Urban adolescent students and technology: access, use and interest in learning language and literacy
Bibliographic record
Abstract
Adolescents today have vastly different opportunities to learn and process information via pervasive digital technologies and social media. However, there is scant literature on the impact of these technologies on urban adolescents with lower socioeconomic status. This study of 531 urban students in grades 6–8 used a self-reported survey to collect information about (1) students' access to and frequency of using desktop, laptop and tablet computers, and mobile phones, (2) their ownership of mp3 players, iPods, touch pads, cellphones, and smartphones, (3) whether they had accounts with any of 10 communication and social media platforms, and (4) their interest in using Facebook, Twitter, YouTube, and text messaging for language and literacy learning purposes. Students reported significantly more access to these technologies at home than school. Grade 8 students had the most access to cellphones and laptop computers, and were most likely to own smartphones. English language learners indicated a significantly higher interest in using social media for language and literacy learning than their native English-speaking peers. The results indicate a great potential to integrate technology strategically with language instruction for urban adolescent students with linguistically diverse backgrounds. The educational implications of these findings are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".