Keyword Extraction and Pattern Model Identification on Online Learning Contents for Classification to Enhance Microlearning Concepts and Obtain Personalized eLearning Contents
Keyword Extraction and Pattern Model Identification on Online Learning Contents for Classification to Enhance Microlearning Concepts and Obtain Personalized eLearning Contents |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-3 |
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| Year of Publication : 2024 | ||
| Author : T. B. Lalitha, P. S. Sreeja |
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| DOI : 10.14445/22315381/IJETT-V72I3P121 | ||
How to Cite?
T. B. Lalitha, P. S. Sreeja, "Keyword Extraction and Pattern Model Identification on Online Learning Contents for Classification to Enhance Microlearning Concepts and Obtain Personalized eLearning Contents," International Journal of Engineering Trends and Technology, vol. 72, no. 3, pp. 230-248, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I3P121
Abstract
The realm of keyword extraction and pattern model identification within the context of online learning materials, specifically focusing on its application to enhance the microlearning concept, delves into challenges in developing intricate recommendation systems. The rapid evolution of digital education platforms has underscored the need for effective content classification techniques to optimize the microlearning experience. Drawing upon an extensive corpus of online learning materials, this research employs advanced computational methods to extract pertinent keywords that encapsulate the essence of the content. By leveraging natural language processing and machine learning techniques, the study aims to unveil the intrinsic keywords that play a pivotal role in elucidating the core themes and concepts embedded within the learning materials. Furthermore, the research delves into identifying pattern models that underlie the structure and organization of the online learning content. These pattern models are systematically categorized and characterized through meticulous analysis and serve as a foundation for the subsequent classification process. The classification process itself constitutes a key facet of the study, as it involves the systematic categorization of online learning materials based on the extracted keywords and identified pattern models. The utilization of K-means, DBSCAN, and Agglomerative algorithms enables the discernment of meaningful clusters, patterns, and relationships within the corpus of online learning contents. This classification process augments the microlearning concept by providing learners with tailored and concise modules that align with their specific learning objectives. By enhancing the granularity and precision of content delivery, learners are empowered to engage more effectively with the material, thereby fostering a more impactful and efficient learning experience. This paper contributes to the scholarly discourse by presenting a comprehensive framework for keyword extraction, pattern model identification, and subsequent classification of online learning materials. The proposed approach not only enhances the microlearning paradigm but also offers insights into the broader landscape of digital education content recommendations. As the realm of online learning continues to evolve, the findings from this study hold significant implications for educators, instructional designers, and researchers alike, providing a robust foundation for the advancement of tailored and effective pedagogical strategies.
Keywords
Keyword extraction, Pattern model, eLearning, Clustering, K-means, DBSCAN, Agglomerative algorithm, classification.
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