Bibliographic record
Abstract
The purpose of this research is to examine the effect of social media on students' academic performance. The case study is based on the academic performance of students majoring in business administration at a higher education institution in Phnom Penh, Cambodia. A conceptual framework was developed from previous research which includes social media information, social media innovation, social media entertainment, social media knowledge generation, and student performance. Quantitative methods were used to distribute questionnaires to 376 respondents. A multistage sampling technique was conducted by nonprobability sampling, using judgmental sampling to select university students, quota sampling to calculate the sample size, and convenience sampling to distribute the questionnaire online, using several popular social networks. Before collecting data, the Index of Item-Objective Congruence (IOC) was used to validate the constructs. Factor loadings and Cronbach's alpha were tested for reliability, using 50 respondents as a pilot study, single and multiple linear regressions were applied to test hypotheses, and correlation matrices were used to identify variable relationships. The results indicated that social media information, social media innovation, and social media entertainment have a strong effect on social media knowledge creation. In addition, social media knowledge creation has a strong impact on student performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".