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 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".