Information Technology of Learning Personalization based on the Advanced Big Data Analytics Methods

Скорочена інформація про документ
dc.contributor.authorHan Zaihuien
dc.date.accessioned2025-03-25T10:55:10Z
dc.date.available2025-03-25T10:55:10Z
dc.date.issued2024
dc.description.abstractThe thesis explores the transformative potential of information technology of learning personalization based on the advanced Big Data analytics methods. It underscores the significance of leveraging advanced BDA techniques to enhance educational outcomes by tailoring learning experiences to individual needs. The study evaluates various BDA tools and methodologies, assessing their efficacy in processing and analyzing vast educational datasets. The research employs a mixed-method approach, integrating quantitative data analysis with qualitative case studies. The findings suggest that BDA can significantly improve personalized learning, leading to better academic performance and student satisfaction. The thesis concludes with recommendations for educational institutions to integrate BDA into their learning management systems to facilitate personalized learning pathways. In the wake of the digital transformation catalyzed by the COVID-19 pandemic, educational institutions have witnessed an unprecedented surge in the adoption of online learning platforms. This shift has resulted in an explosion of educational data, providing a rich repository of information that can be harnessed to enhance the learning experience. Big Data Analytics (BDA) plays a pivotal role in this context, offering insights into student behaviors, preferences, and performance patterns that can inform the design of personalized learning pathways. The purpose of this thesis is to investigate the role of advanced BDA in revolutionizing personalized learning and its impact on educational outcomes post-2020. The study aims to understand how BDA can be utilized to process and analyze educational data to create tailored learning experiences that address the unique needs of individual students. The research focuses on the evaluation of various BDA tools and methodologies, including Machine Learning (ML), Artificial Intelligence (AI), and Educational Data Mining (EDM), to assess their effectiveness in enhancing personalized learning. A mixed-method research approach is adopted, combining quantitative data analysis with qualitative case studies to provide a comprehensive understanding of 3 the impact of BDA on personalized learning. The quantitative analysis involves the examination of large educational datasets from various learning management systems (LMS), while the qualitative component includes detailed case studies of educational institutions that have successfully integrated BDA into their teaching and learning strategies. The case studies reveal that the integration of BDA in educational practices has led to significant improvements in student academic performance and satisfaction. BDA enables educators to identify at-risk students early, personalize content delivery, and adapt teaching methods to better meet the needs of diverse learners. Additionally, BDA facilitates the creation of dynamic learning environments that can adapt in real-time to student interactions and feedback, thus enhancing the overall learning experience. The thesis also discusses the challenges associated with the implementation of BDA in education. These include issues related to data privacy, the need for robust data infrastructure, and the requirement for technical expertise to manage and analyze the data effectively. Despite these challenges, the opportunities presented by BDA are substantial, with the potential to revolutionize education by making it more accessible, inclusive, and effective.en
dc.identifier.citationHan Zaihui Information Technology of Learning Personalization based on the Advanced Big Data Analytics Methods: кваліфікаційна робота магістра / Han Zaihui. – Одеса, 2024. – 52 с.en
dc.identifier.urihttps://dspace.onu.edu.ua/handle/123456789/40874
dc.language.isoen
dc.publisherОдеський національний університет імені І. І. Мечниковаuk
dc.subject126 інформаційні системи та технологіїuk
dc.subjectмагістрuk
dc.subjectInformation Technologyen
dc.subjectLearning Personalizationen
dc.subjectAdvanced Big Data Analytics Methodsen
dc.titleInformation Technology of Learning Personalization based on the Advanced Big Data Analytics Methodsen
dc.typeDiplomasen
Файли
Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
Han Zaihui Thesis.pdf
Розмір:
631.6 KB
Формат:
Adobe Portable Document Format
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Ескіз недоступний
Назва:
license.txt
Розмір:
1.71 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити
Зібрання
Магістри МФІТ