Decimal to Binary Number Conversion can be Fun

Numbering systems are of great importance in Computer Science and Engineering education. The binary numbering system can be considered as one of the most fundamental, since its understanding is essential for the understanding of other Computer Science and Engineering concepts, such as data representation, data storage, computer architecture, networking, and many more. Yet, students are having difficulties understanding it. One approach which has been shown to improve learning of different science and mathematics concepts is the use of educational games. Educational games have the potential to engage and motivate learners through fun activities. This paper presents a small exploratory survey on an electronic educational game for practicing decimal to binary number conversions

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods

Technical Drafting and Mental Visualization in Interior Architecture Education

We explored how beginning-level interior architecture students develop skills to create mental visualizations of three-dimensional objects and environments, how they develop their technical drawing skills, and whether or not physical and computer generated models aid this design process. We used interviews and observations to collect data. The findings provide an insight on what kind of difficulties students experience during their learning process and how they overcome those difficulties. The results of the study indicate that the students’ lack of skills in technical drawing and in creating 2D and 3D mental visualizations negatively influenced their design process. Using the existing body of literature, we discussed the findings and suggested teaching strategies to improve the learning process for the beginning-level interior architecture students. The findings of this study allowed us to have a better understanding of the student design and learning process

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster

Algorithm-Aided Design with Python: Analysis of Technological Competence of Subjects

Difficulties in learning computer programming for novices is a subject of abundant scientific literature. These difficulties seem to be accentuated in students whose academic choice is not computation, like architecture students. However, they need to study programming, since it is part of the new academic curricula. The results presented here are part of a PhD research, which investigates the achievement motivation and the acquisition and transfer of programming knowledge from an online environment designed on the basis of the 4C-ID instructional design model. These results are a sociodemographic analysis, and the technological competence of these subjects. We concluded that most of the students of our sample do not know how to auto assess their ICT expertise level, because they believed that they had sufficient computational knowledge for their needs. However, most of them told that they had difficulties creating codes. However, they recognized the importance of learning to program, thought it was valuable for architectural students, and felt motivated to acquire this new skill.info:eu-repo/semantics/publishedVersio

The Prevalence of Specific Learning Difficulties in Higher Education: A Study of UK Universities Across 12 Academic Years

Specific learning and attention difficulties are often first identified in childhood but they can cause lifelong academic and occupational challenges. We explored the prevalence of these difficulties and the representation of sex and ethnicity amongst all first-year students in UK higher education across 12 years– almost 5.7 million students –and compared course preferences and University destinations of those with and without difficulties. Students declaring learning/attention difficulties were more likely to be White or of Mixed ethnicity and least likely to be Asian. They were more likely to attend specialist HE institutions or newer universities, and more likely to study courses in creative arts and design, agriculture and architecture than law, languages, computer science and mathematical sciences. The number of students declaring difficulties has increased year on year, in actual terms and as a proportion of the student body, suggesting that efforts to increase diversity and inclusion have been successful. However, differences remain between students with and without learning/attention difficulties in terms of ethnicity, subjects studied, and HE institutions attended, so more needs to be done to identify and address reasons for this. While this paper reports data from UK students, it addresses an international question and invites similar explorations of other national datasets

Emergent requirements for supporting introductory programming

The problems associated with learning and teaching first year University Computer Science (CS1) programming classes are summarized showing that various support tools and techniques have been developed and evaluated. From this review of applicable support the paper derives ten requirements that a support tool should have in order to improve CS1 student success rate with respect to learning and understanding