Social Media Use and Test Anxiety: Exploring the Relationship
Bibliographic record
Abstract
The study focused on the relationship between social media usage and test anxiety. It investigated how specific uses of social media are associated with test anxiety. The study classified the uses of social media into four dimensions and explored the relationship between each of the four dimensions and test anxiety. The study also explored the test anxiety levels of students before, during, and after the test. A descriptive survey design was used for the study. This survey used a Test Anxiety and Social Networking Questionnaire to collect data from 106 College of Education students sampled using a multi-stage sampling procedure. The data collected from the respondents were analyzed using scatter plots, Pearson’s Product Moment Correlation Co-efficient, mean scores, and independent samples T-test. It was found that the respondents’ test anxiety was generally high and it progressively increased from before to after the test. There was a strong positive correlation between test anxiety during and after the test. This finding suggests that if the students are well prepared for a test and can answer questions to their satisfaction, their anxiety during the test will be low and this will, in turn, reduce their anxiety after the test. The study also found that excessive use of social media networking sites essentially increases test anxiety during and after the test. This implies that students can reduce test anxiety by using social media networking in moderation and focusing on their academic work.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".