Teaching and Learning in Two iPad-Infused Classrooms: A Descriptive Case Study of a Dual Classroom, School-Based Pilot Project
Bibliographic record
Abstract
This multi-methods, descriptive case study examines attitudes and practices of classroom-based iPad use. The site is one inner-city, urban, publicly funded school, focused on two iPad-infused classrooms (Grade 2/3 and Grade 4/5). Data were collected from 5 educators and 35 students to investigate two research questions: How are iPads being utilized in student instruction? How do educators and students perceive the value of using iPads in the classroom? For this study, we analyzed the transcript of a focus group with five educators, data from 10 days of structured student observations, and the results from 35 student questionnaires. Five themes emerged from the focus group; the strongest related to pedagogical practices. Data related to student perceptions indicated a positive attitude toward iPads. They enjoyed iPad use, were concerned about equity issues, had high self-ratings about related skills, felt they used it most often in Mathematics, and indicated various preferred applications. Overall, iPads were used in 31.7% of observed instructional time, 94.7% of which was facilitated by classroom teachers. Of this iPad- based instructional time, 72.5% was for individualized teaching, typically in language and/or mathematics instruction. Our analysis culminates in recommendations for school leadership such as teaching prerequisite skills and providing ongoing technological supports.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".