an improved collage-bound. This research was supported by the ..