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The aim of this research is to reveal the awareness of teachers working in inclusion environments and their use of educational technology. Specifically, in this mixed methods study, a needs analysis was conducted to determine the knowledge levels of teachers about inclusive education, their awareness and use of educational technologies, and what kind of support they need. Data were collected through a questionnaire with 133 teachers and focus group discussions with 5 groups of 30 teachers. Based on the analysis of quantitative data using descriptive analysis and qualitative data employing the constant comparison method, the findings showed that teachers' knowledge of special education needs (SEN) students is insufficient and they need support at this point. In addition, the qualitative data analysis unveiled social, academic, and environmental positive outputs of inclusive education alongside the identification of the psychological, academic, and environmental challenges of inclusive education. The findings are expected to benefit the literature, the field of inclusive education, and policymakers. [Note: The page range (115-131) and year shown on the PDF is incorrect. The correct page range is 116-131. The correct year is 2024.]

This study investigates the primary school teachers' perceptions towards the Intelligent Material System (IMS), which was developed to enhance the quality of inclusive environments and support the academic skills of students with Special Educational Needs (SEN). Within this scope, IMS training was conducted with 84 classroom teachers who worked at primary schools in Istanbul. Following the training, an open-ended questionnaire and two focus group interviews were used to collect data. The findings revealed that IMS has the potential to support the students with SEN academically, as it provides motivating and rich content, has a user-friendly interface and a desired level of accessibility despite the system's limitations and the need for improvements noted by the participants.

This research explores how metacognitive strategies influence the metacognitive awareness of undergraduate students enrolled in an online flipped programming course. It specifically focuses on regulatory actions crucial for success in programming instruction and distance education settings. The primary objective is to contribute to the existing literature by investigating the implementation of online flipped programming courses that integrate metacognitive-oriented approaches to support students' metacognitive regulatory actions. The study employed an explanatory sequential mixed methods design. A total of 29 university students enrolled in programming courses participated in the study, engaging with instructional videos provided before each 10-week lesson. They were administered the Metacognitive Awareness Scale and supplementary forms designed to assess their metacognitive awareness and regulatory actions. A detailed coding scheme was developed to analyze students' metacognitive regulation activities during programming lessons. The study also evaluated the impact of supportive activities on students' metacognitive awareness. While no statistically significant difference was found in the students' metacognitive awareness through quantitative analysis, qualitative data revealed that activities supporting metacognition significantly enhanced students' comprehension of the programming content.

With the advancement of artificial intelligence (AI) and machine learning (ML) techniques, attitudes towards these two fields have begun to gain importance in different professions. One of the affected professions is undoubtedly the teaching profession. Increasing the levels of concern for artificial intelligence and attitudes towards machine learning has become important in order to adapt to potential technologies that will be used. The purpose of this study is to examine the anxiety related to AI and the attitudes towards ML among teacher candidates of different ages, genders, and fields. This study investigates the relationships between sub-dimensions of anxiety towards artificial intelligence and attitudes towards machine learning, as well as to identify differences in these sub-dimensions among gender, age, and department. The findings suggest that although teacher candidates from different disciplines, ages, and genders do not have any concerns regarding learning about artificial intelligence, they do express anxiety about the impact of artificial intelligence on employment rates and social life. The results of this study can be beneficial for developing instructional programs that focus on AI in the long run, considering factors such as age, personal experience, gender, and field-specific elements.

E-learning is a method that meets the time and distance needs of postgraduate medical education. The general workload of postgraduates and disasters such as the COVID-19 pandemic create a need for e-learning. Therefore, in recent years, e-learning has drawn the attention of researchers because of its potential as a practical instructional approach in PGME. Systematic review method was employed in this study aiming to examine the e-learning applications in PGME through a summary of several recent studies that meet predetermined criteria regarding e-learning for postgraduate medical education. The PICOS (population, intervention, comparison, outcome, study design) framework was used to decide on inclusion and exclusion criteria. On reporting the results of the study, The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was used. The review examines the differences between the identified studies in terms of research methods, data collection tools, study duration, sample size, and data analysis method. In addition, it compares the learning technology used, the format of e-learning delivery, types of e-learning, e-learning delivery features, learning content, content type, knowledge type, and learning outcome components of the studies. 57 studies that matched the desired quality indicators were identified from 10 databases. The majority of the studies worked on knowledge, attitude, and satisfaction. The studies were based on various methodologies and were carried out in a wide range of subject areas. There is a need to conduct more studies to compare different methods, tools, and pedagogical approaches in future studies.

The role of techniques involving Artificial Intelligence (AI) has been becoming increasingly important in educational settings. This study aims to reveal the recent trends in research into artificial intelligence in special education by using the systematic review method. Across the 29 studies published between 2008 and 2020 that are reviewed here, the majority are articles on quantitative research carried out in the United States. In terms of learning content, most of the studies are about skill development, focusing on cognitive and affective factors. Moreover, the research was carried out in school settings with learners from various backgrounds. The purposes for and methods of using AI were examined and it was found that software-based methods are more common. The primary disability type examined in the articles covered in this review were disorders on the autism spectrum. There is a tendency toward technical models rather than educational models. ANN and SVM are the most used technical theoretical infrastructures. This study yielded interesting results both on the evolution of AI in special education over the years and on its future development. This paper ends with suggestions developed in light of these studies for the use of artificial intelligence in special education.