Enhancing Online Learning Experiences through Personalization Utilizing Recommendation Algorithms

Main Article Content

Caroline
Oliviane Oroh
Damir Pada

Abstract

This research investigates the implementation and impact of personalized learning systems underpinned by advanced recommendation algorithms in the realm of online education. The study encompasses a diverse group of participants from various educational backgrounds and explores their interactions with the personalized learning platform. The key findings of this research are noteworthy. Participants who had access to the personalized learning environment exhibited a substantial increase in engagement, satisfaction, and learning outcomes compared to those in the control group. This signifies the transformative potential of personalized learning in online education. The research emphasizes the critical role of personalization in enhancing learner engagement and satisfaction. It highlights how learners actively engaged with the system, making use of personalized recommendations to tailor their learning experiences. Moreover, the study sheds light on the positive impact of personalization on learning outcomes, indicating that learners achieved higher academic performance when their learning experiences were customized to their needs and preferences. In addition to its benefits for learners, the research underscores the advantages of personalized learning for instructors. The system provided instructors with valuable insights into each learner's progress and challenges, enabling more targeted and effective support. While the study demonstrates the effectiveness of personalized learning, it acknowledges certain limitations, including a relatively limited sample size and short duration. Future research endeavors could involve larger and more diverse samples and extend the study duration to gain a more comprehensive understanding of the long-term effects of personalized learning. In conclusion, this research contributes to the growing body of literature on personalized learning in online education. It provides compelling evidence that personalized learning, facilitated by sophisticated recommendation algorithms, can significantly enhance the online learning experience. The findings offer insights for educators and institutions looking to integrate personalized learning features into their online platforms to improve learner engagement, satisfaction, and learning outcomes.

Article Details

How to Cite
Caroline, Oroh, O., & Pada, D. (2023). Enhancing Online Learning Experiences through Personalization Utilizing Recommendation Algorithms. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 398–407. https://doi.org/10.35870/ijsecs.v3i3.1852
Section
Articles
Author Biographies

Caroline, Universitas Sultan Fatah

Development Economics Study Program, Faculty of Economics and Social Sciences, Universitas Sultan Fatah, Demak Regency, Central Java Province, Indonesia

Oliviane Oroh, Universitas Teknologi Sulawesi Utara

Management Study Program, Faculty of Economics, Universitas Teknologi Sulawesi Utara, Manado City, North Sulawesi Province, Indonesia

Damir Pada, Institut Cokroaminoto Pinrang

Pancasila and Citizenship Education Study Program, Institut Cokroaminoto Pinrang, Pinrang Regency, South Sulawesi Province, Indonesia

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