In the study, a meta-analysis was conducted to determine the effect of the use of the learning cycle model on the achievements of the students. Doctorate and master theses, made between 2007 and 2016, were searched using the keywords in Turkish and English. As a result of the screening, a total of 123 dissertations, which used learning cycle models to increase the achievement of students, were included in the analysis. As a result of this study, it is confirmed that the effect of learning cycle models on students’ achievement is positive and the determined effect size was found out as 1.164 (% 95 CI, SE = .071) according to random effects model.. In the study, moderator analysis was made according to the learning cycle models, type of the dissertations, disciplines, and education levels of students. The analyses showed that among the learning cycle models, the highest effect size was determined in the 4E Model (2.659), among the dissertations the highest effect size was in master thesis (ES = 1.231), among the disciplines the highest effect size was in the other lessons (ES = 1.637) and among the educational levels the highest effect size was in the high school students (ES = 1.237).

This present study imparts a literature review focusing on Augmented Reality (AR) in the field of education. The writers reviewed the published manuscripts by centering on types of manuscripts, research designs, types of participants, and the promising roles of AR in educational setting. A total number of 18 articles were thoroughly analyzed. The results showed a diverse type of manuscripts published in the journal databases including research articles, reviews, technical notes, features, and news. Types of research designs also varied such as Research and Development, Experimental, Content Analysis, Exploratory, Action Research, Descriptive Study, Survey, Mixed Method, and Design-Based Research. Types of participants embraced a diverse range of participants including university students, primary students, secondary students, teachers, and children with special needs. The most prominent role of AR was that it improved students’ knowledge and understanding of the materials. Current gaps in AR-related studies in educational setting are recognized, and several recommendations for conducting upcoming studies are proposed.

Astronomy concept is regarded as one difficult topic in both teachers’ and students’ perspective even though it strongly appeals to the human mind. This concept requires imagination and the ability to use various skills and knowledge, for example, actual motion, relative position, and coordination of views from several points on the Earth to generate an explanation.  As mentioned in the literature, the teacher plays a vital role in the teaching and learning process. Therefore, this study aimed to investigate 45 in-service science teachers’ understanding of astronomy concepts in a professional development program and to diagnose the misconceptions regarding astronomy concepts. A two-tier test, open-ended questions, and a semi-structured interview were used to gather information on the understanding and misconceptions, particularly on celestial motion concepts. The data were analyzed both qualitatively and quantitatively. The results pointed out that after the four-day professional development program, in-service science teachers gained significantly higher two-tier test scores. The highest progression was in the topic of the Sun’s apparent motion. However, most in-service science teachers still held some misconceptions relating to the concept of the seasons. The obstacles in teaching astronomy were also discussed. The study’s findings lead to the improvement of the professional development program for in-service science teachers required to teach astronomy concepts nationwide.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.