Let's tweet again? The impact of social networks on literature achievement in high school students : Evidence from a randomized controlled trial
Bibliographic record
Abstract
The availability of cheap wi-fi internet connections has stimulated schools to adopt Web 2.0 platforms for teaching. Using social networks and micro-blogs, teachers aim to stimulate students' participation in school activities and their achievement. Although anecdotal evidence shows a high level of teacher satisfaction with these platforms, only a small number of studies has produced rigorous estimates of their effects on students' achievement. We contribute to the knowledge in this field by analyzing the impact of using micro-blogs as a teaching tool on the reading and comprehension skills of students. Thanks to a large-scale randomized controlled trial, we find that using Twitter to teach literature has an overall negative effect on students' average achievement, reducing performance on a standardized test score by about 25 to 40% of a standard deviation. The negative effect is heterogeneous with respect to some students' characteristics. More specifically, the use of this Web 2.0 application appears to have a stronger detrimental effect on students who usually perform better.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".