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Improving academic quality requires feedback from students since they have specific insight in their lived educational experience. Most institutions assess educational experience by evaluating individual modules. Few institutions collectively review evaluation data from multiple modules or explore this data for evidence of curriculum transformation. The University of Pretoria developed four curriculum transformation drivers and the School of Health Systems and Public Health developed and implemented 19 transformation-related statements. We revised the standard endof- module evaluations to explore student experiences regarding the implementation of these drivers. 45 modules were reviewed. The quantitative data were analysed in Excel. Qualitative data were analysed using an inductive approach with open-coding. We generated categories and themes before comparing these to the curriculum transformation drivers. Four major themes emerged from the qualitative data: reflecting the "what", "why", "when", "where", "how" and "who" of the student experience. Categories and subcategories were explored and linked to the quantitative results. The results show that the students valued their educational experience and felt valued as students. The students' voices provided a rich source of data regarding curriculum transformation and included detailed suggestions for quality improvement. Our study presents substantive evidence of authentic educational and philosophical intentions that were realised in practice. [ABSTRACT FROM AUTHOR]

This paper investigates the reconstruction of Van Gogh's drawings which have been degraded in the course of time due to aging problems, like ink fading and discoloration. Learning to predict the past and original appearances of degraded drawings can help to envisage how the artist's work may have looked at the time of creation. In this paper, we use reproductions as reference information for the past appearances of drawings and consider the reconstruction of drawings as a pixel-wise prediction problem. We present an approach to automatically predict the past appearances of drawings. This approach brings together methods from multi-resolution image analysis and deep convolutional neural networks (CNNs) for addressing the task of pixel-wise prediction. Our experiments first investigate how scale affects prediction performance of the proposed multi-scale CNN framework and then demonstrate the reconstruction capability of the multi-scale CNN framework. The results demonstrate that the predictive reconstruction of degraded images is a feasible endeavor. [ABSTRACT FROM AUTHOR]