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326 articles for “Learning Analytics”
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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Student Skill Tracker: A Telemetry-Driven Programming Education Platform
Abstract: The rapid evolution of the software industry has significantly transformed the expectations placed on modern developers. In today’s competitive environment, employers increasingly value practical problem-solving ability, debugging proficiency, logical reasoning, and architectural thinking more than traditional academic grades alone. While academic performance still plays a role, it often fails to reflect how effectively a student can approach real- world programming challenges. Most current educational evaluation systems rely on exam scores …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 3, 2026 Read article
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Literature Review on Real-Time Dashboard Systems in Healthcare and Education
Abstract: Real-time dashboards are revolutionizing data analysis and visualization within education and healthcare, highlighting the realms of online learning analytics and emergency medicine. Such systems maximize decision-making power, efficiency of workflows, and awareness in real time. The literature review below explores diverse methodologies, benefits, and pitfalls, and presents suggestions for potential avenues for research in the future. Growing access to data and advancements in visualization and analytics technologies have given rise …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 · pp. 01–05 Read article
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Automated Intelligence, Machine Learning, and Big Data in Education: A Practical Framework, Synthetic Demonstration, and Deployment Guidance
Abstract: Artificial intelligence (AI), machine learning (ML), and big-data methods are increasingly used to improve educational decision making through personalization, early-warning systems, scalable feedback, and operational analytics. This manuscript proposes a practical end-to-end framework for educational AI/ML projects, covering problem definition, data engineering, modeling, evaluation, intervention design, and responsible governance. To provide a complete and reproducible template without exposing sensitive student data, we present a synthetic demonstration study that mirrors typical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Predictive Analytics for Student Well-Being and Occupational Success
Abstract: The integration of predictive analytics into higher education has significantly transformed institutional decision-making processes. However, prevailing implementations remain predominantly performance-centered, focusing on dropout prediction and grade forecasting rather than holistic developmental outcomes. Concurrently, higher education systems worldwide are confronting escalating concerns regarding student mental health, disengagement, career uncertainty, and labor market volatility. These intersecting challenges necessitate a broader theoretical reconceptualization of predictive analytics—one that integrates psychological well-being and long-term occupational …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 155–163 Read article
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Visual Analytics with Machine Learning for Data-Driven Investigations of Product State Transmission in Production System
Abstract: In recent years, the significance of effectiveness and productivity has grown. Furthermore, the industry has been able to improve production system performance thanks to advances in processing power and advanced analytics. A better understanding of intermediate product stages is therefore required to take the relevant measures. To highlight the importance of this field of study, a study on data-driven probabilistic ML algorithms and their real-time applications to smart energy systems …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 2, 2022 · pp. 1–7 Read article
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Massive Data Capturing and Transmission using Big Data Analytics Model with Machine Learning Prediction
Abstract: Deep learning techniques are widely used in many branches of research and engineering, including language processing, image classification, and speech recognition. Similar to this, processing vast amounts of data is restricted by a number of classical data processing approaches. To deal with information progressively with incredible exactness and effectiveness, huge information investigation requests spic and span, complex calculations in light of machine and profound learning methods. Recent research has, however, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 9, Issue 3, 2022 · pp. 23–30 Read article
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Evaluating an ai-supported experiential learning intervention: a quasi-experimental study of the joyful saturday model for student engagement and holistic development
Abstract: Student disengagement, declining academic motivation, and passive classroom participation remain major challenges in modern higher education systems. Traditional lecture-based teaching methods often fail to accommodate diverse learning styles and do not sufficiently promote active participation or collaborative learning. To address these challenges, the present study evaluates the effectiveness of Joyful Saturday, a structured experiential learning initiative designed to improve student engagement, motivation, and holistic development through interactive academic activities supported …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 1, 2026 · pp. 79–88 Read article
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Deep Learning-Enhanced Polymer-Based Wearable Biosensors for Continuous Health Tracking via IoT
Abstract: The rapid proliferation of wearable biosensor technologies has transformed approaches to real-time health monitoring, yet challenges persist in achieving both mechanical robustness and reliable, continuous data analytics in dynamic environments. Conventional polymer-based sensing systems often fall short due to limited signal fidelity, inadequate adaptive analytics, or insufficient integration with secure, low-latency IoT frameworks. Addressing these deficiencies, this work introduces a flexible, deep learning-enhanced wearable biosensor platform that combines a nanostructured …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 18–31 Read article
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Intelligent Big Data Processing: Optimizing Analytics Pipelines Through Ensemble Machine Learning, Distributed Computing, and Adaptive AI Architectures
Abstract: Traditional data analytics methods are facing significant challenges due to the explosive rise of digital data. Global data generation is expected to reach nearly 175 zettabytes by 2025. This puts significant pressure on existing computing infrastructure. Conventional analytics methods often struggle with scalability, processing speed, and smart decision-making. Rule-based systems and standalone machine learning models frequently cannot handle the complexity and scale of modern big data environments effectively. To tackle …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 1–11 Read article
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An Analytical Study on Learning Difficulties in Estimation Theory Among Undergraduate Engineering Students
Abstract: Estimation Theory is a critical mathematical foundation for all engineering disciplines, enabling learners to model uncertainty, analyse signals, and derive optimal estimators. However, undergraduate students frequently struggle with its abstract properties, complex derivations, and prerequisite statistical concepts. This study investigates the key learning difficulties faced by students across multiple engineering branches using diagnostic tests, structured questionnaires, and interviews. The findings reveal that students commonly experience challenges related to weak probability …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 37–41 Read article
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Federated Intelligence for Privacy-Preserving Health Data Analytics
Abstract: Healthcare artificial intelligence is limited by a structural conflict: useful models benefit from diverse multi-institutional data, while clinical records cannot be freely pooled because of privacy, ownership and governance constraints. This study presents a practical federated-learning framework for privacy-preserving health data analytics and validates its basic learning behavior in a reproducible cross-silo simulation. Five non-identical institutional clients were created from the public Breast Cancer Wisconsin (Diagnostic) dataset, and a standardized …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 3, 2026 Read article
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Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
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Optimizing Airline Efficiency Using Big Data and Predictive Analytics
Abstract: Recent technological advancements have resulted in the generation of vast volumes of data across industries, including the airline sector, supporting operational control and service quality. Big data analytics (BDA) enables organizations to analyze large and complex datasets to derive actionable insights that support highly informed decision-making and truly superior operational performance. This review paper systematically analyzes twenty relevant research studies to explore the application of BDA within the airline industry. …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 1, 2026 · pp. 13–18 Read article
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An Enhanced Application Oriented Predictive Analytic using Optimal Deep Learning Model
Abstract: In order to presents better Predictiveanalytic models of different data using various machine learning algorithms. The data prediction is developed on various applications. These applications are facing many challenges while collecting data and data selection etc. The proposed prediction model involves several phases like (a) Data acquisition, (b) Data cleaning, (c) Data normalization, (d) Optimal Feature selection, and (e) Prediction. The optimal feature selection will be accomplished by a new …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 7, Issue 2, 2020 · pp. 5–10 Read article
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Developing a Comprehensive Framework for User and Entity Behavior Analytics (UEBA): Integrating Advanced Machine Learning and Contextual Insights
Abstract: User and Entity Behavior Analytics (UEBA) has emerged as a crucial approach in modern cybersecurity for detecting and mitigating insider threats, compromised accounts, and other malicious activities within organizational networks. However, existing UEBA frameworks often face challenges in scalability, detection accuracy, and response effectiveness. This research work proposes a novel framework for UEBA that aims to address these limitations and enhance threat detection and response capabilities. The framework integrates advanced …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 20–32 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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An Analytical Review of Machine Learning Methodologies
Abstract: Machine Learning (ML) is a dynamic and rapidly developing area of computer science that enables the system to learn from data and improve its performance without clear programs. Rooted in statistical theory and computer algorithms, ML has become a major technology that progresses in artificial intelligence. It strengthens the detection of the recommendations and speech for extensive applications from autonomous vehicles and medical diagnoses. This paper has reviewed the basics …
Published in Recent Trends in Mathematics · Vol. 3, Issue 1, 2026 · pp. 13–21 Read article
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Predicting Cardiovascular Disease Risk Using Machine Learning: A Comprehensive Review
Abstract: Cardiovascular diseases (CVDs) are the prevalent morbid and fatal conditions worldwide. Repetition is key to stopping these diseases before they start and before they appear. With the advancement of data analytics and AI, machine learning (ML) has emerged as a powerful tool for predicting CVD risk and delivering personalized healthcare solutions. This review comprehensively summarizes the latest trends in the use of machine learning techniques to predict cardiovascular disease risk. …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article