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.
This study aims to analyse the bibliometric evolution and thematic trends in scientific publications on digital transformation in higher education through a mapping review approach. The research addresses the increasing need to understand how digital technologies are reshaping institutional practices, governance, and teaching-learning processes. The methodology is based on a structured four-phase model that includes search, assessment, synthesis, and analysis, applied to a sample of 252 scientific articles retrieved from major academic databases. The findings reveal a marked increase in academic production since 2019, particularly in response to the challenges posed by the global health crisis. The co-occurrence analysis of keywords identifies five main thematic clusters related to distance education, digital competencies, technological innovation, public policy, and equity in access to education. The results suggest that while digital transformation is a global trend, it is highly contextual and multidimensional. This review offers a comprehensive overview of the conceptual frameworks, methodological approaches, and research gaps in the field. The implications of this study are relevant for researchers, university administrators, and policymakers aiming to align institutional strategies with the demands of a rapidly evolving digital society.
The research in mentoring begins in workplace settings as a strategic tool for human resource management and later expands to educational and other service contexts. Mentoring in educational institutions pertains to the supportive relationship between a more experienced faculty/peer mentor and a less experienced student mentee, offering guidance, support, and valuable insights to excel in their academic pursuits, personal growth, and future success. This review systematically identifies and synthesizes the best available evidence on mentoring in educational institutions following the PRISMA methodology, reviewing 77 empirical studies (2013-2023) published over the Scopus and Web of Science databases, and applies the combination of Antecedents-DecisionsOutcomes (ADO) and Theory-Context-Methodology (TCM) frameworks to organize the findings. Results reveal that mentoring practices in educational institutions can foster ‘quality education’, Sustainable Development Goal 4. This review enhances the understanding of academic mentoring practices, contributing to the existing literature and provides new insights to policymakers and practitioners.
The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and Ngrams. The findings reveal a pronounced emphasis on areas such as Blockchain and Security while underscoring the relative underrepresentation of topics like Incident Response and Vulnerability Assessment. These observations suggest potential avenues for future research. This methodical approach delineates the current state of cybersecurity research and highlights areas where further studies could significantly advance the field
Various studies have shown that character values in early childhood in kindergarten develop optimally when instilled early in a positive and consistent environment, influenced by local wisdom, community culture, family roles, and school educational practices. However, no research has specifically explored the development of character value constructs in early childhood in coastal areas. The specific research question of this study is related to the aim of exploring and testing a structural equation model of character values in early childhood in coastal kindergartens. It is hypothesized that character values related to relationships with God, self, others, and the environment contribute to the character development of early childhood in coastal areas. This study used a survey design. The sample size was 191 early childhood children aged 4-6 years from 10 kindergartens in the coastal area of Buton Island, Southeast Sulawesi Province, Indonesia. A four-point Likert-type questionnaire was used as the data collection instrument. The data were then analyzed using SEM. The analysis shows that the character-value construct model has a good fit across various indices and provides strong empirical support for the theoretical model of early childhood character. The findings reveal that all four dimensions are positively and significantly correlated in the structural equation of character values, although the strength of the relationship between each pair of dimensions varies. Practically, the results of this study emphasize the importance of integrating the four dimensions of character values into the curriculum and learning in coastal kindergartens. Educators need to create a learning environment that is positive, consistent, and aligned with local culture. These findings also form the basis for the formulation of contextual and inclusive early childhood character education policies.
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.
Numeracy is a form of Higher Order Thinking Skills (HOTS) that is currently given much attention in the world of education, especially mathematics education. The ability of prospective teachers in numeracy task design is important as a professional skill. However, some studies show that there are deviations in the perception of numeracy in prospective teachers. This study aims to explore the epistemological obstacles of numeracy task design construction experienced by prospective teachers based on their numeracy perception. This study used a qualitative approach with an explorative method. The subjects of this study were 6 prospective mathematics teachers. The research instruments used include task design forms, perception interview guidelines, and field notes. Based on the results of the study, it is known that prospective teachers with limited perception based on the verbal meaning of numeracy tend to experience obstacles in constraining the initial idea generation. Meanwhile, prospective teachers with a broad perception of numeracy meaning tend to experience obstacles in deviating from their final verification of mathematical content in numeracy tasks. By knowing the obstacles of prospective teachers in the numeracy task design, results of this study contribute to mathematics teacher education development.
skills are needed as an essential competency for prospective teachers in the 21st century, enabling them to face diverse and complex educational challenges. This study applies a quasi-experimental design approach using a single-group pretest and post-test to explore the model profile of problem-solving skills development of prospective chemistry teachers through Case-Based Learning (CBL). 30 prospective chemistry teachers built their problem-solving skills by engaging in case analysis, collaborative discussions, and solution development through CBL, which integrated real-world scenarios into the learning process. The data were gathered using test instruments and observation sheets. Paired t-test analysis was performed at a probability level of p <= 0.05 significantly. Data analysis utilizing the SPSS statistical program. Research results show that chemistry problem-solving skills through CBL explored and reviewed from prospective chemistry teachers' activity are excellent, prospective chemistry teachers' problem-solving skills of prospective chemistry teachers (high and very high categories), N-gain of chemistry problem solving skills (high category), and significant influence of CBL implementation. It can be concluded that using CBL models effectively builds prospective chemistry teachers' problem-solving skills. This research has implications for teacher educators guiding practical ways to enhance problem-solving skills.
This research study seeks to address a lack of quantitative research on the effectiveness or performance of information technology governance in existing literature and advances IT governance theory by offering empirical validation of its critical role in fostering operational efficiency and competitive edge. The investigation examines how successful IT governance influences portfolio control, risk mitigation, and alignment strategy between business and information technology. Analysis of survey responses from 282 organizations in Palestine was done. Bridging a gap in quantitative studies, the research combines Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to assess two governance dimensions: perceived importance and implementation success. The data reveals statistically significant links between governance efficacy and enhanced organizational decision-making, underscoring the value of formalized governance frameworks. ANN results further identify governance importance as the strongest predictor of IT outcomes. These insights propose that businesses can unlock greater strategic value from IT investments by refining governance models.
Reading is a fundamental ability that most students must acquire in academic settings; however, many have significant challenges in comprehending the entire book. Students frequently employed notetaking and the use of highlighters to enhance their reading proficiency. The learners' capabilities have been substantially impacted by the COVID-19 pandemic, which has hindered educational institutions from conducting in-person lessons. One method to tackle this issue is to proficiently provide suitable educational resources for online learning environments. This study seeks to examine the correlation between specific eye movement metrics and identify the variables that could enhance the reading ability of students enrolled in an online course. Twenty university students were enlisted as participants; a pre-test and post-test were administered to evaluate their reading proficiency objectively. The combination of pictures and colored text significantly affects reading performance. Surprisingly, the learning materials with pictures, text with colour, and audio support have less effect on reading performance since the text contains audio support. Therefore, combining colored keywords and photographs might be a consideration for producing proper online learning material.
This study explores the digital integration and intelligent management of cultural heritage in Bulgaria through a case study of a tourist site-the Tsarevgrad Tarnov Multimedia Visitor Center, managed by the Regional History Museum in Veliko Tarnovo. The focus is placed on the development of an evaluation model for assessing the level of digitalization, utilizing a matrix of key components, expert assessments, and the application of a weighted scoring system. The analysis also incorporates findings from unstructured interviews that provide insights into the current implementation status of digitalization activities. The proposed methodology is adaptable to other heritage sites and destinations, serving purposes of strategic planning and sustainable management. The research highlights the need for an interdisciplinary approach, effective practices, and the ethical use of technologies such as AI and the metaverse to enhance both accessibility and the preservation of cultural heritage.
Student practicum reports are an important aspect in assessing their ability to apply theoretical knowledge into practice. This study aims to find a format for a practical report that can measure 21st-century competencies as an evaluation instrument for student practicum reports. This study uses a quantitative method with a purposive sampling technique, involving 90 students who have compiled practicum reports using Google Autocrat. Data were collected through a Likert scale-based assessment rubric and analysed using the Rasch model to evaluate reliability, validity, and item difficulty levels and fit order in the assessment instrument. This study shows that practicum reports using the Google Autocrat application have the potential to increase the efficiency and objectivity of student practicum report evaluations. The study contributes to the development of an academic evaluation system in the 21st century by using more accurate and reliable technology-based practicum reports. In addition, the research obtained an innovative product in the form of aspects assessed in 21st-century practicum reports. In addition, this study produced an innovative assessment framework for 21st-century practicum reports, encompassing several key aspects that reflect report quality, originality, timeliness, and practical relevance for educators. The use of Google Autocrat can reduce manual errors in report evaluation. This report format can be used in the preparation of practical reports in various practical classes. Further studies can explore the integration of other evaluation technologies, such as Artificial Intelligence (AI), for automatic report analysis.
The paper investigates the adoption of digital tools and the implementation of change management practices in the semiconductor equipment installation process. Drawing from a case study at Company A, the research analyzes challenges associated with poor data quality and user resistance encountered during the implementation of a digital checklist system. To address these issues, an eight-item data quality checklist was designed and integrated with the ADKAR change model (comprising Awareness, Desire, Knowledge, Ability, Reinforcement). The checklist data were analyzed using principal component analysis (PCA) to identify usage patterns and to cluster users based on their behaviors. Utilizing these insights, customized change management strategies were proposed for each user group. The study highlights that aligning data-driven insights with structured change management methodologies can significantly enhance the adoption of digital tools and improve operational data quality within semiconductor manufacturing environments.
Assessing students' technological creativity remains a challenge due to the absence of standardized, product-based tools that capture both characteristic indicators , creativity constituents. Building on literature that emphasizes the need for authentic, performance-based assessment, the study developed and validated the Technological Creativity Assessment Tool (TCAT) through a developmental research design guided by the revised ADDIE framework. It was hypothesized that TCAT would demonstrate strong validity and reliability as an assessment instrument. Implementation testing involved 260 purposively selected experts, teachers , students of science and technology-based courses, who evaluated five student-developed projects. Six AI programs also assessed TCAT and its validation instrument. Reliability analysis using Cronbach's Alpha produced a coefficient of 0.92, confirming internal consistency. ANOVA tests revealed no significant differences in respondent assessments across five characteristic indicators and three technological constituents, with Bonferroni Post Hoc tests supporting these findings. Automated assessments likewise showed high reliability. Results affirm TCAT's validity and reliability as a product-based tool for assessing technological creativity.Its applications extend to curriculum design , institutional evaluation, offering a scalable model for integrating authentic assessment practices in higher education.
The secure use of electronic banking is critically important for its operation. This paper examines authentication methods and outlines prevalent cyber threats potentially affecting the security of electronic banking. The main goal of the paper is to investigate user preferences in the domain of electronic banking security and the importance of electronic banking security literacy from a user-centric perspective. The hypothesis, that user preferences in electronic banking security may seriously affect the level of safety of its provision and usage, is examined. The conducted survey of 874 Slovak electronic banking users investigates their preferences in the area of e-banking security using descriptive statistics. Empirical findings from the survey uncover deficiencies in user security literacy and behavior. The results highlight insufficient attention regarding safe usage rules and robust password practices. This research underscores the persistent vulnerability created by the human element and emphasizes the urgent need for improved, targeted bank-led user education campaigns. Furthermore, it calls for the continuous implementation of user-friendly, resilient security architectures that can both mitigate permanently evolving digital risks and effectively encourage sustained trust in electronic banking services.
The aim of this article is to verify the effectiveness of influencer marketing by local producers in relation to different generations of consumers. The research was conducted in March 2025 on a sample of 522 respondents. The basic research method was a CAWI questionnaire survey. Five hypotheses were set. These hypotheses concerned the importance of influencer marketing in the communication of local producers; i.e., the impact that influencers have on the purchasing decisions of different generations of consumers, and the relationship between the age of respondents and their preferences regarding the types of local products promoted. Hypotheses 1-4 were statistically tested using the single proportion testing method. Hypothesis 5 was verified using twodimensional descriptive statistics. A review of the literature shows that influencer marketing has evolved from a cost-effective, global communication tool based on trust and authenticity to a complex strategy. Its effectiveness today hinges on selecting influencers whose values align with those of the brand and who have a strong presence in specific regional and market contexts. However, a comprehensive approach that identifies how each generation engages with influencer marketing, and the impact of this communication activity on their purchasing decisions, is lacking. Similarly, an approach that identifies sub-local markets characterised by an increased sensitivity to influencer marketing is also lacking. For this reason, research questions have been derived, the results of which are presented in this paper. The research questions focus on whether influencer marketing is a relevant communication activity for local producers, the impact of influencer marketing on the purchasing decisions of different generations of customers, and which product categories promoted by local producers are characterised by a higher level of sensitivity in purchase decision-making within each generation compared to others. The results show that influencer marketing is a relevant communication activity for local producers, as more than half of respondents follow influencers who promote products from local producers. Generation X respondents are less affected by influencers who promote products from local producers than Generations Y and Z. There is a statistically significant relationship between the age of respondents and their preferred promoted local products: Generation Z specifically prefers cosmetics. Generations X and Y prefer food and clothing.
