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The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Since new digital micro-credential technologies emerged a decade ago, there has been a rapid rise in micro-credentials in the education landscape. Much has been promised about these educational technologies, yet there is much confusion by key stakeholders in the digital micro-credential ecosystem. This confusion has led to significant efforts globally to define micro-credentials to ensure quality learning and generate beneficial impacts to learners, employers, education providers and edtech organisations.

This commentary reviews relevant literature on digital micro-credentials and other alternative credentials to determine how these educational technologies can meet the demands of the Fourth Industrial Revolution to nurture lifelong learning for working learners.

Universities are being challenged to address the changing needs and uncertainty being introduced by the Fourth and Fifth Industrial Revolutions, particularly with implications for workforce upskilling and lifelong learning. To adapt, universities will need to rethink their roles and shift their institutional mindsets in how they may approach the challenges through mechanisms such as digital micro-credntials.

This paper focuses on the analysis of five policy statements about micro-credentials. While these policy statements represent a sample, there is a representation of Western education-related systems. Thus, they skew the findings towards Western education systems thinking.

Understanding how micro-credentials are being positioned within education-related systems is useful for applying the educational technologies by, for example, universities, learners and employers.

Provides an overview of how these educational technologies may provide beneficial impacts for society as it plans to adapt to economic uncertainty and change.

The commentary provides a policy context for the emerging use of micro-credential technologies to examine demands for workforce upskilling.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The Child Friendly Cities Initiative (CFCI) is a UNICEF-led collective impact intervention aimed at promoting children’s rights at the city and community levels. The CFCI operationalizes the UN Convention on the Rights of the Child (CRC) for local governments through a framework for action aimed at realizing the rights of young people under 18 years of age: (1) to be valued, respected and treated fairly; (2) to be heard; (3) to access social services; (4) to be safe; and (5) to participate in family, life, play and leisure. This chapter provides an historical analysis of the CFCI globally and in the United States, and how this intervention draws upon and advances sociological research on young people’s meaningful participation. We present three case studies to analyze young people’s participation in CFCIs and the lessons learned from Houston, Texas, Minneapolis, Minnesota, and San Francisco, California.

This study examined Indian institutional investors' holding data to understand their investment strategy (Portfolio Concentration/Diversification) and explored whether their skills were associated with their portfolio strategy and performance. The study introduced a new proxy to identify skilled investors by forecasting abnormal returns. Moreover, the study also highlighted where skilled Indian investors put their money for long-term investment.

This study measures portfolio concentration based on the number of holdings, the Hirschman–Herfindahl index (HHI) and benchmarks adjusted industry concentration. The study introduced a new proxy to identify skilled investors. We measured Investors' performance with the help of Carhart's four factors model and examined the relationship between variables through various regression models.

The study concluded a negative relationship between portfolio concentration and performance. However, skilled Indian investors get rewards from portfolio concentration decisions. It was found that skilled investors with few stocks and an industry concentration in their portfolio show a positive association between concentration and fund performance. Additionally, this study found Indian investors showing their faith in the financial sector for long-term investment.

This study examined Indian institutional investors' portfolio concentration strategy and introduced a new proxy to measure investors' skills.

The purpose of this study is to verify whether the relationships between supply chain members can maximize supply chain efficiency by adopting an investment model that has been used in family psychology.

Information sharing was added as a link between commitment level and the independent variables of the investment model, and their effect on logistics performance in terms of supply chain operation was examined. The authors surveyed workers involved in supply chain-related work in Korea and collected 300 valid survey responses to verify the findings. The hypotheses were verified through structural equation model using SPSS 18.0 and AMOS 18.0.

This study academically revealed the impacts of intangible efficiency on performance. Satisfaction and investment size had a significant effect on information sharing, but not on the quality of alternatives. Information sharing had a positive effect on the commitment level. Finally, commitment level had a positive effect on logistics performance. Because the effects of satisfaction and investment size are proportional to the degree of information sharing, firms in a supply chain must consider their importance.

Owing to the complexity of today’s supply chains, the existing fragmentary method of analysis limits the evaluation of supply chain performance factors. Accordingly, based on an investment model that is rarely discussed in business administration, this study identified the link of antecedent variables in addition to direct variables that affect supply chain performance.

The lack of physical contact and the absence of nonverbal clues could make some learners uncomfortable interacting with others via online learning platforms. Hence, understanding the determinants of students' motivation and engagement in online learning platforms is crucial in harnessing digital technology as an enabler of unrestricted and quality learning experiences.

Drawing on the self-determination theory (SDT), this study investigates the factors associated with student’s motivation to learn (MOL) and their influence on online learning engagement (OLE). Data were collected from 228 university students from the Klang Valley region of Malaysia using the online survey method.

The results of data analysis using the partial least squares structural equation modeling indicate that self-directed learning, computer and Internet self-efficacy and online communication self-efficacy significantly influence MOL. Besides, these factors indirectly influence OLE through MOL.

This study adds to the SDT framework by demonstrating how students' perceptions of autonomy, competence and relatedness through online interaction relate to MOL and OLE.

Agile principles have been widely used in software development team practice since the creation of the Agile Manifesto. Studies have examined variables related to agile principles without systematically considering the relationships among key team, agile methodology, and process variables underlying the agile principles and how these variables jointly influence the achievement of software development agility. In this study, the authors tested a team/methodology–process–agility model that links team variables (team autonomy and team competence) and methodological variable (iterative development) to process variables (communication and collaborative decision-making), which are in turn linked to software development agility (ability to sense, respond and learn).

Survey data from one hundred and sixty software development professionals were analyzed using structural equation modeling methods.

The results support the team/methodology–process–agility model. Process variables (communication and collaborative decision-making) mediated the effects of team (autonomy and competence) and methodological (iterative development) variables on software development agility. In addition, team, methodology and process variables had different effects on the three dimensions of software development agility.

The results contribute to the literature on organizational IT management by establishing a team/methodology–process–agility model that can serve as a basis for developing a core theoretical foundation underlying agile principles and practices. The results also have practical implications for organizations in understanding and managing holistically the different roles that agile methodological, team and process factors play in achieving software development agility.

This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.

Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.

Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.

First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.

The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.

This study presents novel methodologies and important findings for advancing research and practice in MOOCs.