Çalışmanın amacı; üniversitelerin Covid-19 döneminde gerçekleştirdikleri uzaktan öğretim faaliyetlerini ve salgın sonrasına ilişkin uzaktan öğretime yönelik planlamalarını incelemektir. Bu amaçla, Yükseköğretim Kurulu (YÖK) tarafından belirlenen standartlara göre oluşturulmuş 189 üniversiteye ait Covid-19 dönemi faaliyet raporları incelenmiştir. Nitel araştırma yöntemlerinden içerik analizi kullanılarak yürütülen bu çalışmada veri toplama yöntemi olarak ise doküman incelemesi kullanılmıştır. Veri toplama aracı olarak araştırmacılar tarafından oluşturulmuş “Üniversitelerin salgın dönemi faaliyet inceleme formu” kullanılmıştır. Elde edilen veriler içerik analizi yöntemiyle analiz edilmiş ve kod, kategori ve temalar oluşturulmuştur. Çalışma sonucunda üniversitelerde salgın döneminde uzaktan öğretim uygulamalarının üç hafta içerisinde tamamen başladığı ve uygulamalarda çeşitlilik olduğu görülmüştür. Bu dönemde bazı üniversiteler ders notu ve ilgili kaynaklar paylaşırken bazılarının ise asenkron uygulamaların yanında farklı yoğunluklarda senkron uygulamalara yer verdiği görülmüştür. Yine salgın döneminde farklı araçlar kullanıldığı ve bunların birçoğunun salgın döneminde temin edildiği görülmüştür. Üniversitelerin ilk kez zorunlu olarak geçtikleri bu uygulamada öğrenci ve öğretim elemanlarını desteklemek için mevcut kaynakları kullanarak öğrenci ve öğretim elamanlarına ulaşmaya çalışmış, farklı seviyelerde destek hizmetleri yürütmüştür. Son olarak uzaktan eğitim altyapısına sahip üniversitelerin mevcut sistemi güçlendirmeye yönelik çalışmalar gerçekleştirdiği, bu konuda çok fazla birikimi olmayan üniversitelerin ise daha büyük çaplı altyapı yatırımlarının yanında kurumsallaşmaya yönelik ders ve program seviyesinde yeni planlamalar yaptıkları görülmüştür.

The study aims to reveal the studies' profile for distance education activities in the Covid-19 period. For this purpose, bibliometric analysis and content analysis were used together in the study. Within the scope of the study, 220 studies were selected in the Web of Science database to access the articles. Within the scope of bibliometric analysis, the most used keywords, the most cited journals, the most publishing journals, the most publishing countries and the most cited authors were analyzed with the VOSViewer program. In content analysis, methodological trends (method, sample size, data collection tool, data analysis type) used in the studies were examined. As a result of the research, the most publications were made in the last quarter of 2020, 16% of the studies were single-author and the quantitative research method was the most preferred (40%), the sample was mostly university students (n=78). It was observed that it was clustered between 0-100 intervals, the most frequently used questionnaire (n=105) as the data collection tool, and the descriptive-quantitative analysis method (40%) was used as the data analysis method. Also, the most publications are made in America and China, the most used keywords are "covid-19", "online education" and "distance education", the most cited authors are Chen, Cong, Peng and Yang, and finally It has been observed that the highly cited journal has Sustainability.

Rapid changes in the field of technology have greatly affected learning and teaching styles as in every field. Artificial intelligence (AI), which is increasingly used in the field of education for different purposes such as improving learning outcomes or solving teaching problems, is one of the important technologies that contribute to this change. It is thought that there is a need for applications based on AI technology that can meet these needs in terms of sustainability of learning, especially in ODE environments where individual learning and instant feedback are important. Accordingly, the study aims to determine the impact of AI-supported flipped classroom applications on students’ AI literacy and to evaluate their experiences and perceptions towards these applications. Explanatory sequential mixed design was used as the method in the study. In this context, in the quantitative phase of the study, a one-group pretest-posttest design from the weak experimental design was used to determine students’ perceptions of flipped learning and AI literacy levels. In the qualitative phase of the study, a case study was used to determine students’ views on learning experiences. The sample comprised 33 university students enrolled in a Web Design and Coding associate degree program. Data were collected through a flipped learning perception scale, an AI literacy scale, and semi-structured interviews. Quantitative findings revealed a statistically significant improvement in students’ AI literacy levels from pre-test to post-test. Qualitative analyses indicated that AI-supported flipped classroom practices positively influenced students’ motivation, interest in the course, and personalized learning experiences. Furthermore, the model fostered collaboration by promoting interactive learning environments and enhanced critical thinking and problem-solving skills. However, students also reported challenges related to managing their time effectively within this instructional model. The study concludes that AI-supported flipped classroom applications hold promise for improving AI literacy and fostering active, student-centered learning in higher education. Nevertheless, challenges such as time management warrant further attention. It is recommended that future research explore the implementation of this model across diverse courses and educational contexts, investigate strategies to mitigate time management issues, and conduct comparative studies between AI-supported and traditional flipped classroom models to further elucidate the sources of observed positive perceptions.

This study aimed to examine the digital transformation activities of universities. The document analysis method was used in the study, one of the qualitative research methods. In this context, the websites of 207 universities were examined using the "Digital transformation activities form of universities" developed by the researchers. The data were analyzed by content analysis method. As a result of the analysis, six broad themes, also called dimensions in the current study, emerged, such as research, education, culture, process, technology, and community service regarding digital transformation activities of higher education institutions. Infrastructure studies, educational activities for research and supply and the use of smart systems to provide various cooperation opportunities are discussed within the scope of the research dimension. There are online scientific studies, online education programs, digital resources, and open access opportunities in the education dimension. As for the culture dimension, online manager activities for corporate culture, training of trainers, culture-arts and scientific activities took place. With the transfer of business processes to the digital environment, corporate promotion and communication activities were included in the process dimension. In the technology dimension, smart systems, smart campus applications and innovative technologies took place. Finally, the scope of community service compromised related units, university-industry cooperation activities and social activities. As a result, universities carry out activities to support processes such as education, research, management, and cooperation and to realize them in digital environments. For this purpose, it can be said that they perform effective and efficient processes by using various intelligent systems.

The increasing role of Artificial Intelligence (AI) applications in educational activities is also evident in online education. By providing personalised and adaptive learning experiences and offering support for teachers by tracking student behaviours to make inferences about student success and performance, AI reduces teachers' workload, allowing them to focus more on educational activities. AI applications especially enable the rapid and easy completion of assessment and evaluation activities, which teachers often spend significant time and effort on. Within this scope, AI-supported tools are used in online education activities to perform automatic assessments, provide real-time feedback, and conduct data analysis and performance evaluation activities. In this context, the aim of the study is determined as the examination of measurement, evaluation, and feedback activities in online education with AI-supported tools.