The Role of Social Media in Enhancing Collaborative Learning in Online Education
Bibliographic record
Abstract
This research explores the impact of social media as a tool for enhancing online education, focusing on its role in fostering collaborative learning. With the rise of online education, especially during the COVID-19 pandemic, social media platforms like Facebook and WhatsApp have transitioned from mere networking sites to integral components of digital learning environments. This research highlights the advantages of social media in educational settings, such as increased student engagement, the facilitation of peer-to-peer interaction, and improved access to diverse resources. The study also addresses theoretical frameworks, including Vygotsky's social constructivism, which underscores the role of social interaction in knowledge acquisition, and the Technology Acceptance Model, which examines factors influencing the use of social media in education. Despite the benefits, the study acknowledges challenges, including privacy concerns, information overload, and potential distractions. These limitations require careful consideration and strategic management to maximize the educational potential of social media. The findings suggest that, when properly utilized, social media can enhance online learning by promoting inclusivity, motivation, and academic performance. The paper concludes with recommendations for integrating social media into educational practice, proposing guidelines for balancing its educational advantages with privacy and focus concerns. Future research should continue to explore best practices for using social media to support effective digital learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".