Education policies help to improve society by decreasing inequities and establishing effective learning environments. Periodic assessments can help researchers and policymakers uncover new obstacles and ensure progress on these ever-changing education policy concerns using critical theory. Bibliometric studies, a type of periodic study, emphasize the importance of data-driven approaches in the formulation and implementation of education policies. This study conducts a bibliometric analysis of education policy issues from 2000 to 2023. Based on keywords, we initially selected 931 articles from the Web of Science (WoS) database. Only articles in English were included, and we used PRISMA guidelines to reduce the number of articles to 363. We focused on citations, publication frequency, topics, trends, and issues. Two independent researchers analyzed the documents for reliability. For validity, we used transferability. We also used a content analysis of frequently cited articles. Our analysis revealed three prominent trends. The first trend pertained to controversial environmental issues and sustainability concepts in education policy. The second theme was professional development, special education, and school choice. The third one was science, vocational education, special education, and ICT. The content analysis results indicated that teaching and learning, professional development, science education, subject matter teaching, and mobile learning were the topics of the content analyzed articles. We found that relying solely on bibliometric review resulted in broad conceptualizations of educational policy issues, focusing primarily on efficiency and effectiveness. Additionally, we applied critical theoretical frameworks to conduct a more comprehensive analysis of the emergent issues identified through bibliometric analysis.

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th, 7th, and 8th grades attending a school in Central Anatolian City in Turkey, participated in this study in the 2022-2023 school year. Data was collected with a. A deep learning method called deep neural network, one of the ways of machine learning, was used in data analysis. The average number of books read per year had the highest importance among the variables affecting academic performance in science courses. In addition, deep learning predicted students' final science scores with 90% accuracy. According to the results of this study, the percentage of the academic achievement prediction might be raised by reproducing the required data set for the data analysis method with deep learning. A forecast of the student’s academic achievement with artificial intelligence and detecting the importance of variables’ percentage might be researched for other courses in addition to the Science course.

The scholarship of teaching and learning (SoTL) forms a significant base for higher education. SoTL has the potential to lead higher education institutions in terms of teaching, research, and service. The purpose of the current study is to perform a bibliometric analysis of the scholarship of teaching and learning in higher education. A bibliometric review was conducted in both Scopus and Web of Science databases, including 1491 and 1002 studies published in the sources indexed, respectively, in Scopus and Web of Science from the past to 2020. PRISMA was used to identify and select the documents in the sample. The data were analyzed using Scopus Analytic Tools, Excel functions, and VOSviewer. A considerable upward trend was observed in the number of documents from the past until 2020. “ASEE Annual Conference” was found to be the most influential source in Scopus, while “Teaching Sociology” was found to be the most influential source in Web of Science. Kreber, C., Healey, M., and Marquis, E. were the authors most drawing attention to both Scopus and the Web of Science. The intellectual structure of the knowledge base was based mainly on student voice, scholarship or teaching, and learning tips for both databases. Temporal analyses showed that the topical foci of SoTL were identity and critical pedagogy for Scopus and leadership and sustainability for Web of Science.