Course correction

Analytics has become an essential tool in our search for long-lasting and forward- looking solutions in a world where information is constantly shared, and data is being made at a rate that has never been seen before. Welcome to the ”Sustain- able and Predictive Analytics Model,” an in-depth look at how sustainability and predictive analytics work together. This writing gives people a complete plan for using data-driven ideas to help society and business. Analysis methods for the long term and their ability to predict The book starts by looking at the basic concepts behind sustainable and predictive analytics. This creative work details why and how these two fields must work together to solve the world’s most pressing problems. In this study’s second part, we discuss the analyti- cal ideas and tools we used. The fundamental analytics ideas are looked at in depth to start with a solid base. In the next part, you’ll learn basic facts to help you analyse and understand data more effectively. In the third part, the basic ideas of sustainability are looked at. The main goal of this effort is to bring about sustainable growth. Through a thorough look at sustainability ideas, you will learn what you need to know to make sound economic, environmen- tal, and societal decisions. Part 4 of ”Approaches to Predictive Modelling For the book to make accurate predictions, it must look closely at historical facts. This part will overview the different predictive modelling methods and explain their advantages and disadvantages. Collecting information for a full look at long-term sustainability For statistics to work well, data must be used. This chapter gets into the book’s main topic, which is how to collect and prepare data for a study on sustainability. Building a model that can predict sustain- ability By using the information and tools in this book, we can start the process of building predictive sustainability frameworks, which will help us make well- informed choices about the future. Case studies on how to use statistics that are good for the environment Using real-world examples and case studies help show how important sustainable statistics are in many fields. Explores from a social and moral point of view. In the information age we live in, ethics are becoming increasingly important. The writers of this book look into the ethical and social aspects of sustainable and predictive analytics to ensure they align with responsible practices. We are checking and analysing how accurate and useful the model is. To ensure predictions are accurate throughout the process, you must know much about model validation and performance review. In this part, we’ll talk about how to set up and connect a certain system or piece of technology. The most important thing is that analytics work well with existing systems. This chapter looks at the different ways release and integration can be done. Chapter 11: What the future holds for sustainable and predictive analytics Analytics is a field that is constantly growing and improving. The book looks into the future by discussing upcoming technological changes for sustainable and predictive analytics. Final Thoughts and an Immediate Call for Change A call to action demonstrates the book’s turning point. This article ex- amines the many effects of sustainable and predictive analytics and encourages readers to use this changing method for a better future. This academic study digs deep into the ”Sustainable and Predictive Analytics Model” literature and looks at the complicated link between data, sustainability, and prediction. The joint projects described in the book hold hope for future generations because they show new ways to do things and help make the world more sustainable.

The rapid expansion of the group of students with financial difficulties from their families in higher education schools and the increasing financial support from the state and schools year by year, how to ensure that these two aspects can be matched reasonably and effectively, is a high standard and high accuracy requirement for the identification of students with financial difficulties from their families in higher education schools every academic year. The aim of this paper is to build a cloud-based platform to study an accurate financial aid system for university students. A comparison between Docker and OpenStack is made, and the actual physical equipment situation of this paper is combined with the choice of building a suitable private cloud i.e. building a private cloud with Docker container technology. Then, the modules of the university student financial aid system were developed according to the design plan. Finally, after the development of the university student financial aid system was completed, the performance of the platform was tested in detail and comprehensively. The test results show that the performance of the system has met the design of the project and can meet the basic needs of the users.

Objectives: This review examines global research published between 2020 - 2024 on integrating artificial intelligence (AI) into Learning Management Systems (LMS) in higher education. It identifies dominant platforms and applications, evaluates methodological practices, and highlights unresolved challenges. Methods: Following the PRISMA guidelines, 44 peer-reviewed articles were retrieved from Scopus, Web of Science, and IEEE Xplore. Each study was coded according to platform, AI application, dataset, algorithm, feature selection, interpretability approach, and outcomes. Bibliometric mapping was performed using VOSviewer. Findings: Research activity increased sharply during the pandemic (2020-2022) and stabilised after 2023. Most contributions came from Saudi Arabia, India, and China, while Africa and Latin America were rarely represented. Moodle accounted for about one-quarter of the reviewed studies. Predictive analytics was the most frequent application, with models such as kNN, Random Forest (RF), and PCA showing strong performance. However, few studies employed explainable AI (XAI) methods such as SHAP and LIME appeared in less than 5% of studies and dataset dependence, particularly on OULAD, limited cross-institutional replication. Novelty: Unlike earlier descriptive reviews, this study provides the first cross-platform quantitative synthesis of AI-enabled LMS research. It benchmarks predictive accuracy across algorithms, identifies methodological bottlenecks, and highlights neglected areas, including dataset diversity, XAI, inclusivity, and pedagogical validation. Keywords: Artificial Intelligence, Learning Management Systems, Higher Education, Predictive Analytics, Explainable AI

With the creation of the first federal student loans as part of the National Defense Education Act of 1958, the US postsecondary financial aid system was set on a path from which it has not fundamentally deviated in the intervening decades. While college financing has trended almost inexorably toward greater reliance on student borrowing as costs have outpaced families’ incomes, the major components of the financing “mix” have remained unchanged. Financial aid policy is sometimes tweaked around the edges to lighten the burden of student debt, give colleges a competitive edge, or address undesirable disincentives. For the most part, however, these reforms bear more resemblance to the classic “shell game” than to authentic innovations. What American students need are more powerful tools with which to approach their futures—tools that help them prepare for higher education, persist to completion, and then leverage returns on their degrees. What they get, however, are repackaged versions of the same blunt instruments. While everyone wants improved outcomes from our financial aid investments, the nation’s apparent inability or unwillingness to innovate truly novel approaches to paying for higher education stands in the way of progress. The goal of financial aid policy has been narrowly framed as only helping young adults pay for college, a low bar that completely ignores the role financial aid could play in influencing early education, postsecondary completion, and post-college financial health. As a result, instead of receiving support at critical junctures along the opportunity pipeline to a prosperous adulthood, students are largely left to their own devices except at the moment when the tuition bill becomes due. To capitalize on the resulting missed opportunities, the United States needs more than different loan repayment schedules or loosened rules on grant disbursement. What we need is a fundamental shift in how we think about financing higher education and what we believe about why it matters.

Fiscal Targets. A Guide to Forecasters?

Fashion programs includes specified designing and making courses, such as fashion sketching and draping, other courses train the students on some skills which are related to fashion indirectly, such as fabrics dyeing, the model of the current study. Through observing, investigating, and within the experience of online teaching Fabric Dyeing at University of Nizwa 2020 Fall semester and 2021 Fall semester in pandemic time, the researcher found that the objectives concentrated on teaching definitions and procedures of dyeing, theoretically and practically. The objective of using dyed fabrics in the field of designing fashion is not included in the course syllabus, adding this objective (Dyed Fashion) unleashes and liberates creativity thinking and open the gate of producing creative fashion. Online learning which enhances critical thinking through sharing students the applications within LMS - learning management system, Moodle in the current study. Descriptive approach is followed, through three scales, measuring: Students' satisfaction of the current course, opinion scale for proposed dyed fashion designs and opinion scale for students' attitude toward the proposed lesson. The results indicate the actual need for developing the course and adding the proposed lesson (Dyed Fashion), the proposed lesson enhances the mental abilities of student and rising design sense, the proposed lesson is high potential for application through online teaching/learning

Global trade systems are becoming increasingly complex due to interconnected markets, fluctuating demand, geopolitical uncertainties, and sustainability concerns. Predictive analytics offers transformative potential in optimizing supply chain operations by leveraging data-driven insights for proactive decision-making. This explores the advancements in predictive analytics models tailored for global trade systems, emphasizing their role in enhancing supply chain efficiency, resilience, and agility. By integrating machine learning algorithms, big data analytics, and real-time data feeds, these models enable accurate forecasting of demand, inventory levels, and transportation routes. Key innovations include the incorporation of artificial intelligence (AI) for pattern recognition, predictive maintenance of assets, and dynamic route optimization. The use of ensemble modeling and deep learning enhances the predictive accuracy, while adaptive algorithms accommodate evolving market trends and disruptions. The review also highlights the challenges of data silos, scalability, and model interpretability, which hinder the full potential of predictive analytics in global supply chains. Ethical considerations such as data privacy and fairness in AI-driven decision-making are addressed to ensure responsible model implementation. Case studies from various industries demonstrate how advanced predictive models mitigate risks, reduce costs, and improve sustainability by optimizing resource utilization and reducing carbon footprints. Emerging trends, such as the integration of blockchain for transparent data sharing and IoT for real-time monitoring, further enrich the predictive capabilities. This underscores the critical need for collaborative frameworks between stakeholders, continuous innovation, and investment in analytical capabilities to future-proof supply chains. By advancing predictive analytics, global trade systems can achieve a new paradigm of efficiency and resilience in a volatile and competitive landscape. Keywords: Predictive Analytics, Models, Supply Chain Optimization, Global Trade Systems.

Predictive analytics looks forward trying to divine unknown future trials or actions based on data mining, statistics, modeling, deep learning and artificial intelligence, and machine learning. Business Intelligence, its forerunner in analytics, is a look backward. Predictive models are useful to business activities to well understand the customers, with the goal of forecasting buying patterns, potential risks, and its possible prospects. Healthcare industry organizes predictive analytics in different ways to improve operations and minimize risk. This article will explain the understanding of predictive analytics and predictive modeling, how the healthcare industry adopted predictive analytics and modeling and the importance of data mining in healthcare.

Families who earn more or who heed the “experts' ” advice and save for college typically receive less financial aid. The financial aid system therefore implicitly taxes both income and assets. We use NPSAS aid award data to estimate these implicit taxes, and find that at average-priced colleges the marginal income tax ranges from 2 to 16 % and the marginal asset levy from 8 to 26%. A typical family loses aid worth about $11,000 at average-priced colleges and $15,000 at expensive colleges by accumulating an extra $50,000 in assets.

This study examines the enrollment decisions of freshmen applicants to highly selective private colleges relative to highly selective public colleges. The empirical analysis shows that college enrollment varies by family income—in particular, low‐income students with demonstrated financial need are less likely to enroll at private colleges, controlling for other factors including race and financial aid. Grant aid tends to have a moderating effect but the amount of aid under the existing college pricing and financial aid system appears to be inadequate. This finding provides support for recent revamping of financial aid offerings to low‐income students by some highly selective private colleges. In addition, the results suggest that while family income appears to be a strong determinant of enrollment at these colleges, race does not seem to play a role. (JEL I21)

The Genetics module based on a learning management system (LMS) is a standalone website with an instructional design for self-study about genetic literacy. This module is expected to be useful for medical students of Universitas Riau (FK UNRI) from various class years to study at any time. The purpose of this study was to find comparative results of self-study of genetic modules based on LMS among UNRIs medical students in various class years and to determine effectiveness of genetics module based on learning management system among medical students of Universitas Riau. The research method was quantitative. The subjects were 34 UNRIs medical students who were taken proportionally from three class years. The research data was obtained using the UNC-GKS questionnaire. The results showed that the average pre-test scores for the first, second, and third years were 58, 58, and 63. The statistical test results for  comparing the pre-test scores of these three class years gave no significant difference. The results of the average post-test scores for the first, second and third years were 58, 68, and 68. The results of the statistical test comparing the post-test scores of these three class years gave no significant difference also. The results of the Wilcoxon test was p

Health, Safety, and Environment (HSE) predictive analytics embraces new functionalities and advances the field of offshore analytics. This integration advances analytics offshore, where the external environment and industry risk come together. The role of HSE analytics offshore has been reactive in nature and predictive for incident analytics. This coming together of HSE analytics with real-time data streams and predictive analytics to forecast incidents that can occur is the current status of HSE analytics offshore. The use of real-time data streams with predictive analytics eases the pace at which reactive analytics are done, while providing the outcomes in real-time, allowing for forecasted incidents to be prepared for. The focus of analytics offshore is primarily analytics and integrating with the data streams, which dives into offshore environment monitoring systems. The input in risk models forecasted real-time data into the analytics. The real-time data provides inputs on infrastructural failures, machinery, and operational conditions. An instance of predictive analytics can be used in systems where abnormal vibrations occur that can lead to blowouts. These predictive models lessen the time from analytics to operationalization. Hence, enabling real-time decision support systems, these systems aid in shifts and compliance with emergency preparedness and international safety standards. The more general implication of predictive analytics is its role in enhancing the efficiency and sustainability of offshore operations. By reducing the expenses of unplanned downtime and expensive accidents, predictive safety frameworks economize operational costs and also strengthen environmental protection through early containment of pollution incidents. in addition, these frameworks help meet the legally-bound and self-imposed zero-harm goals, and also the DOE and DHS resilience goals offshore of critical infrastructure. Predictive safety analytics frameworks are crucial in maintaining the safety and protection of people and the marine environment, and equally critical in maintaining the robust, operational stability of offshore industries in the face of unpredictable risks.

In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event logs of historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, these techniques are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study further motivate the need to incorporate interpretability in predictive process analytics.

Time series decomposition and predictive analytics using MapReduce framework

The project, entitled “Comprehensive Health Forecasting and Care Solutions Using Predictive Analytics and AI Techniques” aims to change healthcare for the better. By using advanced artificial intelligence, it hopes to predict and manage patient health outcomes. The main goal here is to build a strong predictive model. This model will use machine learning techniques to foresee various health events. This way, we can promote proactive healthcare interventions. A key part of this project is managing patient data ethically. Ensuring privacy and sticking to legal standards is essential. The methodology involves a careful system for data collection and preprocessing. It gathers a mix of clinical and demographic datasets. Using ensemble learning models along with smart feature selection methods boosts prediction accuracy and reliability. Throughout development, the project emphasizes transparency alongside model interpretability. These aspects matter for healthcare professionals who will use these tools. The potential impact here is huge! It offers a path toward better patient outcomes and smarter allocation of healthcare resources. By giving clinicians clear and understandable predictive insights, this project aids in making informed decisions and tailoring personalized patient care. Moreover, it helps in grasping health patterns and risk factors better, encouraging innovation and research in predictive healthcare. As the project wraps up, its findings are set to greatly impact healthcare practices potentially leading to cost savings and improved patient well-being. Integrating this predictive model into clinical settings could change how healthcare providers identify and respond to health risks. This represents an important step forward in the field of healthcare analytics. In short, this project is a critical milestone in improving analytics for patients, healthcare providers, and the wider medical community alike.