Primary school pupils’ ability to detect fake science news following a news media literacy intervention: Exploration of their success rate, evaluation strategies, self-efficacy beliefs, and views of science news
Bibliographic record
Abstract
Widespread belief in scientific misinformation circulating online is a critical challenge for democracies. While research to date has focused on psychological, sociodemographic, and political antecedents to this phenomenon, fewer studies have explored the role of media literacy educational efforts, especially with children. Recent findings indicate that children are unprepared for critically evaluating scientific information online and that literacy instruction should address this gap. The aim of this study is to examine the ability to detect fake science news and the evaluation strategies employed by pupils after a news media literacy intervention. In addition, we explore the impact of the news media literacy intervention on their self-efficacy beliefs for detecting fake science news, and on their views of science news. A one-group experimental design was employed with a sample of 74 primary school pupils. A few weeks following a 2-hour media literacy intervention, pupils ranked ten Twitter posts on various scientific topics and were invited to justify their ranking in an open-ended question to unveil their evaluation strategies. Participants also completed one pre-test and one post-test designed to elicit their confidence in their ability to detect fake science news and their views of science news. We averaged pupils’ judgement accuracy, categorized student’s evaluation strategies, and compared self-efficacy beliefs before and after the intervention. On average, pupils’ accuracy when asked to detect fake science news was 68%. This performance is higher than success rates reported in previous studies where no news media literacy intervention was tested. Pupils relied mostly on knowledge in news media literacy to detect fake science news, but also in great proportion on prior scientific knowledge and intuitive reasoning. Fake news self-efficacy beliefs increased significantly after the intervention, but views of science news were not impacted by the intervention. Findings indicate that primary school pupils are capable of careful examination of the credibility of scientific news. Children are regularly exposed to misinformation, and knowledge on how to critically engage with scientific information should be taught as soon as this exposure begins. Our findings suggest that news media literacy training can be successfully facilitated with primary school pupils and could be effective in fighting scientific misinformation from a young age.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".