This paper presents an integrative literature review of agile practices across development phases, their impact on software quality, and tools supporting quality assessment and improvement. Previous studies focus on scientific literature and lack systematic quality measurement approaches. The study addresses two research questions: How agile practices impact quality attributes in each development phase and which tools are used for measuring and analyzing software quality. The study assumes that agile practices have a positive effect on key quality attributes and that dedicated tools support their assessment. An integrative literature review of 2,641 studies, including grey literature, resulted in 22 selected studies. The results identify sixteen agile practices and seven tools, providing practical guidance for improving software development processes and software quality.
In recent years, both academia and industry have identified solid waste management (SWM) as a relatively new area of study. This paradigm shift has precipitated the emergence of advanced Machine Learning (ML) and Deep Learning (DL) applications that prioritize cost reduction, expedited processes, and automation, thereby minimizing human intervention. The objective of this study is to conduct a systematic review of the literature to identify the architectures and metrics used for the efficient classification of solid waste (SW). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were utilized for the dissemination of the results. Consequently, a total of 41 articles were identified as relevant sources. The investigation revealed that the most prevalent Convolutional Neural Network (CNN) is VGG-16, which attains a classification accuracy of 96.1% within 100 epochs. A conspicuous absence in the extant literature is the application of R-CNN. The efficacy of this algorithm in the classification of solid waste is notable, as it facilitates the simultaneous categorization of multiple waste types.
The present study on systematic literature review aimed to identify the studies on secondary school teachers training in developing 21stcentury skills. Teaching skills in the recent decades underwent multidimensional changes and teacher training pertaining these changes are less visible in academia. Thus, present study aimed to review the studies on teacher training for 21st-century skills between 2007 and 2024. The study reviewed articles from Scopus, Eric, and EBSCO databases utilising PRISMA framework and analysed the same using JBI appraisal tool. The findings revealed from the finally obtained 31 articles that, teachers were trained on problem-solving, critical thinking, creativity, collaboration, information and technology integration skills using various strategies. The reviews highlighted the importance of developing a training to support skills of 21st-century based on constructivist and experiential theories. Further studies may focus on enhancing such training to a larger scale and to hold quality training hours.
Cyberbullying is a growing problem in digital environments that affects students’ mental health. Anonymity, the broad reach of these platforms, and the lack of regulation worsen the situation. This research proposes that the use of machine learning (ML) can improve the detection of cases by aiming to increase the number of identified cases, reduce detection time, and enhance the accuracy rate. An applied and experimental methodology was implemented, comparing a control group (manual detection) with an experimental group (ML-based system). The results showed significant improvements across all indicators: The number of detected cases increased by 42.12%, detection time was reduced by over 99.9%, and the accuracy rate improved from 85.3% to 98.8%. These findings validate that the use of ML enhances the detection of cyberbullying cases, offering a scalable solution for educational institutions to transition from reactive to preventive strategies, thereby fostering safer digital ecosystems for students.
The objective of the study is to identify the strategies and resources provided by digital transformation for the development of Personalized Learning in higher education. An integrative review was conducted using the methodological approach of Whittemore and Knafl, guided by the PRISMA flow diagram. Literature reviews and studies using quantitative, qualitative, and mixed-methods methodologies stood out. Scopus was the most frequently used database. It is concluded that personalized learning has benefited significantly from digital advances. Technologies such as cloud computing, artificial intelligence (AI), the Internet of Things (IoT), and big data stand out in this transformation.
