Student learning in higher education is influenced by parental involvement, peer support, and lecturers' perceptions of agreeableness. This research aims to examine the correlation between parental involvement, peer support, and the perception of personality agreeableness of lecturers on self-regulated learning (SRL) students. This research is a correlational field research model with a quantitative approach. The respondents of this research were 250 students of Yogyakarta State Islamic University who were obtained using a purposive random sampling technique. Data collection uses a scale of SRL, parent involvement, peer support, and perception of the agreeableness personality of the lecturer. Regression analysis is used as a data analysis technique. The results showed that there was a positive and significant correlation between parental involvement with SRL, peer support with SRL, personality perception agreeableness lecturers with SRL, and parental involvement, peer support, and perceptual personality agreeableness of lecturers together with SRL with an effective contribution (R2) of 15.1%. It was concluded that the involvement of parents, peer support, and perception of personality agreeableness of lecturers related to SRL of students. Therefore, to see student SRL can be seen based on the involvement of parents, peer support, and students' perceptions of the personality agreeableness of their lecturers.

Several concerted movements toward mathematical modeling have been seen in the last decade, reflecting the growing global relationship between the role of mathematics in the context of modern science, technology and real life. The literature has mainly covered the theoretical basis of research questions in mathematical modeling and the use of effective research methods in the studies. Driven by the Realistic Mathematics Education (RME) theory and empirical evidence on metacognition and modeling competency, this research aimed at exploring the interrelationships between metacognition and mathematical modeling and academic year level as a moderator via the SEM approach. This study involved 538 students as participants. From this sample, 133 students (24.7%) were from the first academic year, 223 (41.4%) were from the second and 182 (33.8%) were from the third. A correlational research design was employed to answer the research question. Cluster random sampling was used to gather the sample. We employed structural equation modeling (SEM) to test the hypothesized moderation employing IBM SPSS Amos version 18. Our findings confirmed the direct correlation between metacognition and mathematical modeling was statistically significant. Academic year level as a partial moderator significantly moderates the interrelationships between the metacognitive strategies and mathematical modeling competency. The effect of metacognition on mathematical modeling competency was more pronounced in the year two group compared to the year one and three groups.

The article discusses the topical issue of a model of digital competencies for a future teacher. The analysis of the composition and structure of the most relevant models of digital competencies of a citizen and a modern teacher is carried out. In addition, the article reveals approaches to the formation of the content of digital competence, and provides the results of an empirical study, which consists in analyzing the results of a survey of practicing teachers and teacher-training students in order to identify the most demanded digital competencies of a future teacher. The article substantiates the authors’ content of the competence of a future teacher, a university student. The purpose of this article is to develop a theoretical model of digital competence of a future teacher, taking into account the dynamic technologisation of the modern world and the peculiarities of Russian education, based on an analysis of approaches to determining the content of its digital competencies. According to the analysis of studies, the issue of teachers’ digital competence is not sufficiently disclosed. Numerous studies on digital competencies of a person, teacher, etc. do not fully solve the problem of assessing the digital competence of future teachers.

School quality has become a guiding concept that increasingly shapes educational planning and school development. For many decades, it has been a topic of significant interest, resulting in a wide-ranging and diverse research field. However, it is far from clear how school quality should be defined, what it should encompass, and how it influences student performance. The goal of this scoping review is to examine the existing evidence of the relationship between characteristics of school quality and student cognitive output/ student performance in secondary school. More precisely, it aims to (a) identify, (b) categorize, and (c) examine and evaluate the effects of characteristics of school quality affecting student performance and teaching characteristics in secondary school. In order to achieve these aims, we selected, clustered, and analyses 37 articles. The process was conducted by the research group through regular meetings, discussions, and consensus decisions. Our findings contribute to the comprehensive body of literature by identifying the following dimensions: aims and strategies for quality development, leadership and management, professionalism, school culture, and resources. Furthermore, the review revealed that although the field of school quality has been extensively researched, it lacks consistency, with many different operationalisations and definitions, making comparisons and syntheses challenging or even impossible. We believe that clear operationalisations and definitions are crucial to achieving comparability. Additionally, to achieve a standardized understanding of school quality and establish the categories internationally, uniform, theoretically sound, and content-related definitions of each category are necessary.

The study aims to point out direct and indirect correlations between others' roles on career self-efficacy, and career self-management. This research uses a multifactor correlational model, samples were 342 students. The research instruments are Career Self-Management Self-Efficacy Scale (CEDLE-Scale), Career Decision Self-Efficacy (CDSE Short-Form), and Role Model Influence Career Decisions Scale (RIMICDS). Data were analyzed using path analysis with AMOS 23. The findings show that 1) the variable of others' role directly influences career self-efficacy; 2) career self-management directly influences the career self-efficacy on career self-efficacy; 3) others' influence on career decision directly influences the career self-management, and 4) others' role on the career decision indirectly influences self-efficacy moderated by career self-management. Support, guidance, and inspiration from others will ease someone in deciding on a career. Guidance for managing a career will improve career self-efficacy. It will then positively contribute to the self-confidence to pursue the career.

The world is looking forward to advancements in artificial intelligence (AI) technology, with significant research underway regarding the application of AI in education. This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects. Data from the Scopus database revealed 1,764 publications from 2019 to 2024. The analysis showed a steady annual growth in publications in this area. China and the USA were the leaders in the number of publications. Xiaoyue Wang was the most prolific researcher, having authored 71 AI-related articles. Yueying Li, Xiaoxu Chen, Yanzhu Zhang, and Yi Liu contributed to the field with 56, 55, 53, and 51 articles, respectively. The themes that emerged from 2019 to 2022 are related to media, application, study, institutions, artificial, digital, learning, factors, development, technologies, medical, automated, perception, support, and sustainability. From 2023 to 2024, the topics discussed in AI are related to students, education, perception, algorithms, digital, prediction, networks, challenges, writing, teachers, AI-powered, curriculum, century, integration, technology, and framework. The difference in research in 2019-2022 and 2023-2024 is focusing the theme's focus from the general to the specific. The co-occurrence analysis revealed that prominent keywords appeared in 3 clusters. Cluster 1 is the most popular in recent times. It deals with the application, assessment, and management of AI. Cluster 2 relates to AI relationships and models, while Cluster 3 relates to AI data sources.