Higher education institutions have focused their efforts on promoting research seedbeds as a strategy for formative research. In this regard, the impact of such a strategy remains unknown due to the lack of models that enable its evaluation. Therefore, this study aimed to design an evaluation model for the academic productivity of research seedbeds based on the available evidence in the literature. To achieve this, a systematic review was conducted following the PRISMA model, analyzing 53 documents including articles, book chapters, and conference proceedings from the SCOPUS, ProQuest, Jstor, Scielo, and ScienceDirect databases. The results identified indicators that allowed for the design of a model based on six constructs: research training, institutional capabilities, bibliographic production, innovation and development, social appropriation of knowledge, and human resource training. It was concluded that the indicators evaluating research seedbeds seek greater scientific development involving students and improving the quality of research products, which directly impacts the institutional research mission.

School attendance problems are of great research interest, which is reflected in the increase of scientific publications. This increase hinders the adequate follow-up and updating of the scientific community on the subject. The aim of the present bibliometric study lies in the review of the scientific literature published on school attendance problems during 2014-2021. A bibliographic search and analysis of scientific articles was performed, obtaining a definitive sample of 700 documents. Results were extracted and analyzed for the following indicators: temporal productivity, productivity by authors, co-authorship index, productivity by journals, use of topics, research areas addressed and types of samples used. The number of publications indicates a progressive increase of interest on the subject, which has not corresponded to the creation of a specific journal on the subject. There is also evidence of the need for consensus on the topics to be used; the preference for knowing the factors associated with school attendance problems over other areas of research; and the generalized use of community samples as opposed to more specific ones. In conclusion, the characteristics researched on school attendance problems are presented; knowledge that will facilitate the establishment of intervention processes applicable to different contexts and realities.

The Attraction-Selection-Attrition (ASA) model is a prominent framework for supporting employee retention, stating that organisations attract, select, and retain people who share their values. However, the ASA model only extends to the end of the recruitment stage and lacks clarity on how to assist newcomers in the assimilation process when they first join the organisation. This research proposed a refinement of the ASA paradigm by incorporating the assimilation process of new hires into the new school culture and environment. This study employed a qualitative research approach by interviewing ten participants about the retention process from high teacher retention international schools in Malaysia. Thematic data analysis revealed a new paradigm, 'Attraction-Selection-Onboarding-Retention (ASOR), ' designed to increase teacher retention in international schools. The ASOR model could assist school administrators and human resource managers working in a related setting in properly engaging the workforce to increase teacher retention. This would benefit school sustainability, performance and the local community's economy.

Faculty members’ beliefs in their ability to conduct research and publish research outputs are expected to impact research productivity directly. Thus, the study described the research self-efficacy and productivity among faculty members, their research self-efficacy influence on productivity, and their challenges in research writing and publication. The study utilized a mixed-method sequential explanatory research design, with 36 and nine faculty member-participants for the quantitative and qualitative study. For the quantitative study, the faculty members’ research self-efficacy was ascertained using a validated questionnaire, and their research productivity was determined through a researcher-made survey instrument. Meanwhile, the qualitative study focused on the faculty members’ research writing and publication challenges, which were gathered through focus group discussions. Results showed average research self-efficacy and low research productivity among faculty members. Research self-efficacy significantly predicted research productivity regarding refereed and indexed publications, paper presentations, and bibliometrics. Further, themed findings showed that the faculty members encountered challenges such as a lack of research exposure, time constraints, lack of institutional support, and publication pressure. The study may serve as an inference for higher education institutions in designing faculty development plans and in-service training programs to capacitate its members.

The integration of AI tools in education is reshaping how students view and interact with their learning experiences. As AI usage continues to grow, it becomes increasingly important to understand how students' perceptions of AI technology impact their academic performance and learning behaviours. To investigate these effects, we conducted a correlational study with a sample of 44 students to examine the relationship between students' perceptions of ChatGPT’s utility—focusing on usage frequency, perceived usefulness, accuracy, reliability, and time efficiency—and key academic outcomes, including content mastery, confidence in knowledge, and grade improvement. Additionally, we explored how these perceptions influence student behaviours, such as reliance on ChatGPT, procrastination tendencies, and the potential risk of plagiarism. The canonical correlation analysis revealed a statistically significant relationship between students' perceptions of ChatGPT's utility and their academic outcomes. Students who viewed ChatGPT as reliable and efficient tended to report higher grades, improved understanding of the material, and greater confidence in their knowledge. Furthermore, the bivariate correlation analysis revealed a significant relationship between dependency on ChatGPT and procrastination (r = 0.546, p < .001), indicating that a higher reliance on AI tools may contribute to increased procrastination. No statistically significant association was identified between ChatGPT dependency and the risk of plagiarism. Future research should prioritize the development of strategies that promote the effective use of AI while minimizing the risk of over-reliance. Such efforts can enhance academic integrity and support independent learning. Educators play a critical role in this process by guiding students to balance the advantages of AI with the cultivation of critical thinking skills and adherence to ethical academic practices.

Research fronts are dynamic, knowledge-driven clusters of scholarly activity that emerge in response to pressing problems and/or groundbreaking discoveries. Clarivate Analytics provided a valuable tool based on Citation Productivity and Trajectory (CPT) indicator, which successfully identified particularly hot research fronts on a global scale. To enhance the accuracy and comprehensiveness of identifying both active and emerging research trends, this study develops an extended Clarivate formulation incorporating a novel Impact Factor (IF) metric. The refined approach incorporates growth rates, publication productivity, and the average publication gap between published and citing publications. This method is applied to exploring key research fronts in the digital education ecosystem using bibliometric data from the Scopus database in the period of 2019-2023. The results reveal that artificial intelligence and online learning are the most prominent and influential fields, with virtual reality, blockchain, hybrid learning, and digital literacy representing fast-growing areas. By analyzing both quantitative and qualitative aspects, this work informs key stakeholders about the evolving priorities and trends in the digital educational landscape.