Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs), whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning. Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination of MRI data is a time-consuming process and error prone due to human intervention. In this study we introduced a custom convolutional neural network (CNN) based deep learning model trained from scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet through transfer learning for an effective glioma grade prediction. We trained and tested the models based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of data augmentation techniques was used to expand the training data. Five-fold cross-validation was applied to evaluate the performance of each model. We compared the models in terms of averaged values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve (AUC). According to the experimental results, our custom-design deep CNN model achieved comparable or even better performance than the pretrained models. Sensitivity, specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971 and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1 score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893, 0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the deep CNNs and transfer learning approaches can be very useful to solve classification problems in the medical domain.

En tr With the development of cloud computing technology, learning management systems (LMSs) have gained new features and service options. Consequently, the process of choosing among increasing product alternatives has become difficult. Choosing the most appropriate cloud-based LMS with certain criteria has been an important decision-making problem. In this study, a model has been developed by using Fuzzy Analytic Hierarchy Process (FAHP) to facilitate selection of the most suitable one from a group of cloud-based LMSs according to certain criteria. In this model, content support, interaction and collaboration, measurement and evaluation, course structure, interface, productivity tools, platform flexibility, scalability, security, support and licensing criteria that can be effective in cloud-based LMS selection are examined based on literature and expert opinions, and presented in a hierarchical structure. In the study, both the criteria and six alternatives in the context of a case study were evaluated by seven decision-makers specialized in the field of online education. Based on the determined criteria, the most appropriate cloud-based LMS was chosen as TalentLMS as a result of fuzzy pairwise comparisons between the selected alternatives. Bulut bilişim teknolojisinin gelişmesiyle öğrenme yönetim sistemleri (ÖYS’ler) yeni özellikler ve servis seçenekleri kazanmıştır. Buna bağlı olarak artan ürün alternatifleri arasından seçim yapma süreci zorlaşmıştır. Belli kriterlere bağlı en uygun bulut tabanlı ÖYS’yi seçmek kurumlar için önemli bir karar verme sorunu olmuştur. Bu çalışmada, kurumların bir grup bulut tabanlı ÖYS arasından belli kriterlere uygun seçim yapabilmesini kolaylaştıracak Bulanık Analitik Hiyerarşi Süreci (BAHS) tabanlı bir model geliştirilmiştir. Bu modelde, bulut tabanlı ÖYS seçiminde etkili olabilecek içerik desteği, etkileşim ve iş birliği, ölçme ve değerlendirme, ders yapısı, arayüz, verimlilik araçları, platform esnekliği, ölçeklenebilirlik, güvenlik, destek ve lisanslama kriterleri literatüre ve uzman görüşlerine dayalı incelenmiş ve oluşturulan bir hiyerarşik yapı ile sunulmuştur. Çalışmada hem kriterler hem de durum çalışması kapsamında ele alınan altı alternatif, çevrim-içi eğitim alanında uzman yedi karar verici tarafından değerlendirilmiştir. Belirlenen kriterlere bağlı olarak, seçilen alternatifler arasında yapılan bulanık ikili karşılaştırmalar sonucu en uygun bulut tabanlı ÖYS, TalentLMS olarak belirlenmiştir.

Usability analysis in software has a critical role in creating useful products for users and obtaining the necessary feedback. Learning Management Systems (LMSs) are among the most frequently used software in distance education. The usability of these systems is crucial for both student success and the quality of the service offered. This study examines LMS usability through faculty perceptions at a state university, employing a mixed-methods design. Quantitative data were collected using the System Usability Scale (SUS) from 109 faculty members, while qualitative insights were gathered from semi-structured interviews with nine participants. Statistical and thematic content analyses were employed to interpret and compare results. The quantitative analysis yielded an average SUS score of 63.85 ± 16, indicating moderate usability concerns. Based on findings from both the SUS responses and interview data, several recommendations were proposed, such as enhancing system infrastructure, simplifying the interface, improving instructional guidance, strengthening interaction features, optimizing file management, refining notification systems, addressing character encoding issues, and streamlining listing and reporting functions. The results underscore the importance of a user-centered development approach, incorporating participatory design principles. Future research should track how faculty adapt to LMS updates over time. We hope these findings will guide future usability studies.

Although the number of online academic degree programs offered by universities in Turkey has become increasingly significant in recent years, the current lack of understanding of administrators’ motives that contribute to initiating these programs suggests there is much to be learned in this field. This study aimed to investigate administrators’ perceptions of motives for offering online academic degree programs in universities in Turkey in terms of online associate degree programs, online master's degree programs, online bachelor's degree completion programs, and online bachelor's degree programs. A qualitative research method was employed for this study. Semi-structured interviews were conducted with 16 administrators from different universities’ distance education centers in Turkey and thematic analysis was applied to the data. The research found that administrators’ motives for offering online academic degree programs mainly involve in answering to the high demand of prospective students. Six major themes were identified with regard to influencing factors for administrators’ motives: demands for programs, mission to support education, readiness of infrastructure, teaching staff as well as applicability of content, overcoming the shortage of classroom space and teachers, obtaining revenue, and gaining prestige.