Interactivity, Collaborative Learning, And Engagement on Social Media: Effects on Students' Academic Performance
Social media platforms are increasingly being used for communication between various internet usergenerations. In education, online learning is becoming an effective tool to increase students’ engagement inclass. Students have the advantage of the emerging numerous social media platforms to access educationinformation and for socializing, communicating, and engaging with peers in educational institutions.Academic institutions and educators grasp the opportunity by embracing platforms like YouTube, TikTok,and Instagram to engage students better and deliver instructional content. The shift in how students learnand interact has brought forth key questions about the place of social media in collaborative learning and asa potentially valuable teaching tool. Educators must adapt their teaching methodologies in accordance withthese platforms to enable the students to succeed academically and technologically. The study carried out inthe Tourism and Hospitality Department, Politeknik Sultan Idris Shah Malaysia, to find the associationbetween social media usage for collaborative learning and student academic performance. Hence, thisresearch aims to examine the relationship between peer and lecturer interactivity, collaborative learning,engagement, and academic performance in social media learning in the higher education context. 218students from within the department participated in the survey. The findings, assessed using Partial leastsquares structural equation modeling (PLS-SEM), reveal that peer and lecturer interactions positivelyinfluence active collaborative learning, and collaborative learning is a significant predictor of studentengagement, ultimately enhancing their academic performance. This research underlines higher education'sneed to nurture social media to foster collaborative learning environments and improve educationalperformance.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-05-09
- DOI
- https://doi.org/10.5281/zenodo.15376145
- Primary Topic
- Computational Physics and Python Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00