Writing Workshop is an interactive approach to teaching writing as students learn and practice the importance of rehearsal, drafting/revising, and editing their pieces of writing (Calkins, 1986;Graves, 1983). This study implemented a mixed methodology design incorporating qualitative and quantitative analysis (Mills, 2007) by administering a pre survey to each child before he/she began the Writing Workshop and a post survey after the intervention; systematic observational research as a checklist (Glanz, 2003) to record observed practices of students during peer revising conferences; portfolios to assess student writing and graded via a rubric; and lastly interview of students regarding confidence and ability in writing. Therefore, the purpose of this study was to explore the writing processes of drafting/revising and editing to support first grade students to become independent writers.

No abstract

The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces system complexity. The softmax layer in VGG assumes that the training samples belong to exactly only one class, which is not valid in a real situation, such as in medical image diagnosis. To overcome this situation, SVM is employed instead of the softmax layer in VGG. Data augmentation is also employed as DLA usually requires a large number of samples. VGG model with different SVM kernels is built to classify the mammograms. Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society (MIAS) database images with an accuracy of 98.67%, sensitivity of 99.32%, and specificity of 98.34%.

No abstract

No abstract