Teen Culture, Technology and Literacy Instruction: Urban Adolescent Students’ Perspectives
Bibliographic record
Abstract
Modern teens have pervasively integrated new technologies into their lives, and technology has become an important component of teen popular culture. Educators have pointed out the promise of exploiting technology to enhance students’ language and literacy skills and general academic success. However, there is no consensus on the effect of technology on teens, and scant literature is available that incorporates the perspective of urban and linguistically diverse students on the feasibility of applying new technologies in teaching and learning literacy in intact classrooms. This paper reports urban adolescents’ perspectives on the use of technology within teen culture, for learning in general and for literacy instruction in particular. Focus group interviews were conducted among linguistically diverse urban students in grades 6, 7 and 8 in a lower income neighborhood in the Northeastern region of the United States. The major findings of the study were that 1) urban teens primarily and almost exclusively used social media and technology devices for peer socializing, 2) they were interested in using technology to improve their literacy skills, but did not appear to voluntarily or independently integrate technology into learning, and 3) 8th graders were considerably more sophisticated in their use of technology and their suggestions for application of technology to literacy learning than 6th and 7th graders. These findings lead to suggestions for developing effective literacy instruction using new technologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".