WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Conference Object Citation - WoS: 1Citation - Scopus: 1Empathy Development in Digital Accessibility Through Real-Life Practices in a Programming Course: a Case Study(Assoc Computing Machinery, 2024) Inal, Yavuz; Cagiltay, NergizThis case study adopted a project-based learning approach to a programming course based on real-life practices to help software engineering students develop empathy skills regarding digital accessibility. A project was assigned to first-year students to develop software for people with disabilities. The data were collected from each individual project of thirty-three students over four months. Students' efforts regarding analysis, design and development steps, and project outcomes were analyzed. The study results showed that students' experience level and knowledge about the accessibility domain were quite low initially. Regarding the target disability type in their projects, half of the students selected mental illness, followed by blindness, deafness, and physical illness. The students who gathered requirements from domain experts or target users made their products more accessible, indicating the importance of user involvement in empathy building in the development process. We also measured increased awareness of and knowledge about accessibility at the end of the course, leading us to discuss the effectiveness of real-life practices in teaching digital accessibility in programming courses.Conference Object Analysis of Neurooncological Data To Predict Success of Operation Through Classification(Assoc Computing Machinery, 2016) Tokdemir, Gul; Cagiltay, Nergiz; Maras, H. Hakan; Bagherzadi, Negin; Borcek, Alp OzgunData mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.