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Unlike conventional linear predictive approaches, HALF addresses complex, heterogeneous data and scalability limits by integrating ensemble learning such as bagging and boosting with an adaptive fusion strategy. Using an Open University VLE-derived database, the study reports 87.2% accuracy, 85.4% precision, and 89.1% recall, outperforming traditional methods.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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37:25745–25765  \n[https://doi.org/10.1007/s00521-025-1](https://doi.org/10.1007/s00521-025-1)1018-7  \nIntroducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior predictive analytics in E-learning  \nUmar Islam 1 • Ibrahim Khalil Alali2 • Shoayee Dlaim Alotaibi3 • Zaid Alzaid4 • Babar Shah5 • Ijaz Ali1 • Fernando Moreira6   \nReceived: 17 June 2024 /Accepted: 10 January 2025/Published online: 13 March 2025  \n􀀂 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025  \nAbstract  \nToday’s complex world is defined by digital changes in educational paradigms to which E-learning has contributed significantly, and as such, accurate prediction methods are needed for student performance modeling. In this paper a new and complex model is proposed, namely the Hyperdynamic Adaptive Learning Fusion (HALF) model that leverages adaptive computing paradigms and artificial intelligence to build a fusion of learning that adapts to the new learning patterns. Many conventional predictive models employ linear and simplistic relationships to predict an outcome from an input; hence, they fail to decompose complex and heterogenic data patterns of educational data and also suffer from scalability issues for dealing with large volumes of data. To overcome these issues, the HALF model employs the relevant ensemble learning algorithms that consist of bagging, boosting, and an innovative adaptive fusion strategy that integrates base and adaptive models to achieve higher accuracy and resilience in the latter. In doing so, and by adopting the scientific method of working on trials and errors and rigorous assessment employing a database derived from the Open University VLE, the investigation presented in this paper provides compelling evidence of the HALF model’s superior efficacy, which yields an accuracy of 87% . 2%, precision of 85 . 4% It has been proved that 3% of all students have significant learning disabilities, while the recall value is 89 . 1%, surpassing traditional methods. The model’s equation can be easily applied to any variety of courses and of students, which makes it highly beneficial to educators and administrators; at the same time, the model is highly interpretable. Therefore, HALF model proves to be a revolutionary addition to the current kind of statistical modeling in E-learning that depicts student engagement pattern into more precise and accurate form, reduce biases in all way possible and provides solution that might help to improve the course outcome. The next steps will consist in optimizing the architectural properties of the model as well as the model’s capacity to be scaled, and more generally in learning about the model’s possibilities and limitations across different learning environments.  \nKeywords Accurate prediction methods 􀀂 Artificial intelligence 􀀂 Virtual Learning Environment 􀀂 Predicting student performance  \n1 Introduction  \nAs a result of advancement in technology, E-learning has evolved from a concept to being a revolution tool in changing the scope of educational systems and crossing geographical barriers [1]. To Essay: As curriculum delivery through technology improves, with educational institutions progressively turning to online means, it is crucial to monitor and predict the students’ performance [2, 3] . In E-learning, predictive modeling is helpful in forecasting  \nExtended author information available on the last page of the article  \nthe performance of the students to guide the action of the educators to raise the education performance [5] . Although there are vast progresses in traditional predictive models, they are not without limitations: several inherent concerns rising from the complexity and heterogeneity of educational datasets are hard to identify, measure, and understand; complex and comprehensive endogenous relationships are difficult to learn; and huge amount of data possessed by nowadays educationa","cbCaib4Y0nPylmC2","https://ap.wps.com/l/cbCaib4Y0nPylmC2","pdf",2083587,21,"English","# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"What problem does the HALF model target in e-learning?\",\"answer\":\"It targets the need for accurate prediction methods for student performance while addressing complex, heterogeneous data patterns and limitations of conventional linear models.\"},{\"question\":\"How does HALF improve predictive performance compared with traditional approaches?\",\"answer\":\"HALF uses ensemble learning (bagging and boosting) and an adaptive fusion strategy that integrates base and adaptive models, improving accuracy and resilience while handling data dynamism.\"},{\"question\":\"What dataset and results are reported for evaluating HALF?\",\"answer\":\"The model is assessed using a database derived from the Open University VLE, with reported metrics including 87.2% accuracy, 85.4% precision, and 89.1% recall.\"}]","Introducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior predictive analytics in E-learning | PDF",1789941164,53]