The impact of social media on educational decision making: The mediating role of information credibility, empirical analysis of Jordanian private universities
Bibliographic record
Abstract
The study aims to bridge this gap by examining how the credibility of information on SM influences educational decisions in higher education (HE) among students. The research questions addressed are: (1) how does SM information credibility influence education decision-making? (2) How often does informative information on SM have a kind with corresponding understanding from traditional sources to guide educational decisions? What difficulties are there in how students judge SM regarding the credibility of information? While social media (SM) has been argued to be an important stakeholder in our lives, its role in educational decision-making regarding information credibility is not explicated. This study, combining surveys and interviews in a mixed-methods approach, showed that providing authentic SM information appreciably increases students' ability to make an informed choice when choosing relevant courses, gain insight into possible learning aids, and assist them with their career plans. The findings highlight the importance of digital literacy for SM-content credibility evaluability in students. The study also outlined critical strategies for educators and institutions to support students in assessing the reliability of sources and train them in skills that accompany a responsible thumb online. The conclusion takeaway from the study is that credible information on SNS can impact and change educational decision-making.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".