The Impact of Social Media Participation on Academic Performance in Undergraduate and Postgraduate Students
Bibliographic record
Abstract
The main objective of this study was to analyse the influence of social media participation on academic performance. The sample consisted of 1960 students taking one of two courses at undergraduate or postgraduate level, respectively (Faculty of Education, National Distance Education University, Spain), of whom 411 students carried out an activity based on social media participation. We used a mixed quantitative (descriptive analysis and ANOVA) and qualitative (content analysis) design. Our results showed that the students who participated in a social media-based activity presented better academic performance than those who did not carry out any activity or who took part in a more traditional learning activity. We conclude that regardless of educational level, social media participation exerts a positive influence on performance. Consequently, it is important to consider the variable of social networking site use because this can partially explain academic performance. We also found that the networks generated during the course did not constitute stable communities of practice. Our main recommendation is that three stages of instruction should be considered when designing a course based on social media participation: beginners, intermediate, and professional.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".