Media literacy, which is defined as accessing, analyzing, evaluating and producing media messages in a variety of contexts, has gained importance as media covers every part of our daily life. Individuals are expected to be media literate as well as other kinds of literacy. In this context, the aim of this study is to explore opinions of Science and Art Center students in Elazığ toward media literacy. This is a qualitative study and case study design which is one of the qualitative research methods was adopted. Study group of this research includes 11 gifted and talented students (six female- five male) enrolled at Elazığ Science and Art Center. Data of the study were collected with an interview form including eight semi-structured questions. Totally 11 high school students were interviewed during study process. Descriptive analysis was utilized to analyze the data. Study results revealed that the students had information about media literacy. They mostly used internet, television and cell phones. The students indicated that they watched serials on television. Google is the most used search engine and they frequently use social forums. They complained about false news and harmful web-sites and indicated that they preferred reliable sites. They used social media to communicate with their friends. They demanded to be informed and to gain auto control to save themselves from the harmful sides of media.

With the effect of the COVID-19 virus, which emerged at the end of 2019 and caused a pandemic in the world, education has experienced a significant interruption and the world has embarked on a quest for alternatives for educational practices. Distance education, where the learner and the teacher can perform education by being in different places, has reached the forefront with its applicability to pandemic conditions and has increased its importance. As a result of these developments, the concept of emergency distance education, which is a temporary transfer of face-to-face education to technological environments, has emerged and has been put into practice. Educational institutions, which had to start distance education practices in a very short time, experienced various problems due to student density and limited time. It is considered important in order to reveal the experiences of distance education staff who are directly faced with these problems, to offer solutions to the problems and to be prepared for extraordinary situations that may occur later. The purpose of this study is to identify the problems experienced by the staff of the distance education center (DEC) in the distance education applications carried out during the pandemic and to make suggestions for the solution of these problems. In line with this purpose, focus group interviews were conducted with a total of eight personnel, three of whom were administrative, four of whom were academic and one of whom was technical, working in different positions at the distance education center of a university. The sample was determined in accordance with the convenience sampling. The data obtained in the study using phenomenology, which is one of the qualitative research designs, were analysed by content analysis method. As a result of the study, it was determined that the problems experienced by DEC staff in this process were grouped under four categories as student-centred, instructor-centred, system-centred, and other problems, and solution suggestions were presented within the framework of these problems.

This study proposes a multi-objective, multi-class explainable modeling framework to explain country performance profiles in PISA Mathematics (PISAM), Reading (PISAR), and Science (PISAS). Instead of treating PISA as a simple ranking, the study models each country’s Low/Medium/High-achieving class and asks which structural signals the model relies on when assigning a country to this class. To this end, the study combines governance quality (e.g., accountability, control of corruption, and political stability, etc.), economic and administrative capacity, and regional/institutional location in a single prediction pipeline and explains the resulting classifications with SHAP contributions conditional on class. While the findings do not point to a single, universal determinant, in mathematics, high-level profiles cluster around political stability, economic scale barriers, and regional location, along with governance indicators; in reading, economic capacity is explicitly integrated into this institutional core; and in science, in addition to these two dimensions, the shared institutional dynamics of regional blocs come into play. Furthermore, the study not only produces explanations but also quantitatively reports their reliability. The fit with the model output (Fidelity) and the traceability of the decision logic (Faithfulness) are 0.95/0.85 for PISAM, 0.89/0.92 for PISAR, and 0.89/0.89 for PISAS, which demonstrates high internal consistency and traceability of the decision process. Overall, the study reframes the PISA results not as isolated test scores but as structural profiles generated by the combination of governance, capacity, and region, revealing the policy-relevant levers behind “high performance” as a transparent and reproducible decision-making pipeline. This provides policymakers with an important roadmap for creating a sustainable education policy.

Studies explaining the variation in educational outcomes across countries, when based on “black box” models that provide high accuracy but struggle to present the decision-making mechanism transparently, carry the risk of producing limited interpretations for policy discussions. This study examines the system-level relational patterns through which the subcomponents of the Heritage Foundation Index of Economic Freedom distinguish country-average low–medium–high PISA performance profiles in mathematics, reading, and science, and interprets these patterns using machine learning and explainable artificial intelligence (XAI). The analysis draws on approximately twenty years of nominal country-year records covering 76 countries. The study design proceeds through a classification approach, treating country performance as low–medium–high profiles; thus, model outputs are presented on an interpretable reference plane for cross-country comparisons. The findings indicate that the models demonstrate consistent generalization ability in distinguishing performance profiles and that the XAI layer produces explanations that make the model’s reasoning visible in a verifiable manner. The explanation results indicate that components representing institutional trust (such as government integrity and property rights) produce strong, recurring signals alongside higher performance profiles in all three areas; while components such as public expenditure and tax burden can emerge as balancing/suppressing signals in some scenarios. Rather than offering causal policy implications, these findings transparently reveal the structural areas that stand out in distinguishing performance profiles in cross-country comparisons, thus providing an explainable, replicable evidence base for comparative analysis and further research.